Proceedings

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9th International Conference on Precision Agriculture
15th International Conference on Precision Agriculture
16th International Conference on Precision Agriculture
13th International Conference on Precision Agriculture
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Authors
Aasen, H
Abban-Baidoo, E
Abbas, A
Abbas, F
Abbasi, E
Abdala, M
Abdalla, A
Abdelaty, E.F
Abderaouf, E.A
Abdinoor, J.A
Abdul Rahman, K
Abenina, M
Abney, M
Abon, J.O
Aboutalebi, M
Abukmeil, R
Acconcia Dias, M
Acharya, I
Achigan-Dako, E
Acosta, M
Acquah, H.D
Acuna, T
Adamchuk, V
Adamchuk, V.I
Adedeji, O
Adedeji, O.I
Adhikari, K
Adil, M
Admasu, W.A
Adolwa, I
Adu-Gyamfi, Y
Aduramigba-Modupe, V
Agampodi, G.S
Agarwal, D
Aggarwal, V
Agneroh, T
Aguilera, A.P
Ahmad, A
Ahrends, H.E
Ahuja, L.R
Aijun, Z
Aikes Junior, J
Akin, S
Akorede, B.A
Akune, V.S
Al Amin, A
Al-Gaadi, K
Al-Shammari, D
Alabi, T
Alahe, M
Alarcon, V.J
Albrecht, H
Alchanati, V
Alchanatis, V
Alchnatis, V
Alderman, P
Alderman, P.D
Aldridge, K
Alene, A
Alexandroff, V
Ali, A
Ali, U
Aliloo, J
Alizadeh, H.M
Allam, D.G
Allegro, G
Allen, M
Almallahi, A
Almeida, S.L
Alshihabi, O
Alves de Lima, J.
Alves, M.R
Alwaseela, H
Amado, T.J
Amaral, L.R
Ameglio, L
Amely, N
Amin, S
Amouzou, K.A
Ampatzidis, Y
Amri, M
Anaba, C.I
Andales, A
Andales, A.A
Andersen, P
Anderson Guerrero, S
Anderson, S.H
Anderson, W
Anderson-Guerrero, S
Andersson, K
Andvaag, E
Angar, H
Angrino Chiran, D.F
Anken, T
Anselmi, A.A
Antunes de Almeida, L.F
Antunes, J.F
Anup, A
Apolinário, E
Applegate, D.B
Araujo, R
Archer, J.K
Archila-Diaz, J.F
Archontoulis, S
Ardigueri, M
Arias, A
Arias, A.C
Armstrong, P
Armstrong, P.R
Armstrong, S
Arnall, B
Arnall, D.B
Arnold, S
Arun, A
Aryal, B
Asci, S
Asgedom, H
Ashrafi, Z.Y
Ashworth, A
Asido, S
Astillo, P
Attanayake, A
Attanayake, A.U
Avemegah, E
Avila, E.N
Ayipio, E
Ayık, M
Azzam, T
B K, A
BAdua, S
BHATTARAI, A
BISCAMPS, J
Badua, S
Baeck, P
Bagavathiannan, M
Baghernejad, M
Bai, F
Bai, G
Bailey, J
Bajwa, S
Baklouti, I
Bakshi, A
Balabantaray, A
Balafoutis, A
Balasundram, S.K
Balbinot, A
Balboa, G
Balint-Kurti, P
Balkcom, K
Balla, I
Balmos, A
Balzarini, M
Bansal, G
Bantchina, B
Bao, H
Bao, Y
Barai, K
Barbosa, M
Bareth, G
Bari, M.A
Barker, D
Barnes, E.M
Baroni, G
Barron, J
Barwick, J.D
Basir, M
Basir, M.S
Basran, P.S
Bastos, L
Bathke, K.J
Batuman, O
Bauer, P.J
Baumbauer, C
Bautista, F
Bazakos, M
Bazzi, C
Bazzi, C.L
Bean, G
Bean, G.M
Bean, M
Beasley, D
Bech, A
Becker, M
Bede, L
Bedwell, E
Been, T
Beeri, O
Behera, S
Behrendt, K
Beitz, T
Bekkerman, A
Belasque Junior, J
Belford, R
Bello, N
Belmont, K
Ben-Gal, A
Beneduzzi, H.M
Benez, S.H
Benjamin, M
Benke, S
Bennett, B
Bennett, J
Bennett, S
Bennur, P
Beppu, Y
Berger, A
Berger, A.W
Berger-Wolf, T
Berghaus, A
Bergheim, R
Bernal Riobo, J.H
Berne, D.T
Berretta, B.G
Berti, M
Berzins, R
Best, S
Betzek, N
Betzek, N.M
Bhandari, M
Bhandari, S
Bhattarai, A
Bhattarai, B
Biaou, A
Bier, J
Bierman, D
Bindish, R
Bishop, T
Bisognin, M.B
Biswas, A
Blommaert, J
Boatswain Jacques, A.A
Bobryk, C.W
Bodson, B
Boejer, O
Boersma, S
Boettinger, J.L
Bolfe, E
Bolten, A
Bolton, C
Bonfil, D.J
Bongiovanni, M
Bongiovanni, R
Bonke, V
Bonnardel, B
Boonen, M
Boote, K
Borbás, Z
Bortolon, G
Borůvka, L
Bosompem, M
Botsali, F.M
Bourlai, T
Bourouah, M
Bouroubi, M.Y
Boydston, R
Boyer, C.N
Boyer, W
Bradacova, K
Bramley, R
Brand, H
Brasco, T
Brasco, T.L
Brase, T.A
Brazda, D
Bridges, R.W
Brinkhoff, J
Brinton, C
Brockgreitens, J
Brokesh, E
Bromfield, C
Brooks, J.P
Brorsen, B.W
Brorsen, W
Brown, A.J
Brown, P
Browne, G.T
Bruce, A.E
Brungardt, J.J
Buckmaster, D
Bui, M
Bui, T
Bullock, D
Bullock, R.J
Burkhart, S
Burks, T
Burlai, T
Burns, D
Burris, E
Busby, S
Busch, G
Buschermohle, M.J
Bussher, W.J
Butts, C
Byers, C
Byrne, D
Büchele, D
Bückmann, H
CAMPOS, J
CARCEDO, A
Caballero-Rodriguez, A.M
Cabrera Dengra, M
Cafaro La Menza, N
Cai, S
Camberato, J
Camberato, J.J
Cambouris, A
Cambouris, A.N
Cammarano, D
Campos, S
Canal Filho, R
Canata, T
Canata, T.F
Canavari, M
Cano, P.B
Cao, Q
Cao, W
Capolicchio, J
Cappelleri, D
Capper, J
Caragea, D
Caras, T
Carcedo, A
Cardoso, G.M
Carlier, A
Carneiro Amado, T.J
Carneiro, F.M
Carriedo, L
Carter, P
Carter, P.R
Casanova, J.L
Casey, F
Castiblanco Rubio, F.A
Castro, S.G
Cavayas, F
Cendrero Mateo, M.P
Cerliani, C
Cerri, D.G
Cesario Pereira Pinto, J
Cesario Pinto, J
Chakraborty, M
Chamara, N
Chang, Y
Chaplin, Y
Charvat Jr., K
Charvat jr., K
Charvat, K
Chau, M
Chavan, H
Cheema, S.J
Chen, C
Chen, J
Chen, L
Chen, P.L
Chen, X
Chen, Y
Chen, Z
Chikowo, R
Cho, J
Cho, W
Cho, Y
Choi, D
Choi, J
Choi, M
Chok, S.E
Choton, J
Choudhury, S.D
Christensen, A
Chudy, T
Chung, S
Ciampitti, I
Cisdeli Magalhães, P
Cisneros, M
Citon, L.C
Clark, J
Clark, N
Claussen, J
Clay, D.E
Clay, S.A
Codjia, C
Cohen, Y
Colaço, A
Colaço, A.F
Colbert, J
Colley, T
Connor, J
Constas, K
Conway, L
Conway, L.S
Coppola, A
Corassa, G
Corassa, G.M
Cordova Gonzalez, C
Correndo, A
Cosby, A.M
Costa Barboza, T.O
Costa Souza, J.B
Costa, O.P
Coulter, J.A
Cox, A.S
Craker, B
Craker, B.E
Craven, S
Crawford, M
Cristancho Rojas, O.Y
Cross, T
Cruse, R
Csenki, S
Culman, S
Cushnahan, M.Z
Cushnahan, T
Custer, S
Cutulle, M
Czarnecki, J
D.C, H
DEBANGSHI, U
DUMONT, B
Da Silva, E.R
Da Silva, J
Da Silva, M.L
Dafnaki, D
Daggett, D.G
Daggupati, P
Dalal, A
Dalla Betta, M.M
Dalla Nora, D
Dandrifosse, S
Das, A
Das, A.K
Dash, M
Davadant, P
Davis, G
De Neve, S
De Oliveira Moreira, F
De Poorter, E
De Waele, T
DeBruin, J
DeFauw, S.L
Dean, C
Dean, R
Degioanni, A
Delalieux, S
Delauré, B
Deleon, E
Delgadillo, C.A
Demattê, J.M
Denton, A.M
Derdall, E
Deri Setiyono, T
Derrick, J
Destain, M
Dewdney, M
Dey, S
Dhal, S
Dhawale, N
Dhillon, R
Dhiman, V
Diallo, A.B
Diatta, A
Diaz, D
Dickin, E
Dill, T
Dillen, J
Dilmurat, K
Djighaly, P
Dobos, R
Dokoozlian, N
Dong, R
Dorissant, L
Dornbusch, T
Dos Reis, A.A
Dos Santos, R.S
Dossou-Yovo, E.R
Downing, B
Dr., N
Dr., S
Drew, P
Drewry, D
Dreyer, J.G
Drummond, S
Drzazga, T
DuPont, E.M
Dua, A
Dua, S
Duarte de Val, M
Duarte, P.R
Duary, B
Duchemin, M
Duddu, H
Duddu, H.U
Duff, H
Duff, H.D
Dufrasne, I
Duft, D.G
Dumont, B
Dunbabin, M
Duncan, S
Dunn, D
Duron, D
Dutilleul, P
Dutta, W
Dworak, V
E. Flores, A
EMİNOĞLU, B.M
Eberle, D
Eberz-Eder, D
Edge, B
Ehsani, R
Eigenberg, R.A
Eitelwein, M.T
El Gamal, A
El-Mejjaouy, Y
Eldeeb, E
Eldefrawy, M
Ellixson, A
Ellsworth, J.W
Elmore, R
Elsen, A
Elvir Flores, A
Emadi, M
Emamalizadeh, S
Emmi, L
Emmons, A
Enger, B.D
Engle, J
English, P.J
Ennadifi, E
Erazo, E
Erbe, A
Erickson, B
Erickson, B.J
Esau, T
Esau, T.J
Escolà, A
Eshel, G
Esposito, G
Estrada, A
Evans, D.E
Evans, J
Everett, M
Evers, B
Ewanik, C
Eyster, R
Fageria, N.K
Fallon, E
Farooque, A
Farooque, A.A
Fassinou Hotegni, N
Fasso, W
Fathololoumi, S
Fausti, S
Felderhoff, T
Felipe dos Santos, A
Fenech, A
Feng, G
Ferguson, A
Ferguson, R.B
Fernandez, F.G
Fernandez, O
Fernando, H
Fernández, F
Fernández, F.G
Ferraz Pueyo, C
Ferraz, C
Ferraz, M.N
Ferreyra, R
Fey, S
Figueiredo, G.K
Filippetti, I
Filippi, P
Fiorio, P.R
Firozjaei, M.K
Fisher, D.K
Fleming, K
Flint, E.A
Flippo, D
Flores, A
Flores, P
Flores, P.J
Floyd, W
Fodjo Kamdem, M
Folle, S
Fontenelli, J.V
Ford, L
Fornale, M
Fortes, R
Fortunato, M
Foster, J
Foster, P.N
Fountain, J
Fountas, S
Fraile, S
France, W
Francisco, E
Franco, H.C
Franklin, K
Franklin, K.F
Franz, F
Franzen, D.W
Franzen, J
Frederick, Q
Freire de Oliveira, M.F
Freitas, R.G
Friell, J
Frimpong, K
Frimpong, K.A
Friskop, A
Fritz, A
Fritz, B.K
Frizzel, L
Frotscher, K.J
Fu, W
Fu, Z
Fuller, H.D
Fulton, J.P
G.M. Florax, R.J
Gacek, E.S
Gadhwal, M
Gahler, A
Gailums, A
Gal, A
Galeano, S.A
Gallios, I
Galzki, J
Gamble, A
Gan, H
Ganascini, D
Gandorfer, M
Garcia-Ruíz, F
Garcia-Torres, L
Gardezi, M
Garg, A
Garza, C
Gauci, A
Gavioli, A
Gaviraghi, R
Ge, Y
Gebbers, R
Gebert, F.H
Gebler, L
Gelder, B.K
George, D
Gerighausen, H
Gerken, A.R
Gerth, S
Ghanbari Parmehr, E
Ghansah, B
Ghimire, B
Ghimire, B.P
Ghimire, D
Gholizadeh, A
Gidea, M
Gigena, B
Gil, E
Gill, N
Gillingham, V
Gilson, A
Gimenez, L.M
Gimenez, V
Gips, A
Glavin, M
Glewen, K
Gnatowski, T
Gnyp, M.L
Gobezie, T.B
Goel, R
Goeringer, P
Goffart, J
Goldshtein, E
Goldwasser, Y
Golus, J.A
Gomez, F
Gonzalez, J
González Piqueras, J
Goodrich, P
Goodrich, P.J
Goorahoo, D
Gornushkin, I
Gosselin, B
Gowler, A
Gozdowski, D
Gómez-Candón, D
Graff, N
Grafton, M.C
Grant, R.H
Grassini, P
Gray, G.R
Green, O
Green, S
Greene, J
Gregory, S
Grewal, K
Griffin, T
Griffin, T.W
Grijalva Teran, I.A
Grijalva, I
Grisham, M.P
Gritten, F
Grueninger, R
Gu, H
Guan, H
Guerrero, H.B
Guinness, J
Gulandaz, M
Gumero, J
Gummi, S
Gunther, D
Gunzenhauser, B
Gunzenhauser, R
Guo, W
Guo, Y
Guohua, W
Gupta, S
Gutiérrez, V
Gutteridge, M
Gérard, B
Gómez-Candón, D
HIguti, V.A
Ha, T
Haapala, H.E
Hachisuca, A
Hachisuca, A.
Hachisuca, A.M
Hajda, C
Halvorson, M
Hama Rash, S
Hamagami, K
Hamida, A
Hammond, J
Hammond, K
Han, K
Han, M
Han, S
Han, X
Han, Y.J
Hand, L
Haneklaus, S
Hanks, J.E
Hansel, D
Hansen, J
Hansen, N
Hansen, N.C
Hanumanthappa, D
Hanyabui, E
Harari, A
Haringx, S.C
Harkin, S.J
Harper, J
Harris, E.W
Harris, G
Harris, W.E
Harsha Chepally, R
Hartmann, B
Hartschuh, J
Hartschuh, J.M
Hashim, Z.K
Hassan, M
Hatfield, J
Hatfield, J.L
Hawkins, E
Hayhurst, K
Hazzoumi, Z
He, L
He, Z
Hefley, T
Hegedus, P
Hegedus, P.D
Hegedűs, G
Heggemann, T
Heggemann, T.W
Hehar, G
Heil, K
Hejl, R
Helgason, C
Hennessy, P.J
Henrie, A
Henry, B
Hensley, R
Henties, T
Hernandez, C
Herppich, W.B
Herrmann, I
Herzmann, D
Hessel, R
Hettiarachchi, G
Hillyer, C
Hillyer, C.C
Hintz, G.D
Hinze, J
Hirai, Y
Hock, M.W
Hodeghatta, U.R
Hoffmann Silva Karp, F
Hoffmann, W.C
Hofman, V
Hokanson, G.E
Holland, K.H
Holpp, M
Holthaus, D
Hong, C
Hong, S
Hongo, C
Hoogenboom, G
Hopkins, B
Hopkins, B.G
Horakova, S
Horbe, T
Horbe, T.
Horvath, D
Horváth, B
Hostert, P
Hovio, H
Howatt, K
Howatt, T
Hu, J
Hu, Q
Hu, T.H
Huang, S
Huang, Y
Huang, Z
Huender, L
Hueppi, R
Hufnagel, E
Hunhoff, L
Hunt, A
Hunt, E
Hunt, L
Husni, M.H
Hüging, H
Igwe, K.E
Ikpi, A
Inaba, S
Ingram, B
Inoue, E
Inunciaga Leston, G
Inácio, F.D
Irwin, M.E
Isakeit, T
Islam, M
Isono, S
JANBAZIALAMDARI, S
Jackson, C
Jacquemin, G
Jagadish, K
Jakhar, A
Jakimow, B
Jalem, R.S
Jamei, M
James, D
Jang, S
Janjua, U.U
Jansen, M
Jansky, T
Janz, A
Jarolimek, J
Jasper, J
Javed, B
Jenal, A
Jensen, N
Jensen, R
Jeon, C
Jha, G
Jha, S
Jhala, A
Jia, M
Jiang, J
Jianli, S
Jimenez, A
Jiménez Castaño, V
Joalland, S
Johal, G
Johannsen, C.J
Johnson, A
Johnson, D.M
Johnson, E
Johnson, E.U
Johnson, J
Johnson, R.M
Jones, B
Jones, J
Jones, N
Jorgensen, R
Joseph, K
Joshi, D
Joshi, N
Joshi, R
Jukema, J.N
Jurado-Expósito, M
Jørgensen, R.N
K, S
KABIR, M
KC, K
Kaboré, J.P
Kagami Taira, F
Kaiser, D
Kalafatis, S
Kalmar, J
Kaloya, T
Kalra, A
Kamerer, C
Kang, C
Karam, A
Karamidehkordi, E
Karampoiki, M
Karangwa, A
Karkee, M
Karn, R
Karppinen, E
Kashetri, S
Kasimati, A
Katari, S
Katz, L
Kaushal, S
Kazula, M
Kechchour, A
Keil, F
Keller, B
Keller, M
Kelley, A
Kelley, J
Kemerait, R.C
Kemeshi, J.O
Kempenaar, C
Kepka, M
Kereszturi, G
Kerry, R
Kersebaum, C
Ketterings, Q
Khakbazan, M
Khalilian, A
Khan, H
Khanal, S
Kharel, T
Khosla, R
Khot, L
Khuimphukhieo, I
Khun, K
Kichler, J
Kidd, J
Kieffer, D.L
Kiel, A
Killer, A
Kim, D
Kim, H
Kim, J
Kim, M
Kim, S
Kim, Y
Kindred, D
Kiran, A
Kisekka, I
Kitchen, N
Kitchen, N.R
Kittemann, D
Kizer, E
Klapp, I
Klein, R.N
Klopfenstein, A
Knappenberger, T
Knezevic, S
Ko-Madden, C
Koch, G
Kocks, C
Kolar, P.R
Kolln, O.T
Kombali, G
Kopanja, M
Koparan, C
Koppelman, G
Kotlyarov, D
Kotlyarov, V
Kovacs, A
Kovacs, A.J
Kovacs, P
Krienke, B
Krishnaswamy, K
Krmenec, A
Krogmeier, J
Krys, K
Kshetri, S
Kubickova, H
Kudenov, M
Kuehner, K
Kukal, S
Kukorelli, G
Kulesza, S.E
Kulhandjian, H
Kulhandjian, M
Kulmany, I.M
Kumar R, M
Kumari, S
Kumke, M
Kumpatla, S
Kunwar, S
Kwarteng, J.A
Kyveryga, P
Kyveryga, P.M
Käthner, J
Kósa, A
LENOIR, A
Laacouri, A
Laboski, C
Laboski, C.A
Lacasa, J
Lacerda, L
Lacerda, L.N
Lajunen, A
Lamb, D.W
Lamb, J
Lambert, D.M
Lamichhane, R
Lamparelli, R.A
Lan, Y
Landivar, J
Landivar-Scoot, J.L
Lang, V
Langovskis, D
Lanza, P
Laor, Y
Larbi, P.A
Lare, M
Larson, J.A
Lati, R
Lattanzi, P
Lauzon‎, S
Lavagnino, M
Le Roux, M
Lebeau, F
Leduc, M
Lee, B
Lee, J
Lee, K
Lee, S
Lee, W
Leemans, V
Leenen, M
Lehmann, J
Leininger, A
Lemes Bosche, L
Lemke, R
Lemus, S
Lena, B.P
Lenssen, A
Lenz-Wiedemann, V
Leon Rueda, W.A
Lessl, J
Lesueur, C
Leszczyńska, E
Leszczyńska, R
Levi, M
Lexow, T
Li, D
Li, H
Li, J.C
Li, L
Li, M
Li, X
Li, Y
Liakos, V
Liang, X
Lianqing, Z
Liburd, O.E
Licht, M.A
Lichtenberg,, S
Liew, C
Lilienthal, H
Lima, J.P
Lin, Z
Lindblom, J
Lindsey, A
Lindsey, L
Lingua, L.N
Linker, R
Lins, E.C
Litaor, I
Liu, H
Liu, J
Liu, K
Liu, P
Liu, W
Liu, Z
Livens, S
Lizarazo Salcedo, I.A
Loewen, S
Loewen, S.D
Long, D
Long, D.S
Long, J
Longchamps, L
Longlong, L
Lord, E
Lotsi, A.K
Louis, J
Love, D
Love, D.J
Lovejoy, K
Lowenberg-DeBoer, J
Lowenberg‑DeBoer, J
Lowrance, C
López, J.D
López-Granados, F
Lu, J
Lu, Y
Lucero, M.F
Luck, J
Luck, J.D
Ludewig, U
Lukas, V
Luker, E
Lukwesa, D
Lum, C
Lund, E
Lund, T
Lundström, C
Luns Hatum de Almeida, S
Luo, B
Lusher, J
López-Urrea, R
MECHRI, M
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da Silva, T.R
de Boer, W.F
de Carvalho, H.W
de Castro, A
de Oliveira Costa Neto, A
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de knegt, H
del Val, M.D
dos Santos, C.L
eitelwein, M.T
giriyappa, M
han, K
hassanijalilian, O
li, F
liu, X
maddalon, J
neogi, N
song, S
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Topics
Precision Crop Protection
Unmanned Aerial Systems
Spatial Variability in Crop, Soil and Natural Resources
Profitability, Sustainability and Adoption
Remote Sensing Applications in Precision Agriculture
Proximal Sensing in Precision Agriculture
Engineering Technologies and Advances
Food Security and Precision Agriculture
Precision Nutrient Management
Sensor Application in Managing In-season Crop Variability
Decision Support Systems in Precision Agriculture
Big Data Mining & Statistical Issues in Precision Agriculture
Precision Horticulture
Precision Conservation Management
Standards & Data Stewardship
Precision Agriculture and Climate Change
Agricultural Education
Precision Dairy and Livestock Management
No Group Selected
Remote Sensing for Nitrogen Management
Precision Horticulture
Spatial and Temporal Variability in Crop, Soil and Natural Resources
Guidence, Auto steer, and Robotics
Profitability, Adoption and Performance Evaluation
Modelling and Geo-Statistics
Precision Management / Precision Conservation
Remote Sensing Application / Sensor Technology
Vegetative Indices in Crop Production
Education and Training in Precision Agriculture
Traceability
Engineering Technologies
Adoption of Precision Agriculture
Geospatial Data
Drainage Optimization and Variable Rate Irrigation
On Farm Experimentation with Site-Specific Technologies
Decision Support Systems
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Robotics, Guidance and Automation
Precision Crop Protection
In-Season Nitrogen Management
Education and Outreach in Precision Agriculture
Applications of Unmanned Aerial Systems
Wireless Sensor Networks
Factors Driving Adoption
Precision Horticulture
Land Improvement and Conservation Practices
Smart Weather for Precision Agriculture
Site-Specific Nutrient, Lime and Seed Management
Big Data, Data Mining and Deep Learning
ISPA Community: Economics
Farm Animals Health and Welfare Monitoring
Site-Specific Pasture Management
Profitability and Success Stories in Precision Agriculture
ISPA Community: Latin America
Precision Agriculture and Global Food Security
ISPA Community: Nitrogen
Small Holders and Precision Agriculture
Precision Dairy and Livestock Management
Plenary
Industry Sponsors
Wireless Sensor Networks and Farm Connectivity
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Precision Horticulture
Big Data, Data Mining and Deep Learning
In-Season Nitrogen Management
Digital Agriculture Solutions for Soil Health and Water Quality
Robotics and Automation with Row and Horticultural Crops
On Farm Experimentation with Site-Specific Technologies
Farm Animals Health and Welfare Monitoring
Artificial Intelligence (AI) in Agriculture
Profitability and Success Stories in Precision Agriculture
Application of Granular Materials with Drones
Data Analytics for Production Ag
Land Improvement and Conservation Practices
Country Representative Report
Site-Specific Pasture Management
Precision Crop Protection
Drone Spraying
Decision Support Systems
Precision Agriculture and Global Food Security
Geospatial Data
Precision Agriculture for Sustainability and Environmental Protection
Social Science Applications within Precision Agriculture
Edge Computing and Cloud Solutions
Site-Specific Nutrient, Lime and Seed Management
Extension or Outreach Education of Precision Agriculture
Scouting and Field Data collection with Unmanned Aerial Systems
Precision Dairy and Livestock Management
Drainage Optimization and Variable Rate Irrigation
Weather and Models for Precision Agriculture
Education of Precision Agriculture Topics and Practices
Genomics and Precision Agriculture
Small Holders and Precision Agriculture
Demonstration
International Symposium on Robotics and Automation
Meeting
Type
Poster
Oral
Year
2016
2008
2022
2024
Home » Conference » Results

Conference

Filter results785 paper(s) found.

1. Field Evaluation of a Variable-rate Aerial Application System

Variable rate aerial application systems are becoming more readily available; however, aerial applicators typically only use the systems for constant rate application of materials, allowing the systems to compensate for upwind and downwind ground speed variations. Much of the resistance to variable rate application system adoption pertains to applicator’s trust in the systems to turn on and off automatically as desired.  If an application system operating in an automatic mode ... D.E. Martin, C. Yang

2. Ground-based Imagery Data Collection of Cotton Using a Robotic Platform

In modern agriculture, technological advancements are pivotal in optimizing crop production and resource management. Integrating robotics and image processing techniques allows the efficient collection, analysis, and storage of high-resolution images crucial for monitoring crop health, identifying pest infestations, assessing growth stages, making precise management decisions and predicting yield potential. The objective of this project is to utilize the Farm-NG Amiga robot to develop an imag... O. Fernandez, M. Bhandari, J.L. Landivar-scoot, M. Eldefrawy, L. Zhao, J. Landivar

3. Integrating Nonlinear Models and Remotely Sensed Data to Estimate Crop Cardinal Dates

Crop planting and harvest dates are a major component affecting agricultural productivity, risk, and nutrient cycling. The ability to track these cardinal dates allows researchers to investigate strategies to manage risk and adapt to climate change. This study was conducted to determine whether nonlinear statistical models combined with remotely sensed data from satellites can be used to estimate planting and harvest dates. Time of planting and harvest were reported by farmers for 16 commerci... C.L. Dos santos, F. Miguez, L. Puntel, D. Bullock

4. Integration of High Resolution Multitemporal Satellite Imagery for Improving Agricultural Crop Classification: a Case Study

Timely and accurate agriculture information is vital for ensuring global food security. Satellite imagery has already been proved as a reliable tool for remote crop mapping. Planet satellite imagery provides high cadence, global satellite coverage with higher temporal and spatial resolution than the Landsat-8 and Sentinel-2. This study examined the potential of utilizing high-resolution multitemporal imagery along with and normalized difference vegetation index (NDVI) to map the agricultural ... U. Ali, T. Esau, A. Farooque, Q. Zaman

5. #DigitAg France

#DigitAg, the Digital Agriculture Convergence Laboratory, is one of 10 French Convergence Institutes financed by the Investissements d'Avenir (Investment for the Future) program. #DigitAg conducts interdisciplinary research between agronomic sciences, engineering sciences (computer science, mathematics, electronics, physics, etc.) and social and management sciences (economics, sociology, business management), bringing together more than 700 experts in these fields to produce the scientifi... J. Taylor

6. 'Spatial Discontinuity Analysis' a Novel Geostatistical Algorithm for On-farm Experimentation

Traditional agronomic experimentation is restricted to small plots. Under appropriate experimental designs the effects of uncontrolled environmental variables are minimized and the measured responses (e.g. in yields) are compared to controllable inputs (seed, tillage, fertilizer, pesticides) using well-trusted design-based statistical methods. However, the implementation of such experiments can be complex and the application, management, and harvesting of treated areas might have to... S. Rudolph, B.P. Marchant, V. Gillingham, D. Kindred, R. Sylvester-bradley

7. 25 Years Precision Agriculture in Germany - a Retrospective

It all started with the availability of Global Positioning Systems for civil services in 1988. In the same year variable rate applications of fertilizers were demonstrated in northern Germany and Denmark, which were globally the first of their kind and introduced a new era of agricultural production. The idea of Computer Aided Farming (CAF) was born. Only one year later the first yield maps were established. In 1992 at the Soil Specific Crop Management Workshop in Bloomington, Minnesota which... H. Lilienthal, E. Schnug, S. Haneklaus

8. 3D Computer Vision with a Spatial-temporal Neural Network for Lameness Detection of Sows

The lameness of sows is one of the biggest concerns for swine producers, which can lead to considerable economic losses due to reduced productivity and welfare. There is a real need for early detection of lameness in sows to enable timely intervention and minimize loss. Currently, lame detection relies on visual observation and locomotion scoring of sows, which is subjective, labor-intensive, and difficult to conduct for large groups of animals within a short time. This study presents 3D comp... Y. Wang, Y. Lu, D. Morris, M. Benjamin, M. Lavagnino, J. Mcintyre

9. 3d Object Recognition, Localization and Treatment of Rumex Obtusifolius in Its Natural Environment

Rumex obtusifolius is one of the most highly competitive and persistent sorts of weed in agriculture. An automatic recognition and plant-treatment system is currently under development as an alternative treatment technique. An infrared-laser triangulation sensor and a high-resolution smart camera are used to generate 3D images of the weeds and their natural environment. In a segmentation process, contiguous surface patches are separated from one other. These 3D surface patc... M. Holpp, T. Anken, D. Seatovic, R. Grueninger, R. Hueppi

10. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data

Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri

11. A Case for Increased Precision Pesticide Application Adoption in California Perennial Specialty Crop Production

Maintaining high and quality crop yields in California’s diverse agriculture requires both good crop care through nutrient management and water management and effective crop protection through integrated pest management (IPM). Despite the promotion and adoption of non-chemical IPM practices in California such as sanitation and biological control, pesticide use remains inevitable in many cases. According to the 2021 California Pesticide Use Report, 37,444,331 kg of pesticide was used in ... P.A. Larbi

12. A Case Study Approach for Teaching and Applying Precision Agriculture

Students often struggle understanding precision agriculture principles and how these principles can be applied to farming operations. A case-study approach that requires students to own a recreational global positioning system (GPS) for collecting on-farm data could be a method for helping students understand and apply precision agriculture. This paper describes a case-study approach to teaching precision agriculture using student owned GPS units and geographical information systems (GIS) sof... J.D. Williams

13. A Content Review of Precision Agriculture Courses Across the US

Knowledge of what precision agriculture (PA) content is currently taught across the United States will help build a better understanding for what PA instructors should incorporate into their classes in the future. The University of Missouri partnered with several universities throughout the nation on a USDA challenge grant. Precision Agriculture faculty from 24 colleges/universities from across the U.S. shared their PA content by sharing their syllabi from 43 different courses. The syllabi we... D. Skouby, L. Schumacher, M. Yost, N.R. Kitchen

14. A Context Changing with Precision Agriculture in Japan

A new context is emerging under introducing of precision agriculture, impacted by top-down ICT policies and bottom-up collaborative activities. Food chain is changing by a holistic technology policy of integration in the fields of breeding, farm production, processing, transportation, and market in consumers. A new ICT strategy was issued by the government for precision agriculture to enhance the interoperability and portability of data/information sets collected from the field. The administr... S. Shibusawa

15. A Data Fusion Method for Yield and Soil Sensor Maps

Utilizing yield maps to their full potential has been one of the challenges in precision agriculture.  A key objective for understanding patterns of yield variation is to derive management zones, with the expectation that several years of quality yield data will delineate consistent productivity zones.  The anticipated outcome is a map that shows where soil productive potentials differ.  In spite of the widespread usage of yield monitors, commercial agriculture has found it dif... E. Lund, C. Maxton, T. Lund

16. A Data Retrieval System to Support Observational Research of On-Farm Experimentation

Observational research is a powerful methodology, capable of rapidly identifying trends and patterns present in complex systems. New work seeks to apply these techniques to agronomic production systems. While data generated from on-farm experimentation are often considered anecdotal, these data hold significant importance for farmers because they originate from their distinctive agricultural systems. Combining the large volumes of farmer-collected data with remote sensing, environmental, and ... P. Lanza, A. Yore, L. Longchamps

17. A Decade of Precision Agriculture Impacts on Grain Yield and Yield Variation

Targeting management practices and inputs with precision agriculture has high potential to meet some of the grand challenges of sustainability in the coming century, including simultaneously improving crop yields and reducing environmental impacts. Although the potential is high, few studies have documented long-term effects of precision agriculture on crop production and environmental quality. More specifically, long-term impacts of precision conservation practices such as cover crops, no-ti... M.A. Yost, N. Kitchen, K. Sudduth, S. Drummond, J. Sadler

18. A Decision-support Tool to Optimize Mid-season Corn Nitrogen Fertilizer Management from Red, Green, Blue SUAS Images

Corn receives more nitrogen (N) fertilizer per unit area than any other row crop and optimized soil fertility management is needed to help maximize farm profitability. In Arkansas, N fertilizer for corn is delivered in two- or three-split applications. Three-split applications may provide a better match to crop needs and contribute to minimizing yield loss from N deficiency. However, the total amounts are selected based on soil texture and yield goal without accounting for early-season losses... A. Poncet, T. Bui, W. France, T. Roberts, L. Purcell, J. Kelley

19. A Digital Interactive Decision Dashboard to Analyze, Store and Share Year-to-year Crop Genotype Yield

The lag time between data collection and sharing is a critical bottleneck in order to make impactful decision at farmer field-scale. Following this line, there is a need for developing a digital interactive decision dashboard for sharing results of crop trials, in parallel to establish a database for storing data. These crop trials, invaluable for farmers seeking to determine the optimal genotype for their crops, are at risk of becoming obsolete due to the current format and the lack of more ... P. Magalhaes cisdeli, G.N. Nocera santiago, I. Ciampitti, C. Hernandez

20. A Digital Twin for Arable Crops and for Grass

There is an opportunity to use process-based cropping systems models (CSMs) to support tactical farm management decisions, by monitoring the status of the farm, by predicting what will happen in the next few weeks, and by exploring scenarios. In practice, the responses of a CSM will deviate more and more from reality as time progresses because the model is an abstraction of the real system and only approximates the responses of the real system. This limitation may be overcome by using the CSM... F. Van evert, P. Van oort, B. Maestrini, A. Pronk, S. Boersma, M. Kopanja, G. Mimić

21. A Dynamic Variable Rate Irrigation Control System

Currently variable rate irrigation (VRI) prescription maps used to apply water differentially to irrigation management zones (IMZs) are static.  They are developed once and used thereafter and thus do not respond to environmental variables which affect soil moisture conditions.  Our approach for creating dynamic prescription maps is to use soil moisture sensors to estimate the amount of irrigation water needed to return each IMZ to an ideal soil moisture condition.  The UGA Sma... G. Vellidis, V. Liakos, W. Porter, X. Liang, M.A. Tucker

22. A Flexible Software Architecture for General Precision Agriculture Decision Support Systems

Agricultural data management is a complex problem. Both the data and the needs of the users are diverse. Given the complexity of the problem, it's easy to ascertain that a single solution will not be able to meet the needs of all users. This paper presents a software architecture designed to be extensible as well as flexible enough to provide agricultural management tools for a wide variety of users. The solution is based on a microservice architecture, which allows for the creation of ne... W. Neils, D. Mommen

23. A Framework for Imputation of Missing Parts in UAV Orthomosaics Using Planetscope and Sentinel-2 Data

In recent years, the emergence of Unmanned Aerial Vehicles (UAV), also known as drones, with high spatial resolution, has broadened the application of remote sensing in agriculture. However, UAV images commonly have specific problems with missing areas due to drone flight restrictions. Data mining techniques for imputing missing data is an activity often demanded in several fields of science. In this context, this research used the same approach to predict missing parts on orthomosaics obtain... F.R. Pereira, A.A. Dos reis, R.G. Freitas, S.R. Oliveira, L.R. Amaral, G.K. Figueiredo, J.F. Antunes, R.A. Lamparelli, E. Moro, N.D. Pereira, P.S. Magalhães

24. A Fusion Strategy to Map Corn Crop Residues

Access to post-harvest residue coverage information is crucial for agricultural management and soil conservation. The purpose of this study was to present a new approach based on an ensemble at the decision level for mapping the corn residue. To this end, a set of Landsat 8 imagery and field data including the Residue Cover Fraction (RCF) of corn (149 samples), were used. Firstly, a map of common spectral indices for RCF modeling was prepared based on the spectral bands. Then, the efficiency ... S. Fathololoumi, M.K. Firozjaei, A. Biswas, P. Daggupati

25. A Generative Adversarial Network-based Method for High Fidelity Synthetic Data Augmentation

Digital Agriculture has led to new phenotyping methods that use artificial intelligence and machine learning solutions on image and video data collected from lab, greenhouse, and field environments. The availability of accurately annotated image and video data remains a bottleneck for developing most machine learning and deep learning models. Typically, deep learning models require thousands of unique samples to accurately learn a given task. However, manual annotation of a large dataset will... S. Sridharan, S. Sornapudi, Q. Hu, S. Kumpatla, J. Bier

26. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal Data

Field scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locati... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi

27. A Harvesting Robot System for Fresh Cherry Tomato in Greenhouse

In order to improve the , a new harvesting robot system for cherry tomato was designed and tested, which mainly consisted of a railed-type vehicle, a visual servo unit, a manipulator, a picking end-effector, and other accessories. According to the greenhouse environment and the standard planting mode, the robot configuration was determined, whose operating space could be adjusted horizontally and vertically in order to enlarge the harvesting range. Besides, a harvested fruits automatic transp... F. Qingchun, W. Xiu, W. Xiaonan, W. Guohua

28. A High-throughput Phenotyping System Evaluating Salt Stress Tolerance in Kale Plants Cultivated in Aquaponics Environments

Monitoring plant growth in a controlled environment is crucial to make informed decisions for various management practices such as fertilization, weed control, and harvesting. Agronomic, physiological, and architectural traits in kale plants (Brassica oleracea) are important to producers, breeders, and researchers for assessing the performance of the plants under biotic and abiotic stresses.  Traditionally, architectural, and morphological traits have been used to monitor plant growth. H... T. Rehman, M. Rahman, E. Ayipio, D. Lukwesa, J. Zheng, D. Wells, H.H. Syed

29. A Hyperlocal Machine Learning Approach to Estimate NDVI from SAR Images for Agricultural Fields

The normalized difference vegetation index (NDVI) is a key parameter in precision agriculture used globally since the 1970s. The NDVI is sensitive to the biochemical and physiological properties of the crop and is based on the Red (~650 nm) and NIR (~850 nm) spectral bands. It is used as a proxy to monitor crop growth, correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions which might al... R. Pelta, O. Beeri, T. Shilo, R. Tarshish

30. A Low-tech Approach to Manage Within Field Variability – Toward a Territorial Scale Application

Managing within field variability is promising to achieve European objectives of sustainability in crop production. Technological development has allowed to precisely characterize fields heterogeneity in space and time. However, learnings from low adoption of yield maps in west-European context have highlighted the importance of reliable methods to support decisions. Blackmore et al. designed a delineation method considering yield as an integrative variable that reflects spatial and ... A. Lenoir, B. Vandoorne, B. Dumont

31. A Multi Sensor Data Fusion Approach for Creating Variable Depth Tillage Zones.

Efficiency of tillage depends largely on the nature of the field, soil type, spatial distribution of soil properties and the correct setting of the tillage implement.  However, current tillage practice is often implemented without full understanding of machine design and capability leading to lowered efficiency and further potential damage to the soil structure. By modifying the physical properties of soil only where the tillage is needed for optimum crop growth, variable depth tillage (... D. Whattoff, D. Mouazen, D. Waine

32. A Multi-level Filtering Approach for Yield Data Cleaning and Automated Analysis Using R Programming

In the realm of on-farm studies, a recurring challenge surfaces in the form of disparities between field implementation and experimental design within Rx treatment plots. This disjunction underscores the critical need for intensive data cleaning and analysis to generate precise outcomes for the experiments. Complicating matters is the absence of readily available ground truth data for comparative analyses, making it particularly challenging to ascertain the extent of necessary data cleaning a... S. Vinod, J.D. Luck

33. A Multi-objective Optimisation Analysis of Virtual Fencing in Precision Grazing

Virtual fencing is a precision livestock farming tool consisting of invisible boundaries created via Global Navigation Satellite Systems (GNSS) and managed remotely and in real time by app-based technology. Grazing livestock are equipped with battery-powered collars capable of delivering audio or vibration cues and possibly electric shocks when approaching or crossing an invisible boundary. Virtual fencing makes precision grazing possible without the need for physical fences. This technology ... E. Maritan, K. Behrendt, J. Lowenberg-deboer, S. Morgan, M.S. Rutter

34. A New Approach for Quantitative Land Suitability Evaluation Using Geostatistics, Remote Sensing (Rs) and Geographic Information System (Gis)

The objective of this study was to incorporate geostatistics, remote sensing and geographic information system methods due to improving the quantitative land suitability assessment in Arsanjan plain, southern Iran. The primary data was collected from 85 soil samples from tree depths (0­30, 30­60 and 60­90 cm) and the secondary information from remotely sensed data “LISS­III receiver from IRS­P6 satellite”. In order to identify the spatial dependence of soil imp... M. Baghernejad, M. Emadi

35. A Passive-RFID Wireless Sensor Node for Precision Agriculture

Accurate soil data is crucial for precision agriculture.  While existing optical methods can correlate soil health to the gasses emitted from the field, in-soil electronic sensors enable real-time measurements of soil conditions at the effective root zone of a crop. Unfortunately, modern soil sensor systems are limited in what signals they can measure and are generally too expensive to reasonably distribute the sensors in the density required for spatially accurate feedback.  In thi... P.J. Goodrich, C. Baumbauer, A.C. Arias

36. A Photogrammetry-based Image Registration Method for Multi-camera Systems

In precision agriculture, yield maps are important for farmers to make plans. Farmers will have a better management of the farm if early yield map can be created. In Florida, citrus is a very important agricultural product. To predict citrus production, fruit detection method has to be developed. Ideally, the earlier the prediction can be done the better management plan can be made. Thus, fruit detection before their mature stage is expected. This study aims to develop a thermal-visible camer... H. Gan, W. Lee, V. Alchanatis

37. A Precise Fruit Inspection System for Huanglongbing and Other Common Citrus Defects Using GPU and Deep Learning Technologies

World climate change and extreme weather conditions can generate uncertainties in crop production by increasing plant diseases and having significant impacts on crop yield loss. To enable precision agriculture technology in Florida’s citrus industry, a machine vision system was developed to identify common citrus production problems such as Huanglongbing (HLB), rust mite and wind scar. Objectives of this article were 1) to develop a simultaneous image acquisition system using multiple c... D. Choi, W. Lee, J.K. Schueller, R. Ehsani, F.M. Roka, M.A. Ritenour

38. A Software for Managing Remotely Sensed Imagery of Orchards Plantations for Precision Agriculture

Agronomic and environmental characteristics of fruit orchards/ forests can be automatically assessed from remote-sensing images by a computer programme named Clustering Assessment (CLUAS®). The aim of this paper is to describe the operational procedure of CLUAS and illustrate examples of the information provided for citrus orchards and Mediterranean forest. CLUAS® works as an additional menu (“add-on”) of ENVI®, a world-wide known image-processing programme, and operat... L. Garcia-torres, J.M. Peña-barragán, D. Gómez-candón, F. López-granados, M. Jurado-expósito

39. A Tree Planting Site-Specific Fumigant Applicator for Orchard Crops

The goal of this research was to use recent advances in the global positioning system and computer technology to apply just the right amount of fumigant where it is most needed (i.e., in the neighborhood of each tree planting site or tree- planting-site-specific application) to decrease the incidence of replant disease, and achieve the environmental and economical benefits of reducing the application of these toxic chemicals. In the first year of this study we retrofitted a chemical applicato... S.K. Upadhayaya, V. Udompetaikul, M.S. Shafii, G.T. Browne

40. Accuracy of Differential Rate Application Technology for Aerial Spreading of Granular Fertiliser Within New Zealand

Aerial topdressing of granular fertilizer is common practice on New Zealand hill country farms because of the challenging topography. Ravensdown Limited is a New Zealand fertilizer manufacturer, supplier and applicator, who are funding research and development of differential rate application from aircraft. The motivation for utilising this technology is to improve the accuracy of fertilizer application and fulfil the variable nutrient requirements of hill country farms.  The capability ... I.J. Yule, S.E. Chok, M.C. Grafton, M. White

41. Accurately Mapping Soil Profiles: Sensor Probe Measurements at Dense Spatial Scales

Proximal sensing of soil properties has typically been accomplished using various sensor platforms deployed in a continuous sensing mode collecting data along transects, typically spaced 10-20 meters apart. This type of sensing can provide detailed maps of the X-Y soil variability and some sensors provide an indication of soil properties within the profile, however without additional investigations the profile is not delineated precisely.  Alternatively, soil sensor probes can provide de... T. Lund, E. Lund, C.R. Maxton

42. Active and Passive Crop Canopy Sensors As Tools for Nitrogen Management in Corn

The objectives of this research were to (i) assess the correlation between active and passive crop canopy sensors’ vegetation indices at different corn growth stages and (ii) assess sidedress variable rate nitrogen (N) recommendation accuracy of active and passive sensors compared to the agronomic optimum N rate (AONR). The experiment was conducted near Central City, Nebraska on a Novina sandy loam planted to corn on 15 April 2015. The experiment was a randomized complete-block design w... L. Bastos, R. Ferguson

43. Active Learning-based Measurements Prediction in Sparsely Observed Agricultural Fields

The sustainability of farming methods relies on the quality of soil health. Rich soil supplies vital nutrients to plants. The soil structure and aggregation possess crucial physical attributes that facilitate the infiltration of water and air, as well as enable roots to explore. Long-term and extensive monitoring of soil data is crucial for obtaining important information into the water dynamics of the land surface. Soil moisture dynamics play a critical role in the hydrothermal process that ... D. Agarwal, A. Tharzeen, B. Natarajan

44. Adjustment of Corn Population and Nitrogen Fertilization Based on Management Zones

The main objective of this study was to adjust the corn population and nitrogen fertilization according to management zones, based on past grain yield maps (seven of soybean and three of corn) and soil electrical conductivity. The study was carried out in Não-Me-Toque, Rio Grande do Sul, Brazil, and it was conducted in a factorial strip blocks with 3 repetitions in each management zone, being the treatments: corn populations (56000, 64000, 72000, 80000 and 88000 plants ha-1)... R. Schwalbert, T.J. Carneiro amado, T. Horbe, G.M. Corassa, F.H. Gebert

45. Advanced Classification of Beetle Doppelgängers Using Siamese Neural Networks and Imaging Techniques

The precise identification of beetle species, especially those that have similar macrostructure and physical characteristics, is a challenging task in the field of entomology. The term "Beetle Doppelgängers" refers to species that exhibit almost indistinguishable macrostructural characteristics, which can complicate tasks in ecological studies, conservation efforts, and pest management. The core issue resides in their striking similarity, frequently confusing both experts and a... P.R. Armstrong, L.O. Pordesimo, K. Siliveru, A.R. Gerken, R.O. Serfa juan

46. Advancements in Agricultural Robots for Specialty Crops: a Comprehensive Review of Innovations, Challenges, and Prospects

The emergence of robot technology presents a timely opportunity to revolutionize specialty crop production, offering crucial support across various activities such as planting, supporting general traits, and harvesting. These robots play a pivotal role in keeping stakeholders up-to-date of developments in their production fields, while providing them the capability to automate laborious tasks. Then, to elucidate the advancements in this domain, we present the results of a comprehensive review... M. Barbosa, R. Santos, L. Sales, L. Oliveira

47. Advancements in Agrivoltaics: Autonomous Robotic Mowing for Enhanced Management in Solar Farms

Agrivoltaics – the co-location of solar energy installations and agriculture beneath or between rows of photovoltaic panels – has gained prominence as a sustainable and efficient approach to land use. The US has over 2.8 GW in Agrivoltaics, integrating crop cultivation with solar energy. However, effective vegetation management is critical for solar panel efficiency. Flat, sunny agricultural land accommodates solar panels and crops efficiently. The challenge lies in managing grass... S. Behera, S. Pitla

48. Advancing Adaptive Agricultural Strategies: Unraveling Impacts of Climate Change and Soils on Corn Productivity Using APSIM

With unprecedented challenges to achieve sustainable crop productivity under climate change and dynamic soil conditions, adaptive management strategies are required for optimizing cropping systems. Using sensors, cropping systems can be continuously monitored and the data collected by them can be analyzed for making informed adaptive management decisions to enhance productivity and environmental sustainability. But sensors can only tell the past and decisions bring implications into the ... H. Pathak, C.J. Warren, D. Buckmaster, D.R. Wang

49. Aerial Photographs to Predict Yield Loss Due to N Deficiency in Corn

Nitrogen fertilizer is a crucial input for corn production, and in the U.S. more nitrogen is applied to corn than to all other crops combined.  In wet weather, nitrogen can be lost from soil by leaching and by denitrification.  Which process predominates depends largely on soil drainage.  Nitrogen deficiency in nearly any plant is expressed by a lighter green color of leaves than in nitrogen-sufficient plants.  Nitrogen deficiency in corn can be easily seen from the air.&n... P. Scharf

50. Affordable Telematics System for Recording and Monitoring Operational Data in Crop Farming

The aim of this research was to create an affordable telematics system for agricultural tractors for enhancing existing data logging capabilities. This system enables real-time transmission of operational data from the tractor's CAN bus to a server for storage, monitoring, and further analysis. By leveraging standardized communication protocols like ISO 11783 and J1939, operational data such as fuel consumption and engine load can be easily monitored. The system was built around a Raspber... A. Lajunen, H. Hovio

51. Africa Regional Meeting

... K.A. Frimpong

52. African Association for Precision Agriculture Community Meeting

... K.A. Frimpong, V. Aduramigba-modupe, N. Fassinou hotegni

53. AgDataBox-IA – Web Application with Artificial Intelligence for Agricultural Data Analysis in Precision Agriculture

Agriculture has been continually evolving, incorporating hardware, software, sensors, aerial surveys, soil sampling for chemical, physical, and granulometric analysis (based on sample grids), and microclimatic data, leading to a substantial volume of data. This requires platforms to store, manage, and transform these data into actionable information for decision-making in the field. In this regard, Artificial Intelligence (AI) is the most widely used tool globally to mine and transform vast d... R. Sobjak, C.L. Bazzi, K. Schenatto, W.K. Oliveira, A.E. Menegasso

54. AgDataBox-IoT - Managing IoT Data and Devices on Precision Agriculture

The increasing global population has resulted in a substantial demand for nourishment, which has prompted the agricultural sector to investigate ways to improve efficiency. Precision agriculture (PA) uses advanced technologies such as the Internet of Things (IoT) and sensor networks to collect and analyze field information. Although the advantages are numerous, the available data storage, management, and analysis resources are limited. Therefore, creating and providing a user-friendly web app... C.L. Bazzi, W.K. Oliveira, R. Sobjak, K. Schenatto, E. Souza, A. Hachisuca, F. Franz

55. AgDataBox-IoT Application Development for Agrometeorogical Stations in Smart Farm

Currently, Brazil is one of the world’s largest grain producers and exporters. Brazil produced 125 million tons of soybean in the 2019/2020 growing season, becoming the world’s largest soybean producer in 2020. Brazil’s economic dependence on agribusiness makes investments and research necessary to increase yield and profitability. Agriculture has already entered its 4.0 version, also known as digital agriculture, when the industry has entered the 4.0 era. This new paradigm ... A. Hachisuca, E.G. Souza, E. Mercante, R. Sobjak, D. Ganascini, M. Abdala, I. Mendes, C. Bazzi, M. Rodrigues

56. AgDataBox: Web Platform of Data Integration, Software, and Methodologies for Digital Agriculture

Agriculture is challenging to produce more profitably, with the world population expected to reach some 10 billion people by 2050. Such a challenge can be achieved by adopting precision agriculture and digital agriculture (Agriculture 4.0). Digital agriculture has become a reality with the availability of cheaper and more powerful sensors, actuators and microprocessors, high-bandwidth cellular communication, cloud communication, and Big Data. Digital agriculture enables the flow of informatio... E.G. Souza, C. Bazzi, A. Hachisuca, R. Sobjak, A. Gavioli, N. Betzek, K. Schenatto, E. Mercante, M. Rodrigues, W. Moreira

57. AgGateway Traceability API – The Foundation to Track Raw Agricultural Commodities

There is increasing demand for food traceability, ranging from consumers wanting to know where their food comes from (GMO, organic, climate-smart commodities), to manufacturers of agricultural inputs wanting to know the effectiveness of their products as used by farmers. Existing traceability requirements focus on the supply chain of goods packaged from their origin to retail grocery stores, with regulations provided by the Food Safety Modernization Act (FSMA) from the US Food and Drug Admini... S.T. Nieman, J. Tevis, B.E. Craker

58. AGOC: Agriculture Operations Center

After another long day, the farmer sits down in front of a computer (wishing this time was instead spent on the front porch catching a last glimpse of the sunset), and reflects once again ...     What if   ...  I actually knew the health of 100% of my crops rather than what I know today. a mere 20%. What if   ...  there was an effective, simple way to synchronize crop scouting and crop imagery efforts. ... M. Zamzow, H. Moulton

59. Agricultural Robots Classification Based on Clustering by Features and Function

Robotic systems in agriculture (hereafter referred to as agrobots) have become popular in the last few years. They represent an opportunity to make food production more efficient, especially when coupled with technologies such as the Internet of Things and Big Data. Agrobots bring many advantages in farm operations: they can reduce humane fatigue and work-related accidents. In contrast, their large-scale diffusion is today limited by a lack of clarity and exhaustiveness in the regulatory fram... M. Canavari, M. Medici, G. Rossetti

60. Agriculture Machine Guidance Systems: Performance Analysis of Professional GNSS Receivers

GNSS (Global Navigation Satellite Systems) plays nowadays a major role in different civilian activities and is a key technology enabling innovation in different market sectors. For instance, GNSS-enabled solutions are widespread within the Precision Agriculture and, among them, applications in the field of machinery guidance are commonly employed to optimize typical agriculture practices. The scope of this paper is to present the outcomes of the agriculture testing campaign performe... J. Capolicchio, D. Mennuti, I. Milani, M. Fortunato, R. Petix, J. Reyes gonzalez, M. Sunkevic

61. Agronomic Characteristics of Green Corn and Correlations with Productivity for the Establishment of Management Zones in Vale Do Ribeira, SP, Brazil

In Brazil, the progressive development in the cultivation of the corn for consumption in the green stadium stands by the relevant socio-economic role that this related to multiple applications, the attractive market price and continuous demand for the product in nature. Therefore, this study was to analyze the correlations and spatial variability of the productivity of the culture of the green corn in winter, in alluvial soil of the type Cambisols eutrophic in the amount areas and Hydromorphi... W.J. Souza, V.S. Akune, S.H. Benez, L.C. Citon, P.H. Nakazawa, A.J. Santana neto

62. Agronomic Opportunities Highlighted by the Hands Free Hectare and Hands Free Farm Autonomous Farming Projects

With agriculture facing various challenges including population increase, urbanisation and both mitigating and managing climate change, agricultural automation and robotics have long been seen as potential solutions beyond precision farming. The Hands Free Hectare (HFH) and Hands Free Farm (HFF) collaborative projects based at Harper Adams University (HAU) have been developing autonomous farming systems since 2016 and have conducted multiple autonomous field crop production cycles since a wor... K.F. Franklin

63. Agrosense: AI-enabled Sensing for Precision Management of Tree Crops

Monitoring the tree inventory and canopy density and height frequently is critical for researchers and farm managers. However, it is very expensive and challenging to manually complete these tasks weekly. Therefore, a low-cost and artificial intelligence (AI) enhanced sensing system, Agrosense, was developed for tree inventory, canopy height measurement, and tree canopy density classification in this study. The sensing system mainly consisted of four RGB-D cameras, two Jetson Xavier NX, and o... C. Zhou, Y. Ampatzidis, H. Guan, W. Liu, A. De oliveira costa neto, S. Kunwar, O. Batuman

64. AGTECH CHILE: an Outreach and Technology Transfer Platform for Closing Gaps in Emerging Chilean Precision Agriculture Companies

Precision agriculture (PA) is being developed in Chile since 1997. Today there are approximately 20 companies providing products and services in PA at different levels. Most of them are young entrepreneurships which have important knowledge gaps, particularly on technology basis and data management to transform them into useful information. In order to help closing some of the gaps, and contributing to the development of an innovation ecosystem, an extension proposal was developed, ... R.A. Ortega, P. Trebilcock

65. AI Enabled Targeted Robotic Weed Management

In contemporary agriculture, effective weed management presents a considerable challenge necessitating innovative solutions. Traditional weed control methods often rely on the indiscriminate application of broad-spectrum herbicides, giving rise to environmental concerns and unintended crop damage. Our research addresses this challenge by introducing an innovative AI-enabled robotic system designed to identify and selectively target weeds in real-time. Utilizing the advanced Machine Learning t... A. Balabantaray, S. Pitla

66. AI Tools in Agri DSS Pipeline - the Case of Irrigated Sugarbeet

A general pipeline that can be associated to a DSS includes several steps. Data Collectionn includes Acquisition, extraction, and aggregation of data from previously identified and selected sources. Data Cleaning and preparation make data available for exploratory analysis that make them usable. Data Analysis is then applied to extract meaningful information e.g. by statistical and/or simulation models. Data are successively synthesized and visualized to make them clear to the decision-maker ... G.-. Vitali, C. Ferraz

67. AI-based Fruit Harvesting Using a Robotic Arm

Fruit harvesting stands as a pivotal and delicate process within the agricultural industry, demanding precision and efficiency to ensure both crop quality and overall productivity. Historically reliant on manual labor, this labor-intensive endeavor has taken a significant leap forward with the advent of autonomous jointed robots and Artificial Intelligence (AI). Our project aims to usher in a new era in fruit harvesting, leveraging advanced technology to perform this essential task autonomous... H. Kulhandjian, N. Amely, M. Kulhandjian

68. AI-based Pollinator Using CoreXY Robot

The declining populations of natural pollinators pose a significant ecological challenge, often attributed to the adverse effects of pesticides and intensive farming practices. To address the critical issue of pollination in the face of diminishing natural pollinators, we are pioneering an AI-based pollinator that utilizes a CoreXY pollination system. This solution aims to augment pollination efforts in agriculture, increasing yields and crop quality while mitigating the adverse impacts of pe... H. Kulhandjian, M. Kulhandjian, D. Rocha, B. Bennett

69. AI-based Precision Weed Detection and Elimination

Weeds are a significant challenge in agriculture, competing with crops for resources and reducing yields. Addressing this issue requires efficient and sustainable weed elimination systems. This paper presents a comprehensive overview of recent advancements in weed elimination system development, focusing on innovative technologies and methodologies. Specifically, it details the development and integration of a weed detection and elimination system based on the CoreXY architecture, implemented... H. Kulhandjian, M. Kulhandjian, D. Rocha, B. Bennett

70. AI-enabled 3D Vision System for Rapid and Accurate Tree Trunk Detection and Diameter Estimation

Huanglongbing (HLB) is the major threat to citrus production in Florida. Imidacloprid and oxytetracycline injections were proven to be effective in controlling HLB. The total amount of imidacloprid and oxytetracycline needs to be injected for the tree depending on the trunk diameter. Therefore, precisely measuring trunk diameter is important to effectively control the HLB. However, manually injecting imidacloprid or oxytetracycline and measuring the trunk diameter is time-consuming and labor-... C. Zhou, Y. Ampatzidis

71. AIR-N: AI-Enabled Robotic Precision Nitrogen Management Platform

The AI-Enabled Robotic Nitrogen Management (AIR-N) system is a versatile, cloud-based platform designed for precision nitrogen management in agriculture, targeting the reduction of nitrous oxide emissions as emphasized by the EPA. This end-to-end integrated system is adaptable to various cloud services, enhancing its applicability across different farming environments. AIR-N's framework consists of three primary components: a sensing layer for gathering data, a cloud layer where AI and ma... A. Kalra, S. Pitla, J.D. Luck

72. Airborne Spectral Detection of Leaf Chlorophyll Concentration in Wild Blueberries

Leaf chlorophyll concentration (LCC) detection is crucial for monitoring crop physiological status, assessing the overall health of crops, and estimating their photosynthetic potential. Fast, non-destructive, and spatially extensive monitoring of LCC in crops is critical for accurately diagnosing and assessing crop health in large commercial fields. Advancements in hyperspectral remote sensing offer non-destructive and spatially extensive alternatives for monitoring plant parameters such as L... K. Barai, C. Ewanik, V. Dhiman, Y. Zhang, U.R. Hodeghatta

73. Airspeed and Pressure Affect Spray Droplet Spectrum from an Aerial Nozzle for Fixed-wing Applications

The atomization of the droplets generated by a flat fan nozzle has been studied in the IEA-I high speed wind tunnel at NERCIEA with Marvern Spraytec Laser Diffraction system. The measurement point is set at 0.15m, 0.25m and 0.35m away from the orifice of the nozzle. The wind speed range is from 150km/h to 305km/h, and the tube pressure is set about 0.3MPa, 0.4MPa and 0.5MPa. The measuring distance from the orifice of the nozzle is found important to the diameter and relative span of the dropl... Q. Tang, L. Chen, R. Zhang, M. Xu, G. Xu, T. Yi

74. Algorithm to Estimate Sorghum Grain Number from Panicles Using Images Collected with a Smartphone at Field-scale

An estimation of on-farm yield before harvest is important to assist farmers on deciding additional input use, time to harvest, and options for end uses of the harvestable product. However, obtaining a rapid assessment of on-farm yield can be challenging, even more for sorghum (Sorghum bicolor L.) crop due to the complexity for accounting for the grain number at field-scale. One alternative to reduce labor is to develop a rapid assessment method employing computer vision and artificial intell... G.N. Nocera santiago, P. Cisdeli magalhães, I. Ciampitti, L. Marziotte

75. All for One and One for All: a Simulation Assessment of the Economic Value of Large-scale On-farm Experiment Network

While on-farm experiments offer invaluable insights for precision management decisions, their scope is usually confined to the specific conditions of individual farms and years, which limits the derivation of more broad and reliable decisions. To address this limitation, aggregating data from numerous farms of various crop growth conditions into a comprehensive dataset appears promising. However, the quantifiable value of this experiment network remains elusive, despite the common agreement o... X. Li

76. Allelopathic Effects of Sunflower (Helianthus Annuus) on Germination and Growth of Wild Barley (Hordeum Spontaneum)

Sunflower [Helianthus annuus (L.) Koch.] contains watersoluble allelochemicals that inhibit the ermination and growth of other species. This characteristic could be used in weed management programmes. Greenhouse and laboratory experiments were conducted to determine the effects on wild barley (Hordeum spontaneum Koch.) germination and seedling growth of(i) preceding crops, (ii) fresh sunflower residue incorporation, and (iii) sunflower leaf, stem, flower and root water extract concen... Z.Y. Ashrafi, H.R. Mashhadi, S. Sadeghi

77. Almond Canopy Detection and Segmentation Using Remote Sensing Data Drones

The development of Unmanned Aerial System (UAV) makes it possible to take high resolution images of trees easily. These images could help better manage the orchard. However, more research is necessary to extract useful information from these images. For example, irrigation schedule and yield prediction both rely on accurate measurement of canopy size. In this paper, a workflow is proposed to count trees and measure the canopy size of each individual tree. The performances of three different m... T. Zhao, M. Cisneros, Y. Chen, Q. Yang, Y. Zhang

78. Almonds and Pistachios: Sustaining Legacy, Innovations, and Nutritional Advancements in California

California's unique Mediterranean climate has made it the global epicenter for tree nut production, providing nearly 99 percent of the nation’s almond and pistachio supply. The California tree nut industry is characterized by its deep-rooted heritage, with 90% of its farms being family-owned and operated, often spanning multiple generations. These farmers have been at the forefront of agricultural innovation, investing approximately millions of dollars annually in scientific researc... H. Kulhandjian, S. Asci

79. An IoT-based Smart Real Time Sensing and Control of Heavy Metals to Ensure Optimal Growth of Plants in an Aquaponic Set-up

The concentration of heavy metals that needs to be maintained in aquaponic environments for habitable growth of plants has been a cause of concern for many decades now as it is not possible to eliminate them completely in a commercial set-up. Our goal is to design a cost-effective real-time smart sensing and actuation system in order to control the concentration of heavy metals in aquaponic solutions. Our solution consists of sensing the nutrient concentrations in the aquaponic solution, name... S. Dhal, J. Louis, N. O'sullivan, J. Gumero, M. Soetan, S. Kalafatis, J. Lusher, S. Mahanta

80. An Open Database of Crop Yield Response to Fertilizer Application for Senegal

Food security is one of the major global challenges today.  Africa is one of the continents with the largest gaps in terms of challenges for food security. In Senegal, about 60% of the population resides in rural areas and the cropping systems are characterized as a low productivity system, low input and in reduced areas, smallholder subsistence systems. Increasing crop productivity would have a positive impact on food security in this country. One of the main factors limiting crop produ... F. Gomez, A. Carcedo, A. Diatta, L. Nagarajan, V. Prasad, Z. Stewart, S. Zingore, I. Ciampitti, P. Djighaly

81. Analysis of High Yield Condition Using a Rice Yield Predictive Model

Rice production in Japan is facing problems of yield and quality instability owing to recent climate changes and a decline in rice prices, and possible competition with foreign inexpensive rice. Thus, it is becoming more important to stably achieve high yield and quality, while reducing production costs. Various data, including crop growth, farmer’s management styles, yield and quality, has recently become accessible in actual fields using advanced information and communication technolo... Y. Hirai, T. Yamakawa, E. Inoue, T. Okayasu, M. Mitsuoka

82. Analysis of the Mapping Results Using SoilOptix TM Technology in Chile After Two Seasons

Soil mapping is a key element to successfully implement Integrated Nutrient Management (INM) in high value crops.  SoilOptixTM is a mapping service based on the use of gamma radiation technology that arrived in Chile in 2019. Since then, around 2000 ha have been mapped, mainly in fruit orchards and vineyards. The technology has demonstrated its value in determining the most limiting factors in new and old orchards, and the possibility of correcting them in a site-spe... R.A. Ortega, A.F. Ortega, M.C. Orellana

83. Analysis of Yield Gaps in Sub-Saharan African Cereal Production Systems

Food production in sub-Saharan Africa (SSA) is one of the lowest and keeps declining across farmers’ fields season after season (Assefa et al., 2020; F Affholder, 2013). Yield gaps in cereal cropping systems have been reported by many researchers, attesting to the existence of huge variability in production levels of cereals such as corn, wheat, sorghum, rice and millet. across SSA. It is still unclear whether the yield gaps are similar in size or driven by similar factors across differ... E. Odoom, K.A. Frimpong, S. Phillips

84. Analytical and Technological Advancements for Soybean Quality Mapping and Economic Differentiation

In the past, measuring soybean protein and oil content required the collection of soybean seed samples and laboratory analyses. Modern on-the-go near-infrared (NIR) sensing technologies during the harvest and proximal remote sensing (aerial and satellite imagery) before harvest time can be used to provide an early estimate of seed quality levels, benchmark in-season predictions with at-harvest final seed quality and enable seed differentiation for farmers leading to better marketing strategie... A. Prestholt, C. Hernandez, I. Ciampitti , P. Kyveryga

85. Analytics Model for Predicting Sucrose Percentage in Sugarcane Using Machine Learning Techniques

Sucrose is one of the most important indicators in the final profitability of Colombian sugar mills, therefore, its understanding and forecast are fundamental for the business. In this work, a proposal is formulated for an analysis model that allows predicting the percentage of sucrose based on historical data from mechanically harvested farms with the objective of knowing the numerical value of sucrose for each month of milling and be able to plan monthly and annual sugar production. ... P. Valencia ramirez

86. Apparent Electrical Conductivity Sensors and Their Relationship with Soil Properties in Sugarcane Fields

One important tool within the technological precision agriculture (PA) package are the apparent electrical conductivity (ECa) sensors. This kind of sensor shows the ability in mapping soil physicochemical variability quickly, with high resolution and at low cost. However, the adoption of this technology in Brazil is not usual, particularly on sugarcane fields. A major issue for farmers is the applicability of ECa, how to convert ECa data in knowledge that may assist the producer in decision-m... G.M. Sanches, L.R. Amaral, T. Pitrat, T. Brasco, P.S. Magalhaes, D.G. Duft, H.C. Franco

87. Apparent Soil Electrical Conductivity As an Indicator of Failed Subsurface Drains

It is estimated that 2,000 ha of cropland are taken out of production daily worldwide due to salinization and sodification. Salinity is estimated to result in economic losses of $27.3 billion U.S. dollars annually. Our project aimed to develop techniques for quantifying the severity of soil-water salinity and impacts on crop production in the Lower Arkansas River Valley (LARV) in Colorado. The Fairmont Drainage District (FDD) study site in the LARV is a furrow-irrigated, tile-drained area of ... A. Andales, A.J. Brown

88. Application Accuracy of Two Different Sprayer Flow Control Systems During Site-specific Pesticide Applications

Precise and efficient pesticide applications are crucial aspects of modern agriculture to effectively manage pests throughout the season while also reducing the negative impacts of pesticides on the environment. Recent advancements in spray technology, such as pulse width modulation (PWM) and individual nozzle control, have enabled capabilities for site-specific pesticide applications on modern application equipment. With the increasing interest of industry and growers in site-specific pestic... R.K. Meena, S. Virk, C. Byers, G. Rains

89. Application of Advanced Soft Computing to Estimate Potato Tuber Yield: a Case Study from Atlantic Canada

The potato crop plays a crucial role in the economy of Atlantic Canada, particularly in Prince Edward Island and New Brunswick, where it contributes significantly to potato production. To help farmers make informed decisions for sustainable and profitable farming, this study was conducted to examine the variations in potato tuber yield based on thirty soil properties collected over four growing seasons through experimental trials. The study employed an advanced and explainable ensemble model ... Q.U. Zaman, A. Farooque, M. Jamei, T.J. Esau

90. Application of Drone Data to Assess Damage Intensity of Bacterial Leaf Blight Disease on Rice Crop in Indonesia

The Government of Indonesia has launched agricultural insurance program since 2016. A key in agricultural insurance is damage assessment which is required to be as precise, quick, quantitative and inexpensive as possible. Current method is to inspect the damage by human eyes of specialist having experiences. This method, however, costs much and is difficult to estimate disease infected fields precisely in wide area. So, there is increasing need to develop effective, simplified and low cost me... C. Hongo, S. Isono, G. Sigit, B. Utoyo, E. Tamura

91. Application of Geographic Information Systems in Socioeconomic Analysis: A Case of Integrated Soil Fertility Management in the Savannas of Nigeria

Population pressure increases, shortened fallow cycles, cropping intensification, inaccessibility and low output prices as well as concerns about agricultural sustainability and self-sufficiency have combined to contribute to increased demand for integrated soil fertility management of the agricultural resource base. Following this situation, organic fertilizer in the form of animal manure becomes one of the principal sources of nutrients for soil fertility maintenance and crop production. He... O. Olayide, A. Alene, A. Ikpi, G. Nziguheba, T. Alabi

92. Application of Radio Frequency Identification Technology in Agriculture: a Case with Dragon Fruit

Global and local concerns about food safety are turning food traceability into a trade requirement. Typically, a Food Traceability Scheme (FTS) discloses information about food production and its distribution process. A reliable FTS will increase consumer trust in the quality and safety of farm produce. In Malaysia, dragon fruit is a profitable commodity that is growing in export value. Hence, dragon fruit is an excellent candidate for FTS solution development.  ... S.K. Balasundram, M.H. Husni

93. Are Pulses Really More Variable Than Cereals? a Country-wide Analysis of Within-field Variability

In Australia, pulses are underutilised by growers relative to cereal crops. There is significant global interest in growing pulses to provide more plant protein, and they also provide a string of agronomic and environmental benefits, such as their ability to fix nitrogen, and provide a pest and disease break for cereal crops. Many studies attribute this underutilisation to pulses exhibiting greater within-field yield variability than cereals. However, this has never been comprehensively exami... P. Filippi, T. Bishop, D. Al-shammari, T. Mcpherson

94. Asia and Oceania Regional Meeting

... S.K. Balasundram

95. Assess the Feasibility of Remote Sensing Vegetation Index for In-season N Status Evaluation with Nitrogen Measurement from Commercial Field

Nitrogen (N) fertilization plays a crucial role in corn production in the United States. Corn, being a major commodity crop, relies heavily on N fertilization throughout its growth cycle to achieve optimal yields and maintain profitability. During this period of rapid N uptake, it's imperative for farmers to supply sufficient N at the right time to support proper crop development. However, the use of N fertilizer comes with environmental considerations as it can be susceptible to loss thr... A. Nguyen, A. Sharma, R. Prasad

96. Assessing Crop Yield and Profitability with Site-specific Seed Rate Management in Corn and Soybean Cropping Systems

Integrating the information about soil and topographic properties for variable rate seeding is a prerequisite for improved crop production and thus profit. However, limited studies have explored the geospatial and machine learning approaches to understand factors influencing crop yield and profit under site-specific seed rate management. The objectives of this study were to: a) observe the effect of variable seeding rate based on soil and topographic properties on soybean and corn grain ... J. Neupane, N. Joshi, J.P. Fulton, S. Khanal, A. B k, B. Bhattarai

97. Assessing Plant Spacing Inequality and Its Impact on Crop Yield Using Lorenz Curves and Gini Index

Plant spacing is the distance between individual plants in a crop field. It is vital for proper crop establishment as it can influence the spatial and temporal variation in plant emergence. These variations alter how plants interact for light, water, and nutrient resource needs, which, in turn, impact an individual plant's growth conditions and crop yield. Alternatively, studies have associated uniformity in plant spacing with higher yields and increased weed suppression. Modern precision... B. Aryal, A. Sharda, J. Peiretti

98. Assessing Precision Water Management in Cotton Using Unmanned Aerial Systems and Satellite Remote Sensing

The goal of this study was to improve agricultural sustainability and water use efficiency by allocating the right amount of water at the right place and time within the field. The objectives were to assess the effect of variable rate irrigation (VRI) on cotton growth and yield and evaluate the application of satellites and Unmanned aerial systems (UAS) in capturing the spatial and temporal patterns of cotton growth response to irrigation. Irrigation treatments with six replications of three ... O. Adedeji, W. Guo, H. Alwaseela, B. Ghimire, E. Wieber, R. Karn

99. Assessing Soybean Injury from Dicamba Using RGB and CIR Images Acquired on Small UAVs

Dicamba is an herbicide used for postemegence control of several broadleaf weeds in corn, grain sorghum, small grains, and non-cropland. Currently, dicamba-tolerant (DT) soybean and cotton are under development, which provide new options to combat weeds resistant to glyphosate, the most widely used herbicide.  With the use of DT-trait cotton and soybean, off-target dicamba drift onto susceptible crops will become a concern. To relate soybean injury to different rates of dicamba applicati... Y. Huang, H. Brand, D. Pennington, K. Reddy, S.J. Thomson

100. Assessing Soybean Water Stress Patterns and ENSO Occurrence in Southern Brazil: an in Silico Approach

Water stress (WS) is one of the most important abiotic stresses worldwide, responsible for crop yield penalties and impacting food supply. The frequency and intensity of weather stresses are relevant to delimitating agricultural regions. In addition, El Nino Southern Oscillation (ENSO) has been employed to forecast the occurrence of seasonal WS. Lastly, planting date and cultivar maturity selection are key management strategies for boosting soybean (Glycine max (L.) Merr.) y... A. Carcedo, L.F. Antunes de almeida, T. Horbe, G. Corassa, L.P. Pott, I. Ciampitti, G.D. Hintz, T. Hefley, R.A. Schwalbert, V. Prasad

101. Assessing Spray Coverage Variability of an Under-canopy Robotic Sprayer System in Sorghum Crop

An under-canopy robotic sprayer system was developed for site-specific pest management in row crops. However, the effect of nozzle type and spray coverage variability at different points within the plant canopy was unknown. The objective of this study was to quantify the spray coverage at multiple locations within the sorghum crop canopy to determine the effectiveness of such robotic systems. The experiments were conducted in a sorghum field in Ashland, Kansas, using XR8001 flat fan and TXVS6... P. Pokharel, A. Sharda, M. Gadhwal, B. Aryal

102. Assessing the Distribution Uniformity of Broadcast-interseeded Cover Crops at Different Crop Stages by an Unmanned Aerial Vehicle

Drones can now carry larger payloads and have become more affordable, making them a viable option to use for broadcast-interseeding cover crops in the fall, prior to main crop harvest. This strategy has become popular in Ohio over the past two years. However, this new strategy arose quickly with a limited understanding of field performance of the drone’s distribution uniformity under different parameters such as rates, swath widths, speeds, or cash crop type. Therefore, the objective of... A.D. Thomas, J.P. Fulton, S. Khanal, O. Ortez, G. Mcglinch

103. Assessing the Nutritional Status of Field Crops by Remote Sensing During the Growing Season

Plant nutritional status is one of the most important indicators of stand vigour that can be monitored by remote sensing techniques. In this study, we focused on the possibility of assessing crop nutritional status, which was evaluated by plant nitrogen content, using different multispectral Earth remote sensing systems throughout the growing season. Core data were obtained from Sentinel-2 and PlanetScope satellites as well as from an unmanned aerial vehicle (UAV) system, and the data were co... B. Šusliková

104. Assessing the Potential of Sentinel-1 in Retrieving Mango Phenology and Investigating Its Relation to Weather in Southern Ghana

The rise in global production of horticultural tree crops over the past few decades is driving technology-based innovation and research to promote productivity and efficiency. Although mango production is on the rise, application of the remote sensing technology is generally limited and the available study on retrieving mango phenology stages specifically, was focused on the application of optical data. We therefore sought to answer the questions; (1) can key phenology stages of mango be retr... B.A. Torgbor, M.M. Rahman, A. Robson, J. Brinkhoff

105. Assessing the Variability in Cover Crop Growth Due to Management Practices and Biophysical Conditions Using a Mixed Modeling Approach

Planting winter cover crops provides numerous agronomic and environmental benefits. Cereal rye, which is a commonly planted cover crop in Ohio, when established, offers advantages such as recycling residual nitrogen in the soil, enhancing soil organic matter, and reducing nutrient loss. However, understanding cover crop growth is challenging due to field management and weather conditions, and insights using traditional methods are limited. Remote sensing offers a cost-effective and timely alt... K. Kc, S. Khanal, N. Bello, S. Culman

106. Assessing the Variability of Red Stripe Disease in Louisiana Sugarcane Using Precision Agriculture Methods

Symptoms of red stripe disease caused by Acidovorax avenae subsp. avenae in Louisiana between 1985 and 2010 were limited to the leaf stripe form which caused no apparent yield loss.  During 2010, the more severe top rot form was observed, and a study was initiated to investigate the distribution of red stripe in the field and determine its effects on cane and sugar yields. Two fields of cultivar HoCP 00-950, one plant-cane (PC) crop and one first-ratoon (FR) crop, affected by top rot wer... R.M. Johnson, M.P. Grisham

107. Assessment of Active Crop Canopy Sensor As a Tool for Optimal Nitrogen Management in Dryland Winter Wheat

Optimum nitrogen (N) fertilizer application is important for agronomic, economic, and environmental reasons. Among different N management tools, active crop canopy sensors are a recent and promising tool widely evaluated for use in corn but still under-evaluated for use in winter wheat. The objective of this study was to determine whether vegetation indices derived from in-season active crop canopy sensor data can be used to predict winter wheat grain yield and protein content and subsequentl... D. Ghimire

108. Assessment of Goss Wilt Disease Severity Using Machine Learning Techniques Coupled with UAV Imagery

Goss Wilt has become a common disease in corn fields in North Dakota.  It has been one of the most yield-limiting diseases, causing losses of up to 50%. The current method to identify the disease is through visual inspection of the field, which is inefficient, and can be subjective, with misleading results, due to evaluator fatigue. Therefore, developing a reliable, accurate, and automated tool for assessing the severity of Goss's Wilt disease has become a top priority. The use of un... A. Das, P. Flores, Z. Zhang , A. Friskop, J. Mathew

109. Assessment of Soil Spatial Properties and Variability Using a Portable VIS-NIRS Soil Probe for On-farm Precision Experimentation

Assessing the spatial variability of soil properties represents an important issue for on-farm sustainable management owing to high cost of sampling densities. Actual methods of soil properties measurement are based on conventional soil sampling of one sample per ha, followed by laboratory analysis, requiring many soil extraction processes and harmful chemicals. This conventional laboratory analysis does not allow exploring spatial variation of soil properties at desired fine spatial scale. T... A. Cambouris, M. Duchemin, E. Lord, N. Ziadi, B. Javed, J.D. Nze memiaghe, D.A. Ramirez-gonzalez

110. Automated Detection and Length Estimation of Green Asparagus Towards Selective Harvesting

Green asparagus is an important vegetable crop in the United States (U.S.). Harvesting the crop is notoriously labor-intensive, accounting for over 50% of production costs. There is an urgent need to develop harvesting automation technology for the U.S. asparagus industry to remain sustainable and competitive. Despite previous research and developments on mechanical asparagus harvesting, no practically viable products are available because of their low harvest selectivity and significant yiel... J. Xu, Y. Lu

111. Automated Geometrical Field Boundary Delineation Algorithm for Adjacent Job Sites

Establishing farmland geometric boundaries is a critical component of any assistive technology, designed towards the automation of mechanized farming systems. Observing farmland boundaries enables farmers and farm machinery contractors to determine; seed purchase orders, fertiliser application rate, and crop yields. Farmers must supply acreage measurements to regulatory bodies, who will use the geometric data to develop environmental policies and allocate farm subsidies appropriately. Agricu... S.J. Harkin

112. Automated In-field Ornamental Nursery Plant Counting and Quality Assessment with End-to-end Deep Learning for Inventory Management

Efficient inventory management and rigorous quality evaluation play crucial roles for monitoring sales, yield, space utilization, production schedules, and quality enhancements in the ornamental nursery sector. The current method for conducting inventory and quality assessments is through manual plant counting, even when dealing with thousands of plants. The prevailing approach is inefficient, time consuming, labor intensive, potential inaccuracies, and high expenses. Given the continuous dec... H.H. Syed, T. Rehman

113. Automated Lag Phase Detection in Wine Grapes

Crop yield estimation, an important managerial tool for vineyard managers, plays a crucial role in planning pre/post-harvest operations to achieve desired yield and improve efficiency of various field operations. Although various technological approaches have been developed in the past for automated yield estimation in wine grapes, challenges such as cost and complexity of the technology, need of higher technical expertise for their operation and insufficient accuracy have caused major concer... P. Upadhyaya, M. Karkee, X. Zhang, S. Kashetri

114. Automated Pipeline for Research Plot Extraction and Multi-polygon Shapefile Generation for Phenotype and Precision Agriculture Applications

The plant breeding community increasingly adopt remote sensing platforms like unmanned aerial vehicles (UAVs) to collect phenotype data on various crops. These platforms capture high-resolution multi-spectral (MS) image data during extensive field trials, enabling concurrent evaluation of hundreds of plots with diverse seed varieties and management practices. Currently, the plant breeders rely on manual and intricate data extraction, processing, and analysis of high-resolution imagery to draw... A. Sharda, A. Dua, W. Schapaugh, R. Hessel

115. Automated Southern Leaf Blight Severity Grading of Corn Leaves in RGB Field Imagery

Plant stress phenotyping research has progressively addressed approaches for stress quantification. Deep learning techniques provide a means to develop objective and automated methods for identifying abiotic and biotic stress experienced in an uncontrolled environment by plants comparable to the traditional visual assessment conducted by an expert rater. This work demonstrates a computational pipeline capable of estimating the disease severity caused by southern corn leaf blight in images of ... C. Ottley, M. Kudenov, P. Balint-kurti, R. Dean, C. Williams

116. Automated Sow Estrus Detection Using Machine Vision Technology

Successful artificial insemination for gilts and sows relies on accurate timing that is determined by estrus check. Estrus checks in current farms are manually conducted by skilled breeding technicians using the back pressure test (BPT) method that is labor-intensive and inefficient due to the large animal-to-staff ratio. This study aimed to develop a robotic imaging system powered by artificial intelligence technology to automatically detect estrus status for gilts and sows in a stall-housin... J. Zhou, Z. Xu, T.J. Safranski, C. Bromfield

117. Automated Support Tool for Variable Rate Irrigation Prescriptions

Variable rate irrigation (VRI) enables center pivot management to better meet non-uniform water and fertility needs. This is accomplished through correctly matching system water application with spatial and temporal variability within the field. A computer program was modified to accommodate GIS data layers of grid-based field soil texture properties and fertility needs in making management decisions. The program can automatically develop a variable rate application prescription along the lat... A.T. Nguyen, A.L. Thompson, K.A. Sudduth, E.D. Vories, A.T. Nguyen

118. Automatic Body Condition Score Classification System for Individual Beef Cattle Using Computer Vision

Body condition scoring (BCS) is a widely used parameter for assessing the utilization of energy reserves in the fat and muscle of cattle. It fulfills the needs of animal welfare and precision livestock farming by enabling effective monitoring of individual animals. It serves as a crucial parameter for optimizing nutrition, reproductive performance, overall health, and economic outcomes in beef cattle. The precise and consistent assessment of BCS relies on personal experience using visuals tha... M. Islam, J. Yoder, H. Gan

119. Avena: an Event-driven Software Framework for Informed Decisions and Actions in Cropping Systems

Interoperability is one of the enabling factors of real-time communications and data exchange between asynchronous data actors. Interoperability can be attained by introducing events to systems that extract data from consumed ground-truth event streams that utilize application-specific structures. Events are specific occurrences happening at a particular time and place. Event-data are observations of phenomena, or actions, as seen by different systems in Internet of Things (IoT) deployments, ... F.A. Castiblanco rubio, M. Basir, A. Balmos, J. Krogmeier, D. Buckmaster

120. Balancing Water Productivity and Nutrient Use Efficiency: Evaluation of Alternate Wetting and Severe Drying Technology

With emerging water scarcity and rising fertilizer prices, it is crucial to optimize future water use while maintaining yield and nutrient efficiency in irrigated rice. Alternate wetting and moderate drying has proven to be an efficient water-saving irrigation technology for the semi-arid zones of West Africa, reducing water inputs without yield penalty. Alternate wetting and severe drying (AWD30), by re-irrigating fields only when the water table reaches 30 cm below the soil surface, may fur... J. Johnson, M. Becker, J.P. Kaboré, E.R. Dossou-yovo, K. Saito

121. Barriers and Adoption of Precision Ag Tehcnologies for Nitrogen Management Nebraska

A statewide survey of Nebraska farmers shows that they determine the N rate based on soil lab recommendations (82%),  intuition, traditional rate, and own experience (67%). The adoption of dynamic site-specific models (23%), and sensor-based algorithms (11%) remains low. The survey identified the main barriers to the adoption of these N management technologies.  ... G. Balboa, L. Puntel, L. Thompson, P. Paccioretti

122. Bio-Effectors As a Promising Tool for Precision Agriculture and Integrated Plant Nutrition

Bio-effectors, such as microorganisms and active natural compounds, are of increasing interest as promising alternatives or substitutes to precarious agrochemicals. European and global markets (valued at 14.6 billion US$ in 2023) for agricultural biologicals (bio-pesticides, bio-fertilizers, and bio-stimulants) are predicted to grow at rates of more than 13.5 % per year. Improved availability and use efficiency of mineral nutrients, tolerance to abiotic stresses, yield and quality traits, as ... M. Weinmann, M. Nkebiwe, N. Weber, K. Bradacova, N. Morad-talab, U. Ludewig, T. Müller, G. Neumann, M. Raupp, K. Bradacova

123. Biochar Synthesis, Its Impact on Different Soils and Canola Growth

Biochar has been demonstrated as a soil amendment to improve soil health and plant yield. The present study aimed at investigating the potential of wheat straw on canola morphology and yield grown in different soils. The influence of biochar on soil physical and chemical properties was also assessed..Biochar was prepared by pyrolysis of wheat straw in a fixed-bed reactor.  Crushed wheat straw was loaded into the reactor in an N2 environment, and the heating was continued up to... M. Hassan

124. Bird Welfare and Comfort in Poultry Coops Through Computations and AI

Bird welfare and comfort is very important inside poultry coops during transportation, especially during summer and winter months.  The microenvironment inside a poultry coop resulting from hot/cold temperatures, relative humidity and heat production leads to complex scenarios affecting the bird welfare. The enthalpy comfort index (ECI) that relates to temperature, relative humidity was calculated to evaluate the poultry coop welfare that corresponds to bird welfare conditions (comfort; ... R. Pidaparti, A. Moghadham, H. Thippareddi

125. Botanix Explorer (BX1): Precision Plant Phenotyping Robot Detecting Stomatal Openings for Precision Irrigation and Drought Tolerance Experiments

Under drought conditions, the kidney-shaped organs on the epidermal surface of plants, called stomata, are crucial to plant health. During transpiration, the stomata, which resemble pores, open and close. When the rate of photosynthesis is balanced, plants can withstand droughts by decreasing their stomatal transpiration. Drought-stressed plants are characterized by a higher number of open stomata. Measuring the pore aperture ratio is essential for precisely quantifying the degree of stomatal... S. Gummi, J.O. Kemeshi, Y. Chang

126. Can AI and Automation Transform Specialty Crop Production?

... Y. Ampatzidis

127. Can Soil Fertility Data and Topography Predict Yield Stability Zones for Corn Fields in New York?

Yield monitor systems play a vital role in precision agriculture given their ability to capture and map within-field yield variability. When three or more years of yield data are available, yield stability zone maps can be generated to show both the spatial and temporal variability of yield within a field. Based on the farm’s overall temporal mean and standard deviation for a specific crop, we can classify areas in the field as consistently high- (Q1) or low-yielding (Q4), and variably ... M. Marcaida, X. Zhang, S. Srinivasagan, S. Shajahan, Q. Ketterings

128. Can Topographic Indices Be Used for Irrigation Management Zone Delineation

Soil water movement is affected by soil physical properties and field terrain changes. The identification of within-field areas prone to excess or deficit of soil moisture could support the implementation of variable rate irrigation and adoption of irrigation scheduling strategies. This study evaluated the use of the topographic wetness index (TWI) and topographic position index (TPI) to understand and explain within-field soil moisture variability. Volumetric water content (VWC) collected in... B.V. Ortiz, B.P. Lena, F. morlin , G. Morata, M. Duarte de val, R. Prasad, A. Gamble

129. Capacity Building of African Young Scientists in Precision Agriculture Through Cross-regional Academic Mobility for Enhanced Climate-smart Agri-food System: an Intra Africa Mobility Project on Precision Agriculture

Climate change is one of the main problems affecting food and nutrition globally, particularly in sub-Saharan Africa. Adapting to and/or mitigating climate change in the agri-food sector requires merging information technologies, genetic innovations, and sustainable farming practices to empower the agricultural youth sector to create effective and locally adapted solutions. Precision Agriculture applied to crops (PAAC), has been advocated as a strategic solution to mitigate/adapt agriculture ... N. Fassinou hotegni, A. Karangwa, A. Manyatsi, K.A. Frimpong, M. Amri, D. Cammarano, C. Lesueur, J. Taylor, S. Phillips, E. Achigan-dako

130. Challenges and Successes when Generating In-season Multi-temporal Calibrated Aerial Imagery

Digital aerial imagery (DAI) of the crop canopy collected by aircraft and unmanned aerial vehicles is the yardstick of precision agriculture.  However, the quantitative use of this imagery is often limited by its variable characteristics, low quality, and lack of radiometric calibration.  To increase the quality and utility of using DAI in crop management, it is important to evaluate and address these limitations of DAI.  Even though there have been improvements in spatial reso... P.M. Kyveryga, J. Pritsolas, J. Connor, R. Pearson

131. Changes in Soil Chemical and Physical Properties After a Flooding Event in Chile

During the winter of 2023, ridges were made to plant French prunes (Prunus domestica). After building the ridges, the soil was surveyed using gamma radiation technology (SoilOptix technologies, Ontario, CA).  Due to the intense rains that occurred at the end of august 2023, the Cachapoal River, the main water supply of the O’Higgins region, left its course and flooded several fields, including the one where the ridges had been built, destroying them. Ridges were washed out... R.A. Ortega, H.P. Poblete

132. Changes in Soil Quality when Building Ridges for Fruit Plantation

Many fruit plantations are usually performed in ridges for various reasons including, escaping from a clay horizon, improving overall soil quality and drainage, among others. Normally ridges are built using the surface horizons, producing a mixture of soils layers, and therefore changing the quality of the soil at the rooting zone. We were interested in studying the changes in soil properties when building ridges in a flat alluvial soil that was planted with avocado. A det... H.P. Poblete, R.A. Ortega

133. Cherry Yield Forecast: Harvest Prediction for Individual Sweet Cherry Trees

Digitalization continues to transform the agricultural sector as a whole and also affects specific niches like horticulture. Particularly in fruit and wine production, the focus is on the application of sensor systems and data analysis aiming at automated detection of drought stress or pests in vineyards or orchards.  As part of the  “For5G” project, we are developing an end-to-end methodology for the creation of digital twins of fruit trees, with a strong focu... A. Gilson, L. Meyer, A. Killer, F. Keil, O. Scholz, D. Kittemann, P. Noack, P. Pietrzyk, C. Paglia

134. Citizens Perspectives on Robot-based Crop Farming – a Cluster Analysis Using Unsupervised Machine Learning

Artificial intelligence (AI) and its possibilities and threats are prominently discussed by the broader public. Robotic solutions are based on AI and offer the potential to change agricultural production drastically. However, new food technologies have not been perceived solely positively by society in the past. Genetic engineering, for example, has been the subject of repeated controversy. Science communication theory suggests that individual opinion leaders highly influence steering a socia... H. Zeddies, G. Busch

135. Claypan Depth Effect on Soil Phosphorus and Potassium Dynamics

Understanding the effects of fertilizer addition and crop removal on long-term change in spatially-variable soil test P (STP) and soil test K (STK) is crucial for maximizing the use of grower inputs on claypan soils. Using apparent electrical conductivity (ECa) to estimate topsoil depth (or depth to claypan, DTC) within fields could help capture the variability and guide site-specific applications of P and K. The objective of this study was to determine if DTC derived from ECa... L. Conway, M. Yost, N. Kitchen, K. Sudduth, B. Myers

136. Climate Sensitivity Analysis on Maize Yield on the Basis of Precision Crop Production

In this paper by prediction we have defined maize yield in precision plant production technologies according to five different climate change scenarios (Ensembles Project) until 2100 and in one scenario until 2075 using DSSAT v. 4.5.0. CERES-Maize decision support model. Sensitivity analyses were carried out. The novelty of the method presented here is that precision, variable rate technologies from relatively small areas (in our case 2500 m2) enable a large amount of data to be co... A. Nyeki, G. Milics, A.J. Kovacs, M. Neményi, J. Kalmar

137. Climate Smart Precision Nitrogen Management

Climate Smart Agriculture (CSA) aims at improving farm productivity and profitability in a sustainable way while building resilience to climate change and mitigating the impacts of agriculture on greenhouse gas emissions. The idea behind this concept is that informed management decision can help achieve these goals. In that matter, Precision Agriculture goes hand-in-hand with CSA. The Colorado State University Laboratory of Precision Agriculture (CSU-PA) is conducting research on CSA practice... L. Longchamps, R. Khosla, R. Reich

138. Closing Yield Gaps with GxExM and Precision Agriculture

There are many challenges to be faced by agriculture if the global population of nine billion people projected for 2050 is to be fed and clothed, especially given the effects of changing climate.  A focus on the interactions of genetics x environment x management (GxExM) offers potential for meeting the yield, and environment and economic sustainability goals that are integral to these challenges.  The yield gap –defined as the difference between current farmer yields and pote... C. Walthall, J. Hatfield, S. Schneider, M. Vigil

139. Cloud Correction of Sentinel-2 NDVI Using S2cloudless Package

Optical satellite-derived Normalized Difference Vegetation Index (NDVI) is by far the most commonly used vegetation index value for crop monitoring. However, it is quite sensitive to the cloud, and cloud shadows and significantly decreases its usability, especially in agricultural applications. Therefore, an accurate and reliable cloud correction method is mandatory for its effective application. To address this issue, we have developed an approach to correct the NDVI values of each and every... A. Saxena, M. Dash, A.P. Verma

140. Combining Remote Sensing and Machine Learning to Estimate Peanut Photosynthetic Parameters

The environmental conditions in which plants are situated lead to changes in their photosynthetic rate. This alteration can be visualized by pigments (Chlorophyll and Carotenoids), causing changes in plant reflectance. The goal of this study was to evaluate the performance of different Machine Learning (ML) algorithms in estimating fluorescence and foliar pigments in irrigated and rainfed peanut production fields. The experiment was conducted in the southeast of Georgia in the United States i... C. Rossi, S.L. Almeida, M.N. Sysskind, L.A. Moreno, A. Felipe dos santos, L. Lacerda, G. Vellidis, C. Pilcon, T. Orlando costa barboza

141. Comparative Analysis of Different On-the-go Soil Sensor Systems

This study is part of the field of precision agriculture. This management mode is one of the great revolutions in the agriculture field, and it means better management of farm inputs such as fertilizers, herbicides, and seeds by applying the right amount at the right place and at the right time. To succeed in this, we should dispose of a tool that allows a precise assessment of the soil’s physical state. Thus, on-the-go soil sensors can be used as a creative tool to gain bette... H. Moulay, B. Arnall, S. Phillips

142. Comparative Analysis of Light-weight Deep Learning Architectures for Soybean Yield Estimation Based on Pod Count from Proximal Sensing Data for Mobile and Embedded Vision Applications

Crop yield prediction is an important aspect of farming and food-production. Therefore, estimating yield is important for crop breeders, seed-companies, and farmers to make informed real-time financial decisions. In-field soybean (Glycine max L.(Merr.)) yield estimation can be of great value to plant breeders as they screen thousands of plots to identify better yielding genotypes that ultimately will strengthen national food security. Existing soybean yield estimation too... J.J. Mathew, P.J. Flores, J. Stenger, C. Miranda, Z. Zhang, A.K. Das

143. Comparative Analysis of Spray Nozzles on Drones: Volumetric Distribution at Different Heights

Agricultural drones are emerging as a revolutionary tool in modern agriculture, aiming to enhance precision and efficiency in crop management. One of their main advantages is the ability to operate in adverse soil and canopy height conditions, making them a valuable instrument for the application of agrochemicals. In this context, the optimization of spraying systems plays a critical role, with the goal of ensuring the effective application of agrochemicals, aiming to maximize productivity an... A. Felipe dos santos, J.E. Silva, O.P. Costa, F.D. Inácio , R. Oliveira, W. Silva, L. Lacerda, T. Orlando costa barboza

144. Comparative Benefits of Drone Imagery for Nitrogen Status Determination in Corn

Remotely sensed vegetation data provide an effective means of measuring the spatial variability of nitrogen and therefore of managing applications by taking intrafield variations into account. Satellites, drones and sensors mounted on agricultural machinery are all technologies that can be used for this purpose. Although a drone (or unmanned aerial vehicle [UAV]) can produce very high-resolution images, the comparative advantages of this type of imagery have not been demonstrated. The goal of... N. Tremblay, K. Khun, P. Vigneault, M.Y. Bouroubi, F. Cavayas, C. Codjia

145. Comparing Adapt-N to Static N Recommendation Approaches for US Maize Production

Large temporal and spatial variability in soil N availability leads many farmers across the US to over apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses.  Static N recommendation tools are typically promoted in the US, but new dynamic model-based tools allow for more precise and adaptive N recommendations that account for specific production environments and conditions. This study compares two static N recommendation tools... H. Van es, S. Sela, R. Marjerison, B. Moebiu-clune, R. Schindelbeck, D. Moebius-clune

146. Comparing Global Shutter and Rolling Shutter Cameras for Image Data Collection in Motion on a UGV

In a bid to drive the adoption of precision farming (PF) technology by reducing the cost of developing an Unmanned Ground Vehicle (UGV), during the Reduction-To-Below-Two grand (R2B2) project we compared Arducam’s AR0234, a global shutter camera (GSC) to their IMX462, a rolling shutter camera (RSC). Since the cost of the AR0234 is approximately three times the price of the IMX462, the comparison was done to determine the possibility of using the latter for image data collection in place... J.O. Kemeshi, Y. Chang, P.K. Yadav, M. Alahe

147. Comparing Hyperspectral and Thermal UAV-borne Imagery for Relative Water Content Estimation in Field-grown Sesame

Sesame (Sesamum indicum) is an irrigated oilseed crop, and studies on its water content estimation are sparred. Unmanned aerial vehicle (UAV)-borne imageries using spectral reflectance as well as thermal emittance for crops are an ample source of high throughput information about their physiological and chemical traits. Though several studies have dealt with thermal emittance to assess the crop water content, evaluating its relation to the plant’s solar reflectance is limi... M. Sahoo, R. Tarshish, V. Alchanatis , I. Herrmann

148. Comparing Predictive Performance of Near Infrared Spectroscopy at a Field, Regional, National and Continental Scales by Using Spiking and Data Mining Techniques

The development of accurate visible and near infrared (vis-NIR) spectroscopy calibration models for selected soil properties is a crucial step for variable rate application in precision agriculture. The objective of the present study was to compare the prediction performance of vis-NIR spectroscopy at local, regional, national and continental scales using data mining techniques including spiking. Fresh soil samples collected from farms in the UK, Czech Republic, Germany, Denmark and the Nethe... S.M. Nawar, A.M. Mouazen, D. George, A. Manfield

149. Comparing Profitability of Variable Rate Nitrogen Prescription Methods

Variable rate nitrogen (VRN) prescriptions have been field-tested against uniform N application for over 25 years.  VRN prescription algorithms vary in the type and cost of information they require.  To date, few studies have compared the benefits and costs of alternative VRN prescription methods. VRN prescriptions draw on diverse information, including soil and tissue N sampling, yield history (YH), and remotely sensed spectral reflectance (such as the Normalized Differen... S. Lee, S.M. Swinton

150. Comparing Proximal and Remote Sensors for Variable Rate Nitrogen Management in Cotton

Sensing and variable rate technology are becoming increasingly important in precision agriculture. These technologies utilize sensors to monitor crop growth and health, enabling informed decisions such as diagnosing nitrogen (N) stress and applying variable rates of N. Sensor-based solutions allow for customized N applications based on plant needs and environmental factors. This approach has led to notable reductions in N application rates, minimized N losses by improving N use efficiency (NU... A. Bhattarai, A. Jakhar, L. Bastos, G.J. Scarpin

151. Comparison and Validation of Different Soil Survey Techniques to Support a Precision Agricultural System

The data need of precision agriculture has resulted in an intensive increase in the number of modern soil survey equipment and methods available for farmers and consultants. In many cases these survey methods cannot provide accurate information under the used environmental conditions. On a 36 hectare experimental field, several methods have been compared to identify the ones which can support the PA system the best. The methods included contact and non contact soil scanning, yield mapping, hi... V. Lang, G. Tóth, S. Csenki, D. Dafnaki

152. Comparison Between High Resolution Spectral Indices and SPAD Meter Estimates of Nitrogen Deficiency in Corn

Low altitude remote sensing provides an ideal platform for monitoring time sensitive nitrogen status in crops. Research is needed however to understand the interaction between crop growth stage, spatial resolution and spectral indices derived from low altitude remote sensing. A TetraCam camera equipped with six bands including the red edge and near infrared (NIR) was used to investigate corn nitrogen dynamics. Remote sensing data were collected during the 2013 and 2014 growing seasons at four... D. Mulla, A. Laacouri, D. Kaiser

153. Comparison Between Tractor-based and UAV-based Spectrometer Measurements in Winter Wheat

In-season variable rate nitrogen fertilizer application needs a fast and efficient determination of nitrogen status in crops. Common sensor-based monitoring of nitrogen status mainly relies on tractor mounted active or passive sensors. Over the last few years, researchers tested different sensors and indicated the potential of in-season monitoring of nitrogen status by unmanned aerial vehicles (UAVs) in various crops. However, the UAV-platforms and the available sensors are not yet accepted t... M. Gnyp, M. Panitzki, S. Reusch, J. Jasper, A. Bolten, G. Bareth

154. Comparison of Canopy Extraction Methods from UAV Thermal Images for Temperature Mapping: a Case Study from a Peach Orchard

Canopy extraction using thermal images significantly affects temperature mapping and crop water status estimation. This study aimed to compare several canopy extraction methodologies by utilizing a large database of UAV thermal images from a precision irrigation trial in a peach orchard. Canopy extraction using thermal images can be attained by purely statistical analysis (S), a combination of statistical and spatial analyses (SS), or by synchronizing thermal and RGB images, following RGB sta... L. Katz, A. Ben-gal, I. Litaor, A. Naor, A. Peeters, E. Goldshtein, V. Alchanatis, Y. Cohen

155. Comparison of Different Aspatial and Spatial Indicators to Assess Performance of Spatialized Crop Models at Different Within-field Scales

Most current crop models are point-based models, i.e. they simulate agronomic variables on a spatial footprint on which they were initially designed (e.g. plant, field, region scale). To assess their performances, many indicators based on the comparison of estimated vs observed data, can be used such as root mean square error (RMSE) or Willmott index of agreement (D-index) among others. However, shifting model use from a strategic objective to tactical in-season management is becoming a signi... D. Pasquel, S. Roux, B. Tisseyre, J.A. Taylor

156. Comparison of NDVI Values at Different Phenological Stages of Winter Wheat (Triticum Aestivum L.)

The main objective of this study is to monitor, detect and quantify the presence of live green vegetation with the MicaSense RedEdge-MX Dual Camera System (MS) mounted on a DJI Matrice 210 V2 and GreenSeeker HCS 250 (GS) in winter wheat (Triticum aestivum L.) by using Normalized Difference Vegetation Index (NDVI). Surveys were conducted in the North-Western part of Hungary, in Mosonmagyaróvár on six different dates. A small-scale field trial in winter wheat was constructed as a ... S. Zsebő, G. Kukorelli, V. Vona, L. Bede, D. Stencinger, A. Kovacs, G. Milics, I.M. Kulmany, B. Horváth, G. Hegedűs, J.A. Abdinoor

157. Comparison of Plant and Soil Mapping in Prunus Domestica L. Orchard

In the present study, the soil apparent electrical conductivity, ECa, and the plant water status were analyzed in plum production (Prunus domestica L 'Tophit plus'/Wavit) targeting (i) the spatial characterization of soil ECa and fruit yield, (ii) instantaneous water status, and (iii) cumulative pattern of water status and yield. The plum orchard is located in semi-humid, temperate climate (Potsdam, Germany), capturing 0.37 ha with 156 trees. Measurements were carried out on... M. Zude-sasse, J. Käthner, W.B. Herppich, J. Selbeck

158. Computer Vision by UAVs for Estimate Soybean Population Across Different Physiological Growth Stages and Sowing Speeds

Soybean (Glycine max (Linnaeus) Merrill) production in the United States plays a crucial role in agriculture, occupying a considerable amount of cultivated land. However, the costs associated with soybean production have shown a notable increase in recent years, with seed-related expenses accounting for a significant proportion of the total. This increase in costs is attributed to a number of factors, including the introduction of patented and protected genetic traits, as well as inflationary... F. Pereira de souza, L. Shiratsuchi, H. Tao, M. Acconcia dias, M. Barbosa, T. Deri setiyono, S. campos

159. Consequences of Spatial Variability in the Field on the Uniformity of Seed Quality in Barley Seed Crops

Spatial variation is known to affect cereal growth and yield but consequences for seed quality are less well-known. Intra-field spatial variation occurs in soil and environmental variables and these are expected to affect the crop. The objective of this paper was to identify the spatial variation in barley seed quality and to investigate its association with environmental factors and the spatial scale over which this correlation occurs. Two uniformly-managed, commercial fields of wi... S. Hama rash, A.J. Murdoch

160. Considering Farmers' Situated Expertise in AgriDSS Development to Fostering Sustainable Farming Practices in Precision Agriculture

Agriculture is facing immense challenges and sustainable intensification has been presented as a way forward where precision agriculture (PA) plays an important role. More sustainable agriculture needs farmers who embrace situated expertise and can handle changing farming systems. Many agricultural decision support systems (AgriDSS) have been developed to support farm management, but the traditional approach to AgriDSS development is mostly based on knowledge transfer. This has resulted in te... C. Lundström, J. Lindblom

161. Constraint of Data Availability on the Predictive Ability of Crop Response Models Developed from On-farm Experimentation

Due to the variability between fields and across years, on-farm experimentation combined with crop response modeling are crucial aspects of decision support systems to make accurate predictions of yield and grain protein content in upcoming years for a given field. To maximize accuracy of models, models fit using environmental covariate and experimental data gathered up to the point that crop responses (yield/grain protein) are fit repeatedly over time until the model can predict future crop ... P. Hegedus, B. Maxwell

162. Content Analysis of the Challenges of Using Drones in Paddy Fields in the Haraz Plain Watershed, Iran

Drone technology has gained popularity in recent years as a sustainable solution to changing agricultural conditions. Using drones in agriculture provides many advantages in farm management. However, the use of drones in paddy fields in Iran is a new phenomenon facing numerous challenges. This study aims to explore the challenges for using drones in paddy fields and provide practical guidelines to solve the challenges facing the their application. This research was conducted with a qualitativ... J. Aliloo, E. Abbasi, E. Karamidehkordi , E. Ghanbari parmehr, M. Canavari, G.-. Vitali

163. Controller Performance Criteria for Sensor Based Variable Rate Application

Sensor based variable rate application of crop inputs provides unique challenges for traditional rate controllers when compared to map based applications. The controller set point is typically changing every second whereas with a map based systems the set point changes much less frequently. As applied data files for a sensor based variable rate nitrogen applicator were obtained from a wheat field in north central Oklahoma. These data were analyzed to determine the magnitude and frequency of r... R.K. Taylor, P. Bennur, J.B. Solie, N. Wang, P. Weckler, W.R. Raun

164. Cotton Boll Detection and Yield Estimation Using UAS Lidar Data and RGB Image

Cotton boll distribution is a critical phenotypic trait that represents the plant's response to its environment. Accurate quantification of boll distribution provides valuable information for breeding cultivars with high yield and fiber quality. Manual methods for boll mapping are time-consuming and labor-intensive. We evaluated the application of Lidar point cloud and RGB image data in boll detection and distribution and yield estimation. Lidar data was acquired at 15 m using a DJI Matri... Z. Lin, W. Guo, N. Gill

165. Cotton Yield Estimation Using High-resolution Satellite Imagery Obtained from Planet SkySat

Satellite images have been used to monitor and estimate crop yield. Over the years, significant improvements on spatial resolution have been made where ortho images can be generated at 30-centimeter resolution. In this study, we wanted to explore the potential use of Planet SKYSAT satellite system for cotton yield predictions. This system provided imagery data at 50 centimeters resolution, and we collected data 14 times during the season. The data were collected from two different cotton... M. Bhandari

166. Coupling Machine Learning Algorithms and GIS for Crop Yield Predictions Based on Remote Sensing Imagery and Topographic Indices

In-season yield prediction can support crop management decisions helping farmers achieve their yield goals. The use of remote sensing to predict yield it is an alternative for non-destructive yield assessment but coupling auxiliary data such as topography features could help increase the accuracy of yield estimation. Predictive algorithms that can effectively identify, process and predict yield at field scale base on remote sensing and topography still needed. Machine learning could be an alt... M.F. Oliveira, G.T. Morata, B. Ortiz, R.P. Silva, A. Jimenez

167. Coupling Macro-scale Variability in Soil and Micro-scale Variability in Crop Canopy for Delineation of Site-specific Management Grid

The efficient application of fertilizers via Site-Specific Management Units (SSMUs) or Management Zones (MZs) can significantly enhance crop productivity and nitrogen use efficiency. Conventional mathematical and data-driven clustering methods for MZ delineation, while prevalent, often lack precision in identifying productivity zones. This research introduces a knowledge-driven productivity zone to mitigate these limitations, offering a more precise and efficacious approach. The hyp... W.A. Admasu, D. Mandal, R. Khosla

168. Creating a Comprehensive Software Framework for Sensor-driven Precision Agriculture

Robots and GPS-guided tractors are the backbone of smart farming and precision agriculture. Many companies and vendors contribute to the market, each offering their own customized solutions for common tasks. These developments are often based on vendor-specific, proprietary components, protocols and software. Many small companies that produce sensors, actuators or software for niche applications could contribute their expertise to the global efforts of creating smart farming solutions, if the... O. Scholz, F. Uhrmann, M. Weule, T. Meyer, A. Gilson, J. Makarov, J. Hansen, T. Henties

169. Creating Prescription Maps from Historical Imagery for Site-specific Management of Cotton Root Rot

Cotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is a severe plant disease that has affected cotton production for over a century. Recent research found that a commercial fungicide, Topguard (flutriafol), was able to control this disease. As a result, Topguard Terra Fungicide, a new and more concentrated formulation developed specifically for this market was registered in 2015, so cotton producers can use this product to control the disease. Cotton root rot only inf... C. Yang, G.N. Odvody, J.A. Thomasson, T. Isakeit, R.L. Nichols

170. Creating Value from On-farm Research: Efields Data Workflow and Management Successes and Challenges

Farm operations today generate a large amount of data that can be difficult to properly manage. This challenge is further compounded when conducting on-farm research. The Ohio State University eFields program partners with farmers to conduct on-farm research and share results in a timely manner. Since 2017, the team has conducted and shared 987 trials across Ohio with the annual number of trials increasing from 45 to 292. This rapid increase has required development of a data workflow that st... J.P. Fulton, D. Wilson, R. Tietje, E. Hawkins

171. Crop and Water Monitoring Networks with Low-cost, Internet of Things Technology

Making meaningful changes in agroecosystems often requires the ability to monitor many environmental parameters to accurately identify potential areas for improvement in water quality and crop production. Increasingly, research questions are requiring larger and larger monitoring networks to draw applicable insights for both researchers and producers. However, acquiring enough sensors to address a particular research question is often cost-prohibitive, making it harder to draw meaningful conc... A.J. Brown, E. Deleon, E. Wardle

172. Crop Modeling-based Framework to Explore Region-specific Impact of Nitrogen Fertilizer Management on Productivity and Environmental Footprint

To maintain current crop production while reducing negative environmental impacts, improved understanding of the relative impact of the 4Rs for nitrogen (N) management (rate, time, place, and source) for a given geo-agroecosystem are needed and can play a critical role in driving policy, recommendations, and local practices. However, the timeframe and cost required to assess and characterize the impact of N rate and timing over years and weather conditions through field experiments is prohibi... L. Thompson, S. Archontoulis, P. Grassini, L. Puntel, T. Mieno

173. Crop Water Stress Mapping for Site Specific Irrigation by Thermal Imagery and Artificial Reference Surfaces

Variable rate irrigation machines or solid set systems have become technically feasible; however, crop water status mapping is necessary as a blueprint to match irrigation quantities to site-specific crop water demands. Remote thermal sensing can provide these maps in sufficient detail and at a timely delivery. In a set of aerial and ground scans at the Hula Valley, Israel, digital crop water stress maps were generated using geo-referenced high- resolution thermal imagery and artificial refer... M. Meron, J. Tsipris, V. Orlov, V. Alchnatis, Y. Cohen

174. CropSAT - a Public Satellite-based Decision Support System for Variable-rate Nitrogen Fertilization in Scandinavia

CropSAT is a free-to-use web application for satellite-based production of variable-rate application (VRA) files of e.g. nitrogen (N) and fungicides currently available in Sweden and Denmark. Even in areas frequently covered by clouds, vegetation index maps from data derived from low-cost or freely available optical satellites can be used in practice as a cost-efficient tool in time-critical applications such as optimized nitrogen use. During the very cloudy year 2015, or more useable ima... M. Söderström, H. Stadig, J. Martinsson, M. Stenberg, K. Piikki

175. Cultivating Future Leaders in Sustainable Agriculture: Insights from the Digital Agriculture Fellowship Program at the University of California, Riverside

Funded by USDA's National Institute of Food and Agriculture’s Sustainable Agricultural Systems Program and housed at the University of California, Riverside (UCR), the Digital Agriculture Fellowship (DAF) aims at equipping undergraduate students with the knowledge and experience necessary to meet the agricultural challenges posed by climate change and sustainability concerns. The program was established in 2020 and will be funded through 2026. Activities span over fifteen months for... E. Scudiero, C.I. Nugent, C. Ng, N. Jones, T. Azzam, N.G. Salunga, S. Lemus

176. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and Vision

Advancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor.  While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collec... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi

177. Data Gator: a Provisionless Network Solution for Collecting Data from Wired and Wireless Sensors

Advances in wireless sensor technology and data collection in precision agriculture enable farmers and researchers to understand operational and environmental dynamics. These advances allow the tracking of water usage, temperature variation, soil pH, humidity, sunlight penetration, and other factors which are crucial for trend prediction and analysis. Capitalizing on this advancement, however, requires data collection infrastructure using large and varied sensor networks. Adoption and impleme... G. Wells, J. Shovic, M. Everett

178. Data Normalization Methods for Definition of Management Zones

The use of management zones is considered a viable economic alternative for the management of crops due to low cost of adoption as well as economic and environmental benefits. The decision whether or not to normalize the attributes before the grouping process (independent of use) is a problem of methodology, because the attributes have different metric size units, and may influence the result of the clustering process. Thus, the aim of this study was to use a Fuzzy C-Means algorithm to evalua... K. Schenatto, E.G. De souza, C.L. Bazzi, A. Gavioli, N.M. Betzek, H.M. Beneduzzi

179. Data Sources and Risk Management in Precision Agriculture

The digitalisation of the agricultural economy provides more data about the biological processes and technological solutions used for producing agricultural products than ever before. Paralell to the data collection – aiming to provide information for agricultural decision-making and operations – the data informs the farmers, public administration officers and other players in agriculture about the state of the environment. The strategic planning on operation of farms and data han... G. Milics, P.M. Varga, F. Magyar, I. Balla

180. Data-driven Agriculture and Sustainable Farming: Friends or Foes?

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive.  It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience.  Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about syne... O. Rozenstein, Y. Cohen, V. Alchanatis , K. Behrendt, D.J. Bonfil, G. Eshel, A. Harari, W.E. Harris, I. Klapp, Y. Laor, R. Linker, T. Paz-kagan, S. Peets, M.S. Rutter, Y. Salzer, J. Lowenberg-deboer

181. Decision Making Factors of Precision Agricultural Practices in South Dakota

A survey among South Dakota Farmers was conducted to document current nutrient management practices. The survey included questions regarding adoption and use of precision ag technologies in addition to information considered to create prescription maps for variable fertilizer and seeding rates. The survey collected demographic information from the producers. The presentation will also highlight how farm size, farm location, farmer/decision maker’s age and/or education level in... P. Kovacs, J. Clark, J. Schad, E. Avemegah

182. Decision Support from On-field Precision Experiments

Empirically driven adaptive management in large-scale commodity crop production has become possible with spatially controlled application and sub-field scale crop monitoring technology. Site-specific experimentation is fundamental to an agroecosystem adaptive management (AAM) framework that results in information for growers to make informed decisions about their practices. Crop production and quality response data from combine harvester mounted sensors and internet available remote sensing d... B.D. Maxwell, P.D. Hegedus, S.D. Loewen, H.D. Duff, J.W. Sheppard, A.D. Peerlinck, G.L. Morales, A. Bekkerman

183. Decision Support Tools for Developing Aflatoxin Risk Maps in Peanut Fields

Aspergillus flavus and Aspergillus parasiticus hereafter referred to jointly as A. flavus, are soil fungi that infect and contaminate preharvest and postharvest peanuts with the carcinogenic secondary metabolite aflatoxin. A. flavus can cause extensive economic losses to peanut growers and shellers by contaminating peanut kernels with aflatoxins. In the southeastern U.S., contamination from aflatoxin continues to be a major threat to the peanut industry and... G. Vellidis, M. Abney, T. Burlai, J. Fountain, R.C. Kemerait, S. Kukal, L. Lacerda, S. Maktabi, A. Peduzzi, C. Pilcon, M. Sysskind

184. Deep Learning for Predicting Yield Temporal Stability from Short Crop Rotations

Investigating the temporal stability of yield in management zones is crucial for both producers and researchers, as it helps in mitigating the adverse impacts of unpredictable disruptions and weather events. The diversification of cropping systems is an approach which leads to reduced variability in yield while improving overall field resilience. In this six-year study spanning from 2016 to 2021, we monitored 40 distinct fields owned by 10 producers situated in Quebec, Canada. These... E. Lord, A.A. Boatswain jacques, A.B. Diallo, M. Khakbazan, A. Cambouris

185. Deep Learning to Estimate Sorghum Yield with Uncrewed Aerial System Imagery

In the face of growing demand for food, feed, and fuel, plant breeders are challenged to accelerate yield potential through quick and efficient cultivar development. Plant breeders often conduct large-scale trials in multiple locations and years to address these goals. Sorghum breeding, integral to these efforts, requires early, accurate, and scalable harvestable yield predictions, traditionally possible only after harvest, which is time-consuming and laborious. This research harnesses high-t... M.A. Bari, A. Bakshi, T. Witt, D. Caragea, K. Jagadish, T. Felderhoff

186. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS Imagery

Deep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high re... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal

187. Delineating Dynamic Variable Rate Irrigation Management Zones

Agriculture irrigation strategies have traditionally been made without accounting for the natural small-scale variability in the field, leading to uniform applications that often over-irrigate parts of the field that do not need as much water. The future success of irrigated agriculture depends on advancements in the capability to account for and leverage the natural variability in croplands for optimum irrigation management both in space and time. Variable Rate Irrigation (VRI) management of... R. Unruh, W.A. Yilma, D. Mandal, R. Joshi, R. Khosla

188. Delineating Management Zones for Optimizing Soil Phosphorus Recommendations Under a No Till Field in Eastern Canada

Corn (Zea mays L.) and soybean (Glycine max L.) represent the most common crop rotation in Eastern Canada. These crops are cultivated using no-tillage (NT) practice to enhance agroecosystem sustainability. However, NT practice can cause several agri-environmental issues related to phosphorus (P) stratification, movement and runoff leading to P eutrophication in waters. Another major challenge is the expensive costs of extensive soil sampling and laboratory tests needed for a... J. Nze memiaghe, A. Cambouris, M. Duchemin, N. Ziadi, A. Karam

189. Delineation of Site-Specific Management Zones using Sensor-based Data for Precision N management

Nitrogen is a critical nutrient influencing crop yield, but the common practice of uniform application of nitrogen fertilizer across a field often results in spatially variable nitrogen availability for the crop, leading to over-application in some areas and under-application in others. This imbalance can cause economic losses and significant environmental issues. Precision nitrogen application involves application of N fertilizers based on soil conditions and crop requirements. One approach ... R. Joshi, R. Khosla, D. Mandal, R. Unruh, W.A. Admasu

190. Delineation of Site-specific Management Zones Using Spatial Principal Components and Cluster Analysis

The delineation of site-specific management zones (MZs) can enable economic use of precision agriculture for more producers. In this process, many variables, including chemical and physical (besides yield data) variables, can be used. After selecting variables, a cluster algorithm like fuzzy c-means is usually applied to define the classes. Selection of variables comprise a difficult issue in cluster analysis because these will often influence cluster determination. The goal of this study was... A. Gavioli, E.G. Souza, C.L. Bazzi, N.M. Betzek, K. Schenatto, H. Beneduzzi

191. Delineation of Site-specific Management Zones with Proximal Data and Multi-spectral Imagery

Many findings suggested that it’s possible to improve the accuracy of delineating site-specific management zones (SSMZs) through a combination of proximal data with remote sensing imagery. The objective of this study is to assess the feasibility of delineating SSMZs with a wide range of ancillary data (proximal survey and multi-spectral data). The study area is a 22.1acre located 10 miles north of Fort Collins, CO and is known for having a high spatial and temporal variability of soil p... W.A. Yilma, J. Siegfried, R. Khosla

192. Delineation of Yield Zones Using Optical and Radar Remote Sensing

Identifying yield zones in agricultural areas is essential for efficient resource allocation, operational optimization, and decision-making. While optical remote sensing is widely used in precision agriculture, the interest in radar remote sensing data, notably from the Sentinel-1 Synthetic Aperture Radar (SAR), has increased due to its operation in the C-band frequency, capturing data through cloud cover and the availability of free data. The main objective of this study was to evaluate ... I.A. Da cunha, H. Oldoni, D.D. Melo, L.R. Amaral

193. Deposition Characteristics of Different Style Spray Tips at Varying Speeds and Altitudes from an Unmanned Aerial System

The application of pesticides with a UAS has become a popular practice over the past few years within crop production. The ability to carry larger volumes of liquid i onboard, reduced costs, and simple operation has attributed to the increased popularity. Additionally, the increased number of fungicide applications in corn due to the tar spot disease has shown that the demand for aerial applications of all types has increased with UAS pesticide application technology providing the opportunity... A. Leininger, K. Verhoff, K. Lovejoy, A. Thomas, G. Davis, A. Emmons, J.P. Fulton

194. Design and Development of a Spraying System for Under Canopy Rover and Its Integration with Computer Vision System

Chemical spraying such as herbicides, insecticides are essential in any agricultural field for controlling pest, weed etc. and ultimately increasing yield. About one-third of agricultural yields rely on the utilization of pesticides. However, around 3 billion kilograms of pesticides are used worldwide every year and effective utilization of it is merely 1%. The precise application of these chemicals is necessary to reduce negative impacts on environment as well as human health. The applicatio... N.K. Piya, A. Sharda, J.R. Persch, D. Flippo, R. Harsha chepally

195. Design of a Greenhouse Monitoring System Based on GSM Technologies

Nowadays, internet and mobile technologies are developing and being used in everyday life. Systems based on mobile technologies and IoT (Internet of Things) are being popular in every area of life and science. Innovative IoT applications are helping to increase the quality, quantity, sustainability and cost effectiveness of agricultural production. In this study; a system which monitors temperature, relative humidity and PAR (Photosynthetically Active Radiation) and warns the farmer... G.T. Seyhan, U. Yegul, M. Ayık

196. Design of an Automatic Travelling Electric Fence System for Sustainable Grazing Management

Fences are used in Precision Livestock Farming (PLF) to prevent herbivores from overgrazing and under grazing forages. While effective in controlling animal entry and exit, traditional fences are not flexible enough to meet the needs of both foraging animals and plants in terms of both nutrient availability and physiological demands. An electric fencing system is a form of traditional fencing that employs an electric charge to create a barrier and dissuade animals or people from crossing it. ... M. Alahe, Y. Chang, J.O. Kemeshi, S. Gummi, H. Menendez iii

197. Design of an Autonomous Ag Platform Capable of Field Scale Data Collection in Support of Artificial Intelligence

The Pivot+ Array is intended to serve as an innovative, multi-user research platform dedicated to the autonomous monitoring, analysis, and manipulation of crops and inputs at the plant scale, covering extensive areas. It will effectively address many constraints that have historically limited large-scale agricultural sensor and robotic research. This achievement will be made possible by augmenting the well-established center pivot technology, known for its autonomy, with robust power inf... S. Jha, J. Krogmeier, D. Buckmaster, D.J. Love, R.H. Grant, M. Crawford, C. Brinton, C. Wang, D. Cappelleri, A. Balmos

198. Design of VAV System of Air Assisted Sprayer in Orchard and Experimental Study in China

One type of new automatic target detecting based on size of canopy with variable chemical dosage and air-flow of fan orchard sprayer was designed and developed to meet the demand of chemical pest control in orchards. Canopy parameter data scanned by infrared sensors and LIDAR (Light Detection and Ranging) were used to detect the target and to design spraying algorithm and PWM (Pulse Width Modulation) control system. Four integrated five-finger atomizers were equipped on each side of sprayer, ... H. Xiongkui, L. Longlong, S. Jianli, Z. Aijun, L. Yajia

199. Detailed Derivation of Spatial Soil Attributes Using Soil Sensor Data, Terrain Analysis and Soil Maps with Supervised Classification

Detailed knowledge of the spatial distribution of soils is critical for improved management and modeling in agriculture and forestry. However, information from existing soil maps is often not accurate enough and soil units are too large. In the current study, we used intensively collected information from soil profile analyses at the Scheyern site and used this as training data to map soil relationships on land in Dürnast with long-term fertilization experiments (BonaRes). Both... K. Heil

200. Detect Estrus in Sows Using a Lidar Sensor and Machine Learning

Accurate estrus detection of sows is labor intensive and is crucial to achieve high farrowing rate. This study aims to develop a method to detect accurate estrus time by monitoring the change in vulvar swollenness around estrus using a light detection and ranging (LiDAR) camera. The measurement accuracy of the LiDAR camera was evaluated in laboratory conditions before it was used in monitoring sows in a swine research facility. In this study, twelve multiparous individually housed sows were c... J. Zhou, Z. Xu

201. Detecting Nitrogen Deficiency and Leaf Chlorophyll Content (LCC) Using Sentinel-2 Vegetation Indices

Leaf chlorophyll content (LCC) is a significant indicator of photosynthetic performance and development status of plants. Remote sensing of crop chlorophyll often serves as a basic tool of crop nitrogen fertilization recommendation. The study's objective is to see how remote sensing can better monitor the growth difference of crops, such as LCC. In this study, we investigated the performance vegetation indices in (1) detecting the responses of wheat growth to nitrogen deficiency, and (2) ... X. Xu, A. Mokhtari, K. Yu

202. Detecting Nitrogen Variability at Early Growth Stages of Wheat by Active Fluorescence and NDVI

Low efficiency in the use of nitrogen fertilizer, has been reported around the world which often times result in high production costs and environmental damage. Today, unmanned aerial vehicles (UAV) cameras are being used to obtain conditions of crops, and can cover large areas in a short time. The objectives of this study were (i) to investigate N-variability in wheat at early growth stages using induced fluorescence indices, NDVI measured by active sensor and NDVI obtained by digital i... E. Patto pacheco, J. Liu, L. Longchamps, R. Khosla

203. Detection of Citrus Canker in Orange Plantation Using Fluorescence Spectroscopy

Citrus canker is a serious disease, caused by Xanthomonas axonopodis pv. Citri bacteria, which infects orange trees (Citrus aurantium L.), leading to a large economic loss in the orange juice production. Brazil produces 50% of the industrialized orange juice in the world. Therefore, the early detection and control of such disease is important for Brazilian economy. However this task is very hard and so far it has been done by naked eye inspection of each tree. Our goal is to... E.C. Lins, J. Belasque junior, L.G. Marcassa

204. Detection of Goat Herding Impact on Vegetation Cover Change Using Multi-season, Multi-herd Tracking and Satellite Imagery

The frequency and severity of Mediterranean forest fires are expected to worsen as climate change progresses, heightening the need to evaluate understory fuel management strategies as rigorously as possible. Prescribed small-ruminant foraging is considered a sustainable, cost-effective strategy, but demonstrating a link between animal presence and vegetation change is challenging. This study tested whether the effect of small-ruminant herd presence in Mediterranean woodlands can be detected b... T. Paz kagan, V. Alexandroff, E.D. Ungar

205. Detection of Nitrogen Stress on Winter Wheat by Multispectral Machine Vision

Hand-held sensors (SPAD meter, N-Tester, …) used for detecting the leaves nitrogen  concentration (Nc) present several drawbacks. The nitrogen concentration is gained by an indirect way through the chlorophyll concentration and the leaves have to be fixed in a defined position for the measurements. These drawbacks could be overcome by an imaging device that measures the canopy reflectance. Hence, the objective of the paper is to analyse the potential of multispectral imaging for d... M. Destain, V. Leemans, G. Marlier, J. Goffart, B. Bodson, B. Mercatoris, F. Gritten

206. Detection of Potato Beetle Damage Using Remote Sensing from Small Unmanned Aircraft Systems

Remote sensing with small unmanned aircraft systems (sUAS) has potential applications in agriculture because low flight altitudes allow image acquisition at very high spatial resolution.  We set up experiments at the Oregon State University Hermiston Agricultural Research and Extension Center (HAREC) to assess advantages and disadvantages of sUAS for precision farming. In 2014, we conducted an experiment in irrigated potatoes with 4 levels of artificial infestation by Colorado Potato Bee... E. Hunt, S.I. Rondon, A.E. Bruce, R.W. Turner, J.J. Brungardt

207. Detection of Sorghum Aphids with Advanced Machine Vision

Sorghum aphid, Melanaphis sorghi (Theobald), became a significant pest concern due to the significant yield losses caused in the sorghum production region. Different management practices, including monitoring and applying insecticides, have been used to manage this invasive pest in sorghum. The most common management strategy consists of visual assessments of aphids on sorghum leaves to determine an economic threshold level to spray. However, because of their rapid reproduction,... I.A. Grijalva teran, B. Spiesman, N. Clark, B. Mccornack

208. Determinants of Ex-ante Adoption of Precision Agriculture Technologies by Cocoa Farmers in Ghana

The study was to identify the best predictors of cocoa Farmers willingness to adopt future Precision Agriculture Technology (PAT) Development in Ghana. Correlational research design was used. The target population was all cocoa farmers who benefited from Cocoa High Technology Programme (an initiative of distributing free fertilizer by government to cocoa farmers) in Ghana. Multistage sampling technique was used to select 422 out of 400,000 cocoa farmers in the six (6) out of the seven (7) coc... M. Bosompem, J.A. Kwarteng, H.D. Acquah

209. Determining Desirable Swine Traits that Correlate to High Carcass Grades for Artificial Intelligence Predictions

With the global population continuing to grow, there has been an increased stress applied to the agriculture industry to improve efficiency and yield. To achieve this goal within the cattle industry, selection and reproductive decisions have been lucrative aspects, both genetically and fiscally. Breeding animal selection impacts farms through passing on favorable market, reproductive, and temperament traits. The cattle industry has experienced genetic advancement due to the flexibility of art... A.N. Spina, J.P. Fulton, S.A. Shearer, T. Berger-wolf, D. Drewry

210. Determining Site-Specific Soybean Optimal Seeding Rate Using On-Farm Precision Experimentation

Ten on-farm precision experiments were conducted in Nebraska during 2018 – 2022 to address the following: i) determine the Economic Optimal Seeding Rates (EOSR), ii) identify the most important site-specific variables influencing the optimal seeding rates for soybeans. Seeding rates ranged from 200,000 to 440,000 seeds ha-1, and treatments were randomized and replicated in blocks across the entire field. The study was implemented using a variable rate prescription. ... M.M. Dalla betta, L. Puntel, L. Thompson, T. Mieno, J.D. Luck, N. Cafaro la menza, P. Paccioretti

211. Determining the Marginal Value of Extra Precision in Precision Grazing Systems – an Ex Ante Analysis of Impacts on System Productivity, Sustainability and Economics

The development of precision livestock farming (PLF) technologies for application in grazing systems is rapidly evolving. PLF technologies that facilitate the spatial and temporal management of variability in landscapes, pastures and animals promise to improve the efficiency, profitability and sustainability of livestock farming. However, such technologies as a complete package do not yet exist in grazing systems and the question of impacts at the farm system level remains unresolved. Other p... K. Behrendt, T. Takahashi, M.S. Rutter

212. Develop Portable Near-infrared Sensing Devices for Rapid Seed Moisture Measuring in Grass Seed Crops

To maximize harvest efficiency and seed yield, it is essential to harvest seed crops at appropriate timing. Seed moisture content (SMC) is the most reliable indicator of seed maturity and harvest timing in grass seed crops. Currently, to determine the SMC of a particular field, a minimum sample of 30 to 50 seed heads has to be collected from representative areas of the field and measured by wet and dry weights to calculate the SMC. The seeds must be either oven dried, microwave dried, or plac... J. Zhou

213. Developing a Decision Support Model for Informing N Fertilization in Corn

Assessing crop nitrogen (N) status is crucial for optimizing the application of N fertilizers in corn. The Critical Nitrogen Dilution Curve (CNDC) stands as a fundamental model supporting diagnostic tool for identifying the corn nitrogen (N) status. However, there is a need for efficient, non-destructive methods to estimate the crop N status. The objective of this study was to evaluate the potential of three handheld sensors: SPAD, LI-600, and Green Seeker to diagnose corn N deficiencies at e... L. Lemes bosche, I. Ciampitti

214. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US Midwest

Effective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly acr... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan

215. Developing a neural-network model for detecting Aflatoxin hotspots in peanut fields

Aflatoxin is a carcinogenic toxin produced by a soilborne fungi, called Aspergillus flavus, causing a difficult struggle for the peanut industry in terms of produce quality, price and the range of selling market. This study aims to develop a successful U-Net CNN (Convolutional Neural Network) model, a reliable image segmentation method, that will help in distinguishing high probability zones of occurrence of Aflatoxin in peanut fields using remotely sensed hyperspectral imagery. The research ... S. Kukal, G. Vellidis

216. Developing a Wheat Precision Nitrogen Management Strategy by Combining Satellite Remote Sensing Data and WheatGrow Model

Precision nitrogen (N) management (PNM) is becoming increasingly popular due to its ability to synchronize crop N demand with soil N supply spatiotemporally. The previous evidence has demonstrated that variable rate fertilization contributes to achieving high yields and high efficiencies. However, PNM at the regional level remains unclear and challenging. This study aims to develop a novel management zone (MZ)-based PNM strategy (MZ-PNM) to optimize the basal and topdressing N rates at the re... Y. Miao, X. Liu, Y. Tian, Y. Zhu, W. Cao, Q. Cao, X. Chen, Y. Li

217. Developing Empirical Method to Estimate Phosphorous in Potato Plants Using Spectroscopy-based Approach

Application of non-destructive sensors opens a promising opportunity to provide efficient information on nutrient contents based on leaf or canopy reflectance in different crops. In potatoes, nutrient levels are estimated by conducting chemical tests for the petioles. In thinking of deploying sensors for potato nutrient estimation, it is necessary to study the spectrum based on petiole chemical testing rather than leaf chemical testing. Thus, this study aimed to investigate whether there is a... R. Abukmeil, A. Almallahi

218. Developing Geospatial Method for Autopilot Harvester Trampling Evaluation in Colombian Sugarcane Fields

Sugarcane is a crop of great importance for the geographical valley of the Cauca River in Colombia, where it covers approximately 241,000 hectares and is cultivated by 13 sugar mills and about 4,200 cultivators. This region is characterized by its favorable climate, which enables year-round sugarcane harvesting and its high productivity, making it a global leader in this sector. This achievement is largely attributed to the technological advances developed by Colombia Sugarcane Research Cente... J.D. Ome narvaez, D.F. Sandoval, S.A. Galeano, H.B. Tarapues, A. Estrada, J.P. Zuñiga, J.M. Valencia-correa

219. Developing Nitrogen Algorithms for Corn Production Using Optical Sensors

Remote sensing for nitrogen management in cereal crops has been an intensive research area due to environmental concerns and economic realities of today’s agronomic system. In the search for improved nitrogen rate decisions, what approach is most often taken and are those approaches justified through scientific investigation? The objective of this presentation is to educate decision makers on how these algorithms are developed and evaluate how well they work in the field on a small-plot... R.W. Mullen, S.B. Phillips, W.R. Raun, W.E. Thomason

220. Developing UAV Image Acquisition System and Processing Steps for Quantitative Use of the Data in Precision Agriculture

Mapping natural variability of crops and land is first step of the management cycle in terms of crop production. Several methods have been developed and engaged for data recording and analyzing that generate prescription maps such as yield monitoring, soil mapping, remote sensing etc. Although conventional remote sensing by capturing images via satellites has been very popular tool to monitor the earth surface, it has several drawbacks such as orbital period, unattended capture, investment co... A. Tekin, M. Fornale

221. Development and Evaluation of a Novel Variable-orifice Nozzle Flow and Droplet Size Control System

Spray drift from crop production operations has been a critical concern across the U.S. as evidenced by the EPA’s efforts to mitigate pesticide drift. Recently, a novel spray control system was developed and evaluated which provided real-time control of both spray droplet size and flow rate. This was achieved via electromechanical control of a variable orifice nozzle along with a novel control system which incorporates real-time weather data to vary system pressure and orifice size and ... T. Monroe, J.D. Luck, S. Marx

222. Development of a Crop Edge Line Detection Algorithm Using a Laser Scanner for an Autonomous Combine Harvester

The high cost of real-time kinematic (RTK) differential GPS units required for autonomous guidance of agricultural machinery has limited their use in practical auto-guided systems especially applicable to small-sized farming conditions. A laser range finder (LRF) scanner system with a pan-tilt unit (PTU) has the ability to create a 3D profile of objects with a high level of accuracy by scanning their surroundings in a fan shape based on the time-of-flight measurement principle. This paper des... C. Jeon, H. Kim, X. Han, H. Moon

223. Development of a Granular Herbicide Spot Applicator for Management of Hair Fescue (Festuca Filiformis) in Wild Blueberry (Vaccinium Angustifolium)

Hair fescue has quickly become the pest of greatest concern for the wild blueberry industry. This is largely due to its ability to outcompete wild blueberry for critical resources including water, nutrients and most importantly space. In Nova Scotia, between 2001 and 2019, hair fescue had increased in field frequency from 7% to 68% and in field uniformity from 1.4% to 25%. This rapidly spreading and economically destructive weed is likewise a significant challenge to manage, with only a s... C. Maceachern, T. Esau, Q. Zaman

224. Development of a High-throughput UAV System for Precision Weed Detection and Control Using Laser Speckle Imaging and UV-C Irradiation

Traditional weed control methods, predominantly reliant on herbicides or labor-intensive ground robots, present notable environmental and efficiency challenges within agricultural practices. To address these concerns, this study introduces an innovative approach utilizing unmanned aerial vehicles (UAVs) for autonomous weed detection and control in agricultural fields. Our proposed system depends on the agility of UAV platforms, integrating two primary technologies. Firstly, Laser Speckle Imag... M.A. Salem, A.H. Rabia

225. Development of a Multiband Sensor for Citrus Black Spot Disease Detection

Citrus black spot (CBS), or Guignardia citricarpa, is known as the most destroying citrus fungal disease worldwide. CBS causes yield loss as a result of early fruit drop, and it leaves severely blemished and unmarketable fruit. While leaves usually remain symptomless, CBS generates various forms of lesions on citrus fruits including hard spot, cracked spot, and virulent spot. CBS lesions often appear on maturing fruit, starting two months before maturity. Warm temperature and sunlight exposur... A. Pourreza, W. Lee, J. Lu, P. Roberts

226. Development of a Multispectral Sensor for Crop Canopy Temperature Measurement

Quantifying spatial and temporal variability in plant stress has precision agriculture applications in controlling variable rate irrigation and variable rate nutrient application. One approach to plant stress detection is crop canopy temperature measurement by the use of thermographic or radiometric methods, generally in the long wave infrared (LWIR) wavelength range. A confounding factor in LWIR canopy temperature estimation is eliminating the effect of the soil background in the image. One ... P. Drew, K.A. Sudduth, E. Sadler

227. Development of a Multispectral Vision-based Automated Sweetpotato Grading System

Quality evaluation and grading of sweetpotatoes is a manual operation that requires significant labor input. Machine vision technology offers a promising solution for automated sweetpotato grading and sorting. Although color imaging is widely used for quality evaluation of various horticultural commodities, a multispectral vision technique that acquires color and near-infrared (NIR) images simultaneously is a potentially more effective modality for fruit grading, especially for defects, while... J. Xu, Y. Lu

228. Development of a PWM Precision Spraying System for Unmanned Helicopter

Application of protection materials is a crucial component in the high productivity of agriculture. Motivated by the needs of aerial precision application, in this paper we present a pulse width modulation (PWM) based precision spraying system for unmanned helicopter. The system is composed of the tank, pipelines, pump, nozzles and the automatic control unit. The system can spray with a constant rate automatically when the speed of the UAV fluctuates between 1 m/s to 8 m/s. The application ra... R. Zhang, L. Chen, T. Yi, Y. Guo, H. Zhang

229. Development of a Sensing Device for Detecting Defoliation in Soybean

Estimating defoliation by insects in an agricultural field, specifically soybean, is performed by manually removing multiple leaf samples, visually inspecting the leaves for feeding, and assigning a value representing a “best guess” at the level of leaf material missing. These estimates can require considerable time and are subjective. The goal of this study was to design a low-cost system containing light sensors and a microcontroller that could remotely record and report long-te... P. Astillo, J. Maja, J. Greene

230. Development of an Airborne Remote Sensing System for Aerial Applicators

An airborne remote sensing system was developed and tested for recording aerial images of field crops, which were analyzed for variations of crop health or pest infestation. The multicomponent system consists of a multi-spectral camera system, a camera control system, and a radiometer for normalizing images. To overcome the difficulties currently associated with correlating imagery data with what is actually occurring on the ground (a process known as ground truthing); a hyperspectral reflect... Y. Lan, Y. Huang, D.E. Martin, W.C. Hoffmann, B.K. Fritz, J.D. López

231. Development of Land Leveling Equipment Based on GNSS

An attitude adjustable land leveling equipment was designed. The reference elevation of the land to be leveled was generated based on the topographic data which was acquired by the RTK-GNSS technology. The blade lifting mechanism was controlled by comparing the reference elevation and the real-time blade’s elevation and attitude data which was obtained by the dual antenna GNSS receiver and as a result the land leveling operation was implemented. A new algorithm using the electro-hydraul... W. Fu, G. Wu, H. Bao, X. Wei, Z. Meng

232. Development of Micro-tractor-based Measurement Device of Soil Organic Matter Using On-the-go Visual-near Infrared Spectroscopy in Paddy Fields of South China

Soil organic matter (SOM) is an essential soil property for assessing the fertility of paddy soils in South China. In this study, a set of micro-tractor-based on-the-go device was developed and integrated to measure in-situ soil visible and near infrared (VIS–NIR) spectroscopy and estimate SOM content. This micro-tractor-based on-the-go device is composed of a micro-tractor with toothed-caterpillar band, a USB2000+ VIS–NIR spectroscopy detector, a self-customized steel plow and a ... Z. Lianqing, S. Zhou, C. Songchao, Y. Yafei

233. Development of Real-time Color Analysis for the On- Line Automated Weeding Operations

Weeds compete with the crop for water, light, nutrients and space, and therefore reduce crop yields and also affect the efficient use of machinery. Chemical sprayer is the most popular method to eradicate weeds but has cause hazardous to the environment, crops and workers. A smart sprayer is required to control the usage of chemical weedicides at the optimal level. Thus an on-line automated sprayer is introduced to the Malaysian farmers to locate in the real time environment the existence and... W. Wan ismail, K. Abdul rahman

234. Development of Sensor Reflection Indices To Predict Yield And Protein Content Based On In-Season N Status

Environmental and economic demands make it necessary for farmers to adopt   management systems that improve Nitrogen Use Efficiency. The premium paid to producers has made farmers striving for maximum grain protein levels because protein is a very important quality component of grains and an important attribute in the market place. The protein content of wheat grains approximately ranges from 8 to 20%. The optimization of nitrogen (N) fertilization is the object of intense research ... U. Yegul, B. Talebpour, U. TÜrker, B.M. EmİnoĞlu, G.T. Seyhan, A. Çolak

235. Development of Standard Protocols for Soil Tilth Assessment As an Essential Component of Tillage Tool Automation to Improve Soil Health

The accurate assessment of soil tilth may be pivotal when assessing soil health as part of a holistic process to ensure sustainable and profitable crop production practices. In this study, we focus on demonstrating methodologies for the spatial assessment of soil tilth as ground truth for assessing real-time soil tilth quality sensing technologies. The proposed methodologies for evaluating tillage effects involve the integration of the line transect method for residue distribution analysis. S... C. Dean, A. Klopfenstein, A. Klopfenstein, S.A. Shearer

236. Diagnosis of Grapevine Nutrient Content Using Proximal Hyperspectral Imaging

Nutrient deficiencies on grapevines could affect the fruit yield and quality, which is a major concern in vineyards. Nutrient deficiencies may be recognizable by foliar symptoms that vary by mineral nutrient and stress severity, but it is too late to manage when visible deficiency symptoms become apparent. The nutrient analysis in the laboratory is the way to get an accurate result, but it is time and cost-intensive. The differences in leaf nutrient levels also alter spectral characteristics ... C. Kang, M. Karkee, Q. Zhang, N. Shcherbatyuk, P. Davadant, M. Keller

237. Digital Agriculture Driven by Big Data Analytics: a Focus on Spatio-temporal Crop Yield Stability and Land Productivity

In the ever-evolving landscape of agriculture, the adoption of digital technologies and big data analytics has ushered in a transformative era known as digital agriculture. This paradigm shift is primarily motivated by the pressing imperative to address the growing global population's food requirements, mitigate the adverse effects of climate change, and promote sustainable land management. Canada, a significant player in global food production, has made a substantial commitment to reduci... K. Nketia, T. Ha, H. Fernando, S. Shirtliffe, S. Van steenbergen

238. Digital Soil Sensing and Mapping for Crop Suitability

Soil, central to any land-based production system, determines the success of any crops. While soil for a farm or field is fixed, the crops can be selected to best fit the soil’s capability and production. Traditionally crops are selected based on farm history, knowledge, and years of trial and error to tailor the right crop to the right soil. Inherent challenges associated with this make the whole process unsustainable. Due to the consistent nature of the information collected, soil sen... D. Saurette, A. Biswas, T.B. Gobezie

239. Dimensionality Reduction and Similarity Metrics for Predicting Crop Yields in Sparse Data Microclimates

This study explores and develops new methodologies for predicting agricultural outcomes, such as crop yields, in microclimates characterized by sparse meteorological data. Specifically, it focuses on reducing the dimensionality in time series data as a preprocessing step to generate simpler and more explainable forecast models. Dimensionality reduction helps in managing large data sets by simplifying the information into more manageable forms without significant loss of information. We explor... L. Huender, M. Everett

240. Driving Growth Through Precision Agriculture: the Evolution of the Nebraska On-farm Research Network

The Nebraska On-Farm Research Network (NOFRN), allows farmers to answer production, profitability and sustainability questions in their own field. The University of Nebraska (USA) sponsors the NOFRN and provides technical support in the experimental design, execution, data analysis and results dissemination. In recent years, precision agriculture technologies have expanded network capabilities through an increasing ​number of experiments and provided new avenues for data analyses. The goal ... G. Balboa, B. Tobaldo, T. Lexow, J.D. Luck

241. Drone Use Extension and Demonstrations Support Management of Riparian Areas, Grazing Land, and Water Quality

Agricultural and natural resource managers have explored a variety of ways in which drones might be used to aid in decision-making. One of the most useful ways may be the production of orthorectified aerial photography which can have very high spatial and temporal resolution. Such photography offers new opportunities for visualizing and measuring features on the landscape. Not just measuring the two-dimensional characteristics of landscape features, but also measuring three-dimensional charac... W. Boyer

242. Drought Tolerance Assessment with Statistical and Deep Learning Models on Hyperspectral Images for High-throughput Plant Phenotyping

Drought is an important factor that severely restricts blueberry growth, output and adversely impacts the desirable physiologic quality. Considering the challenges posed by climate change and erratic weather patterns, evaluating the drought tolerance of blueberry plants is not only vital for the agricultural industry but also for ensuring a consistent supply of these nutritious berries to consumers. Blueberry plants have a relatively ineffective water regulation mechanism due to their shallow... M. Rahman, S. Busby, A. Sanz-saez, S. Ru, T. Rehman

243. Dynamic Management Zones for Real-time Precision Agriculture Optimization

Precision agriculture is an evolving management approach aimed at optimizing resource utilization, enhancing financial returns, and mitigating environmental impacts. The dynamic nature of agricultural conditions throughout a growing season necessitates the integration of innovative remote sensing and precision agriculture techniques. This research explores the creation of dynamic management zones (DMZ) that adapt in real-time to evolving soil and crop conditions. This study focuses on the est... A.H. Rabia, E. Eldeeb

244. Ear Deployed Accelerometer Behaviour Detection in Sheep

An animal’s behaviour can be a clear indicator of their physiological and physical state. Therefore as resting, eating, walking and ruminating are the predominant daily activities of ruminant animals, monitoring these behaviours could provide valuable information for management decisions and individual animal health status. Traditional animal monitoring methods have relied on human labor to visually observe animals. Accelerometer technology offers the possibility of remotely monitoring ... J.D. Barwick, M. Trotter, D.W. Lamb, R. Dobos, M. Welch

245. Early Detection of Nitrogen Deficiency in Corn Using High Resolution Remote Sensing and Computer Vision

The continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) deficiencies in corn fields. Current methods of field surveillance are either completed manually or with the assistance of satellite imaging, which of... D. Mulla, D. Zermas, D. Kaiser, M. Bazakos, N. Papanikolopoulos, P. Stanitsas, V. Morellas

246. EarthScout, GBC

EarthScout is a precision remote sensor technology that provides farmers and researchers with reliable data in real time, straight from your field to your desktop and mobile devices. In season data allows users to access current conditions for smarter decision making in irrigation and nitrogen management. EarthScout is a crop agnostic tool that is used in any soil type and climate. Our plug and play field sensors need no calibration and set up only takes about 5 minutes. There are no data sub... S. Wieland, A. Kelley

247. Eco-friendly LiDAR Drone Surveying for Sugarcane Land Leveling in the Cauca River Valley, Colombia

Land leveling is a crucial process in sugarcane cultivation in the Cauca River Valley. It plays a vital role in ensuring proper water flow within the fields, reducing fuel consumption for water pumping, promoting seed emergence, and facilitating other mechanized tasks that can be carried out more quickly and efficiently. Traditionally, land leveling involves the use of high-powered tractors (typically around 310 horsepower) equipped with high-precision topographic survey systems fro... S. Anderson-guerrero, A.M. Caballero-rodriguez, O. Munar vivas, J.F. Mateus-rodriguez

248. Ecological Refugia As a Precision Conservation Practice in Agricultural Systems

Current global agriculture fails to meet the basic food needs of 687.7 million people. At the same time, our food system is responsible for catastrophic losses of biodiversity. Precision conservation solutions offer the potential to benefit both production systems and natural systems. Transforming low-producing areas on farm fields into ecological refugia may provide small-scale habitat and ecosystem services in fragmented agricultural landscapes. We collaborated with three precision agricult... H. Duff, B. Maxwell

249. Economic Potential of IPMwise – a Generic Decision Support System for Integrated Weed Management in 4 Countries

Reducing use and dependency on pesticides in Denmark has been driven by political action plans since the 1980ies, and a series of nationally funded accompanying R&D programs were completed in the period 1989-2006. One result of these programs was a decision support system (DSS) for integrated weed management. The 4th generation (2016) of the agro-biological models and IT-tools in this DSS, named IPMwise. The concept of IPMwise is to systematically exploit that: ... P. Rydahl, O. Boejer, K. Torresen, J.M. Montull, A. Taberner, H. Bückmann, A. Verschwele

250. Economic Potential of RoboWeedMaps - Use of Deep Learning for Production of Weed Maps and Herbicide Application Maps

In Denmark, a new IPM ‘product chain’ has been constructed, which starts with systematic photographing of fields and ends up with field- or site-specific herbicide application. A special high-speed camera, mounted on an ATV took sufficiently good pictures of small weed plants, while driving up to 50 km/h. Pictures were uploaded to the RoboWeedMaps online platform, where appointed internal- and external persons with agro-botanical experience executed ‘virtual field ... P. Rydahl, O. Boejer, N. Jensen, B. Hartmann, R. Jorgensen, M. Soerensen, P. Andersen, L. Paz, M.B. Nielsen

251. Economics of Field Size for Autonomous Crop Machines

Field size constrains spatial and temporal management of agriculture with implications for farm profitability, field biodiversity and environmental performance. Large, conventional equipment struggles to farm small, irregularly shaped fields efficiently. The study hypothesized that autonomous crop machines would make it possible to farm small non-rectangular fields profitably, thereby preserving field biodiversity and other environmental benefits. Using the experience of the Hands Free Hectar... A. Al amin, J. Lowenberg‑deboer, K. Franklin, K. Behrendt

252. Economics of Gps-enabled Navigation Technologies

To address the economic feasibility of global positioning system (GPS) enabled navigation technologies including automated guidance and lightbar, a linear programming model was formulated using data from Midwestern U.S. Corn Belt farms. Five scenarios were compared: (i) a baseline scenario with foam, disk or other visual marker reference, (ii) lightbar navigation with basic GPS availability (+/-3 dm accuracy), (iii) lightbar with satellite subscription correction GPS (+/-1 dm), (iv) automated... T.W. Griffin, D.M. Lambert, J. Lowenberg-deboer

253. Effect of Application Rate and Height on Spray Deposition and Efficacy of Fungicides Applied with a Spray Drone in Corn

Foliar application of fungicides is a key management strategy for corn growers in the United States to protect crop yield from diseases like southern corn rust (SCR), tar spot (TS), and northern corn leaf blight (NLB). Recently, the use of spray drones for fungicide applications have gained an interest among growers and consultants due to their potential as another application tool to ensure the timely application of fungicides. Currently, the information on optimal application parameters to&... C. Byers, S. Virk, R.C. Kemerait

254. Effect of Soil Solarization, a Nonchemical Method, on the Control of Egyptian Boomrape (Orobanche Aegyptiaca) and Yield Improvement in Greenhouse Grown Cucumber

Cucumber cultivation in the Mediterranean region is susceptible to infestation by the parasitic weed egyptian broomrape (Orobanche aegyptiaca), and severe yield losses can result. The effectiveness of solarization, a soil disinfection technique that uses passive solar heating, to control the incidence of broomrape under greenhouse conditions was studied over two growing seasons. Solarization was accomplished by the application of clear polyethylene sheets to moist soil for 50 to 65 d... Z.Y. Ashrafi, H.M. Alizadeh, S. Sadeghi

255. Effect of Terrain and Soil Properties on the Effectiveness of Crop-model Based Variable Rate Nitrogen in Corn

Growers may be reluctant to adopt variable rate nitrogen (VRN) management because of potential loss in profit and yield. This study assessed the influence of terrain attributes and soil characteristics on the effectiveness of crop-model-based variable rate nitrogen (N) for corn. To evaluate the effectiveness of the VRN methods, yield, total N rate, and N use efficiency (NUE) were compared with the grower’s management. As a crop-model-based recommendation tool, Adapt-N was used. Producti... L. Puntel, L. Thompson, G. Balboa, T. Mieno, P. Paccioretti

256. Effective Furrow Closing Systems for Consistent Corn Seed Placement

Farmers face a constant challenge when choosing the appropriate planter setup due to the variability of cropping systems under no-till. Effective performance of the planter's closing wheels can reduce errors from previous components that affect seedbed formation in the furrow. Effective seed-to-soil contact during planting is essential for optimal seed emergence and overall crop stand, with the closing wheels playing a pivotal role in this process. Producers have a range of closing wheels... J. Peiretti, B. Gigena, S. Badua, A. Sharda

257. Effectiveness of Different Precision Soil Sampling Strategies for Site-Specific Nutrient Management in Row-Crops

Soil sampling is an important component of site-specific nutrient management in precision agriculture. While precision soil sampling strategies such as grid or zone have been around for a while, the adoption and utilization of these strategies varies considerably among the growers, especially in the southeastern United States. The selection of an appropriate grid size or management zone further differ among the users depending on several factors. In order to better understand how some of the ... M.W. Tucker, S. Virk, G. Harris, J. Lessl, M. Levi

258. Effects of Crop Rotation on In-season Estimation of Optimal Nitrogen Rates for Corn Based on Proximal and Remote Sensing Data

A remote sensing and calibration strip-based precision nitrogen (N) management (RS-CS-PNM) strategy has been developed by the Precision Agriculture Center at the University of Minnesota to provide in-season N recommendation rates based on satellite imagery. This strategy involves the application of multiple N rates before planting and the identification of the agronomic optimum N rate (AONR) at V7-V8 growth stages using normalized difference vegetation index (NDVI) calculated using satellite ... A.C. Morales, D. . Quinn, K. Mizuta, Y. Miao

259. Effects of Fallow Management Practices on Soil Water, Crop Yield and Water Use Efficiency in Winter Wheat Monoculture System: a Meta-analysis

Winter wheat monoculture is a predominant cropping system for agricultural production in dry areas. However, fallow management effects on soil water conservation and crop yield and water use have been inconsistent among studies. We selected 137 studies and performed a meta-analysis to test the effects of tillage and mulching during the fallow period on precipitation storage efficiency (PSE), soil water storage at wheat planting (SWSp), crop yield, evapotranspiration (ET), and water use effici... M. Adil

260. Emerging Megatrends of Sustainable Nutrient Management Research in Sub-saharan Africa

Africa has the 12th highest population growth rates in the world, which may double by 2050; and have bio-physical constraints which impinge on development, that need to be addressed. This ever-increasing human population demands corresponding increase in food production, where low nutrient use and management is a critical challenge. Most research conducted by African scientists are rarely used in decision-making, because they are not properly aligned with the needs of decision-makers due to w... V. Aduramigba-modupe, K. Frimpong

261. Employment of the SSEB and CROPWAT Models to Estimate the Water Footprint of Potato Grown in Hyper-arid Regions of Saudi Arabia

Quantifying crops’ water footprint (WF) is essential for sustainable agriculture especially in arid regions, which suffers from harsh environmental conditions and severe shortage of freshwater resources such as Saudi Arabia. In this study, WF of irrigated potato crop was estimated for the implementation of precision agriculture techniques. The CROPWAT and the Simplified Surface Energy Balance (SSEB) approaches were adopted. Soil, plant, and yield samples were randomly collected from six... R. Madugundu, K. Al-gaadi, E. Tola

262. Enabling Field-level Connectivity in Rural Digital Agriculture with Cloud-based LoRaWAN

The widespread adoption of next-generation digital agriculture technologies in rural areas faces a critical challenge in the form of inadequate field-level connectivity. Traditional approaches to connecting people fall short in providing cost-effective solutions for many remote agricultural locations, exacerbating the digital divide. Current cellular networks, including 5G with millimeter wave technology, are urban-centric and struggle to meet the evolving digital agricultural needs, presenti... Y. Zhang, J. Bailey, A. Balmos, F.A. Castiblanco rubio, J. Krogmeier, D. Buckmaster, D. Love, J. Zhang, M. Allen

263. Enhancing Agricultural Feedback Analysis Through VUI and Deep Learning Integration

A substantial amount of information relies on consumers, influencing aspects from product adoption to overall satisfaction. Similarly, the agricultural sector is entirely dependent on farmers, who dictate the success of products and highlight associated challenges. Our study aligns with this perspective, recognizing the significance of understanding farmers' needs to assist tractor manufacturing industries. As these industries aim for widespread adoption of their products among farmers, i... S. Kaushal, A. Sharda

264. Enhancing Nutrient-related Stress Detection: High Throughput Phenotyping and Image Analysis for Improved Precision

In the 21-century agriculture has the unique responsibility to provide food, fuel, fiber and feed for the growing population under the stress of climate change and diminishing natural resources. A feat that will take considerable change to the sustainability of such practices. One of which is the idea of assessing phenotypic expression of complex traits in response to environmental factors. This idea elevates the use of phenotyping to quantitatively monitor stress manifestation.  ... K.J. Bathke, Y. Ge, S.D. Choudhury, J.D. Luck

265. Enhancing NY State On-farm Experimentation with Digital Agronomy

Agriculture is putting pressure on the ecosystems and practices need to evolve towards a more sustainable way of producing food. Industrial agriculture has imposed a unique production model on the ecosystems while it is now understood that it is more sustainable to adapt the production model to the ecosystem. This involves adapting existing solutions to the local agricultural context and developing new solutions that are best suited to the local ecosystem. Farmers are doing this by conducting... L. Longchamps

266. Enhancing On-farm Rice Yields, Water Productivity, and Profitability Through Alternate Wetting and Drying Technology in Dry Zones of West Africa

Irrigated rice farming is crucial for meeting the growing rice demand and ensuring global food security. Yet, its substantial water demand poses a significant challenge in light of increasing water scarcity. Alternate wetting and drying irrigation (AWD), one of the most widely advocated water-saving technologies, was recently introduced as a prospective solution in the semi-arid zones of West Africa. However, it remains debatable whether AWD can achieve the multiple goals of saving water whil... Y.J. Johnson, M. Becker, E.R. Dossou-yovo, K. Saito

267. Enhancing PA Adoption Through Value Connections

Despite an increase in breadth of precision agriculture over time, and the attendant elements of digital agriculture that either support PA or integrates the outputs of PA, the pace of adoption of digital agriculture in our farming systems remains slow. In assessing impediments to adoption of digital agriculture, much work to date has focused on the value proposition as considered by individual producers or value chain actors.  At this level, adoption remains constrained by perceptions o... D.W. Lamb, M.T. Schaefer

268. Enhancing Phosphorus Nutrient Management in Corn Through Tissue Analysis and Diagnostic Tools

Phosphorus (P) plays a pivotal role in crop growth, and optimizing its application is crucial for sustainable agriculture. This research focuses on advancing nutrient management by precisely evaluating tissue phosphorus concentrations in corn. The study delves into identifying critical P levels during various growth stages, assessing alternative diagnostic tools, and exploring correlations to refine phosphorus nutrition strategies. Across 26 locations in Kansas, field experiments employed a r... G. Roa acosta, D. Ruiz diaz

269. Enhancing Precision Agriculture Through Dual Weed Mapping: Delineating Inter and Intra-row Weed Populations for Optimized Crop Protection

In the field of precision agriculture, effective management of weed populations is essential for optimizing crop yield and health. This paper presents an innovative approach to weed management by employing dual weed mapping techniques that differentiate between inter-row and intra-row weed populations. Utilizing advanced imaging and data analysis of CropEye images collected by the Robotti robot from AgroIntelli (AgroIntelli A/S, Aarhus, Denmark), we have developed methods to generate distinct... R.N. Jørgensen, S. Skovsen, O. Green, C.G. Sørensen

270. Enhancing Precision Agriculture with Cosmic-ray Neutron Sensing: Monitoring Soil Moisture Dynamics and Its Impact on Grapevine Physiology

Precision agriculture has emerged as a transformative approach in modern viticulture, seeking to optimize vineyard management. Vineyard operations rely heavily on effective water management, especially in regions where water availability can significantly affect grape quality and yield. The relationship between soil moisture and grapevine physiology is however complex. Therefore, understanding these relationships is crucial for optimizing vineyard operations. Cosmic-ray neutron sensing (CRNS)... R. Mazzoleni, F. Vinzio, S. Emamalizadeh, G. Allegro, I. Filippetti, G. Baroni

271. Enhancing Seeding Efficiency: Evaluating Row Cleaners with Computer Vision in Precision Agriculture

In precision agriculture, the effective sowing of seeds is crucial but often hindered by challenges like hair pinning, low soil temperatures, and heavy residue on the soil surface. To address these issues, row cleaners are employed to clear the path for seeder opener discs, ensuring a clean, uniform trench for seed placement. This study examines the performance of various row cleaner models and introduces a novel method for their automatic, quantitative evaluation using computer vision techno... F. Sidharth, A. Sharda, B.G. Berretta

272. Enhancing Spatial Resolution of Maize Grain Yield Data

Grain yield data is frequently used for precision agriculture management purposes and as a parameter for evaluating agronomy experiments, but unexpected challenges sometimes interfere with harvest plans or cause total losses. The spatial detail of modern grain yield monitoring data is also limited by combine header width, which could be nearly 14 m in some crops.  Remote sensing data, such as multispectral imagery collected via satellite and unmanned aerial systems (UAS), could be used t... J. Siegfried, R. Khosla, D. Mandal, W. Yilma

273. Environmental Characterization for Rainfed Maize Production in the US Great Plains Region

Identifying regions with similar productivity and yield-limiting climatic factors enables the design of tailored strategies for rainfed maize (Zea mays L.) production in vulnerable environments. Within the United States (US) Great Plains region, rainfed maize production in Kansas is susceptible to weather fluctuations. This study aims to delimit environmental regions with similar crop growth conditions and to identify the main climatic factors limiting rainfed maize yield, using the ... L.N. Lingua, A. Carcedo, V. Gimenez, G. Maddonni, I. Ciampitti

274. Establishing the First Soil Water Characteristics Curve for the Soils of Prince Edward Island, Canada

Soil water characteristics curve (SWCC), for Prince Edward Island (PEI), is much more needed currently for the sustainable production of agriculture yields. It will not only fulfil the requirements of the province’s farmers for irrigation scheduling but also help the government to decide about permitting the use of groundwater for supplemental irrigation on the island.  A soil water characteristics curve in PEI does not exist to support precision agriculture practices. Precision ir... S.J. Cheema, A.A. Farooque, F. Abbas, T. Esau, K. Grewal

275. Establishment of a Canola Emergence Assessment Methodology Using Image-based Plant Count and Ground Cover Analysis

Manual assessment of emergence is a time-consuming practice that must occur within a short time-frame of the emergence stage in canola (Brassica napus). Unmanned aerial vehicles (UAV) may allow for a more thorough assessment of canola emergence by covering a wider scope of the field and in a more timely manner than in-person evaluations. This research aims to calibrate the relationship between emerging plant population count and the ground cover. The field trial took place at the Uni... K. Krys, S. Shirtliffe, H. Duddu, T. Ha, A. Attanayake, E. Johnson, E. Andvaag, I. Stavness

276. Estimating Environmental Systems Using Iterated Sigma Point Techniques: a Biomass Substrate Hypothetical System

This paper addresses the problem of biomass substrate hypothetical system estimation using sigma points kalman filter (SPKF) methods. Various conventional and state-of-theart state estimation methods are compared for the estimation performance, namely the unscented Kalman filter(UKF), the central difference Kalman filter (CDKF), the square-root unscented Kalman filter (SRUKF), the square-root central difference Kalman filter (SRCDKF), the iterated unscented Kalman filter (IUKF), the iterated ... I. Baklouti, M. Mansouri, M. Destain, A. Hamida

277. Estimating Real-time Soil Water Content (SWC) in Corn and Soybean Fields Using Machine Learning Models, Proximal Remote Sensing, and Weather Data

Soil Water Content (SWC) is crucial for precise irrigation management, especially in center-pivot systems. Real-time estimation of SWC is vital for scheduling irrigation to prevent overwatering or underwatering. Proper irrigation yields benefits such as improved water efficiency, enhanced crop yield and quality, minimized environmental impact, optimized labor and energy costs, and improved soil health. Various in-situ techniques, such as Time-domain reflectometry (TDR), frequency-do... N. Chamara, Y. Ge, F. Bai

278. Estimating Soil Carbon Stocks with In-field Visible and Near-infrared Spectroscopy

Agricultural lands can be a sink for carbon and play an important role in offsetting carbon emissions. Current methods of measuring carbon sequestration—through repeated temporal soil samples—are costly and laborious. A promising alternative is using visible, near-infrared (VNIR) diffuse reflectance spectroscopy. However, VNIR data are complex, which requires several data processing steps and often yields inconsistent results, especially when using in situ VNIR measurements. Using... C.J. Ransom, C. Vong, K.S. Veum, K.A. Sudduth, N.R. Kitchen, J. Zhou

279. Estimating Spatial and Temporal Variability in Soil Respiration Using UAV-based Multispectral and Thermal Images in an Irrigated Pistachio (Pistachia Vera L.) Orchard

Soil respiration (Rs) accounts for the autotrophic and heterotrophic respiration happening in the soil and is a major component of the carbon budget of agricultural ecosystems. Rs is controlled by various interactive factors, including soil moisture, temperature, soil properties, and vegetation productivity. To quantify the carbon budget of climate-smart agriculture systems, it is necessary to understand how irrigation and cover cropping management practices impact... A. Sapkota, M. Roby, C. Chen, I. Kisekka

280. Estimating Water and Nitrogen Deficiency in Corn Using a Multi-parameter Proximal Sensor

The Crop Circle Phenom (CCP) is an innovative integrated proximal sensor that can be potentially used to perform in-season diagnosis of nitrogen and water status. In addition to measuring spectral reflectance in several bands including the red, red edge, and near-infrared wavelengths, the CCP can also measure canopy and air temperatures and provides several parameters that can be associated with chlorophyll content, crop vigor, and water status. These capabilities differentiate the CCP from o... L. Lacerda, Y. Miao, V. Sharma, A. E. flores, A. Kechchour, J. Lu

281. Estimation of Cotton Biomass Using Unmanned Aerial Systems and Satellite-based Remote Sensing

Satellite and unmanned aerial system (UAS) images are effective in monitoring crop growth at various spatial, temporal, and spectral scales. The objective of the study was to estimate cotton biomass at different growth stages using vegetation indices (VIs) derived from UAS and satellite images. This research was conducted in a cotton field in Hale County, Texas, in 2021. Data collected include 54 plant samples at different locations for three dates of the growing season. Multispectral images ... O.I. Adedeji, B.P. Ghimire, H. Gu, R. Karn, Z. Lin, W. Guo

282. Estimation of Soil Profile Properties Using a VIS-NIR-EC-force Probe

Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related those sensor measuremen... Y. Cho, K.A. Sudduth

283. Europe Regional Meeting

... E. Gil

284. Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and Indiana

Precision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minne... K. Mizuta, Y. Miao, A.C. Morales, L.N. Lacerda, D. Cammarano, R.L. Nielsen, R. Gunzenhauser, K. Kuehner, S. Wakahara, J.A. Coulter, D.J. Mulla, D. . Quinn, B. Mcartor

285. Evaluating APSIM Model for Site-Specific N Management in Nebraska

Many approaches have been developed to estimate the optimal N application rates and increase nitrogen use efficiency (NUE). In particular, in-season and variable-rate fertilizer applications have the potential to apply N during the time of rapid plant N uptake and at the rate needed, thereby reducing the potential for nitrogen fertilizer losses. However, there remains great challenges in determining the optimal N rate to apply in site-specific locations within a field in a given year.&nb... L. Thompson, L. Puntel, S. Archontoulis

286. Evaluating Different Strategies for In-season Potato Nitrogen Status Diagnosis Using Two Leaf Sensors

Accurate and efficient in-season diagnosis of potato nitrogen (N) status is key to the success of in-season N management for improved profitability and environmental protection. Sensor-based approaches will support more timely decision making compared to plant tissue-based approaches. SPAD-502 (SPAD; Konica Minolta, Tokyo, Japan) is a commonly used sensor for potato N status diagnosis. Dualex Scientific+ (Dualex; METOS® by Pessl Instruments, Weiz, Austria) is a new leaf chlorop... S. Wakahara, Y. Miao, S. Gupta, C. Rosen

287. Evaluating Different Strategies to Analyze On-farm Precision Nitrogen Trial Data

On-farm trials are being conducted by more and more researchers and farmers. On-farm trials are very different to traditional small plot experiments due to the existence of significant within-field variability in soil-landscape conditions. Traditional statistical techniques like analysis of variance (ANOVA) are commonly adopted for on-farm trial analysis to evaluate overall performance of different treatments, assuming uniform environmental and management factors within a field. As a result, ... K. Mizuta, Y. Miao, J. Lu, R.P. Negrini

288. Evaluating How Operator Experience Level Affects Efficiency Gains for Precision Agricultural Tools

Tractor guidance (TG) improve environmental gains relative to non-precision technologies; however, studies evaluating how tractor operator experience for non-guidance comparisons impact gains are nonexistent. This study explores spatial relationships of overlaps and gaps with operator experience level (0-1; 2-3; 6+ years) during fertilizer and herbicide applications based on terrain attributes.  Tractor paths recorded by global navigation satellite systems were used to create overlap pol... A. Ashworth, T. Kharel, P. Owens

289. Evaluating low-cost Lidar and Active Optical Sensors for pasture and forage biomass assessment

Accurate and reliable assessment of pasture or forage biomass remains one of the key challenges for grazing industries. Livestock managers require accurate estimates of the grassland biomass available over their farm to enable optimal stocking rate decisions. This paper reports on our investigations into the potential application of affordable Lidar (Light Detection and Ranging) systems and Active Optical (reflectance) Sensors (AOS) to estimate pasture biomass. We evaluated the calibration ac... M. Trotter, K. Andersson, M. Welch, M. Chau, L. Frizzel, D. Schneider

290. Evaluating Nitrogen Use Efficiency in Wheat Using UAV Multispectral Images

Nitrogen (N) is one of the most important nutrients for crop growth and development. For crops, nitrogen fertilizer is the guarantee of high yield, but in practical applications, nitrogen fertilizer is often excessive. Therefore precise and rapid assessment of nitrogen use efficiency (NUE) plays a pivotal role in optimizing fertilizer utilization and ensuring responsible use of nitrogen in agriculture. While most of research evaluate NUE from management scales, e.g., from the field,  dis... J. Wang, K. Yu, S. T.meyer

291. Evaluating Spatial Effects Induced by Alternative On- Farm Trial Experimental Designs with Cross-regressive Variables Using Monte Carlo Methods

The goal of this research was to adapt spatial regression methods to on-farm trials in a farm management context. Different experimental designs and statistical analysis methods are tested with site-specific data under a range of spatial autocorrelation levels using Monte Carlo simulation techniques. Simulations indicated that data usable for farm management decision making could be gathered from limited replication experimental designs if that data were analyzed with the appropriate spatial ... T.W. Griffin, R.J. G.m. florax, J. Lowenberg-deboer

292. Evaluating the Impact of Irrigation Rate, Timing, and Maturity-based Cotton Cultivars on Yield and Fiber Quality in West Texas

In West Texas, effective irrigation is crucial for sustainable cotton production given the water scarcity from the declining Ogallala aquifer and erratic rainfall patterns. A three-year study (2020-2022) investigated irrigation rate and timing effects on early to mid-season cotton maturity groups. Five treatments, including rainfed (W1 or LLL) and variations in irrigation rates at growth stages (P1 to P4), were applied. Evaluation involved six to seven cotton cultivars from four maturity grou... O. Adedeji, R. Karn, B.P. Ghimire, W. Guo, E.N. Wieber

293. Evaluating the Impact of Vegetation Indices on Plant Nitrogen Uptake Prediction: a Comparative Study of Regression Models at Various Growth Stages

Nitrogen and water play crucial roles in impacting both the health and yield of corn crops. However, their demands vary under different soil and weather conditions. Unfortunately, current nitrogen management practices in irrigated fields in the state of Georgia overlook this variability. Thus, this oversight may lead to insufficient nitrogen application, causing plant stress or excessive nitrogen application that can lead to environmental impact. To address this challenge, a precise asses... B. Ghimire, L. Lacerda, T. bourlai

294. Evaluating the Potential of Improving In-season Nitrogen Status Diagnosis of Potato Using Leaf Fluorescence Sensors and Machine Learning

Precision nitrogen (N) management is particularly important for potato crops due to their high N fertilizer demand and high N leaching potential caused by their shallow root systems and preference for coarse-textured soils. Potato farmers have been using a standard lab analysis called petiole nitrate-N (PNN) test as a tool to diagnose potato N status and guide in-season N management. However, the PNN test suffers from many disadvantages including time constraints, labor, and cost of analysis.... S. Wakahara, Y. Miao, S. Gupta, C. Rosen, K. Mizuta, J. Zhang, D. Li

295. Evaluating the Potential of In-season Spatial Prediction of Corn Yield and Responses to Nitrogen by Combining Crop Growth Modeling, Satellite Remote Sensing and Machine Learning

Nitrogen (N) is a critical yield-limiting factor for corn (Zea mays L.). However, over-application of N fertilizers is a common problem in the US Midwest, leading to many environmental problems. It is crucial to develop efficient precision N management (PNM) strategies to improve corn N management. Different PNM strategies have been developed using proximal and remote sensing, crop growth modeling and machine learning. These strategies have both advantages and disadvantages. There is... X. Zhen, Y. Miao, K. Mizuta, S. Folle, J. Lu, R.P. Negrini, G. Feng, Y. Huang

296. Evaluating the Potential of Integrated Precision Irrigation and Nitrogen Management for Corn in Minnesota

The environmental impact of irrigated agriculture on ground and surface water resources in Minnesota is of major concern. Previous studies have focused on either precision irrigation or precision nitrogen (N) management, with very limited studies on the integrated precision management of irrigation and N fertilizers, especially in Minnesota. The Dualex Scientific sensor is a leaf fluorescence sensor that has been used to diagnose crop&nbs... A. Elvir flores, Y. Miao, V. Sharma, L. Lacerda

297. Evaluation of a Seed-fertilizer Application System Using a Laser Scanner

The system evaluated is a design that combines planter and sprayer technologies to allow clients to plant crops while simultaneously spraying initial fertilizer on or in close proximity to the seed.  The system is an idea Capstan Ag Systems has been pursuing for around 15 years, and has recently been revived in a partnership with Great Plains Manufacturing Company.  Great Plains Manufacturing released the final product under the name AccushotTM at the 201... P. Weckler, N. Wang, C. Zhai, L. Zhang, B. Luo, J. Long, R. Taylor

298. Evaluation of a Sensor and Control Interface Module for Monitoring of Greenhouse Environment

Protected horticulture in greenhouses and plant factories has been increased in many countries due to the advantages of year-round production in controlled environment for improved productivity and quality. For protected horticulture, environmental conditions are monitored and controlled through wired and wireless devices. Various devices are used for monitoring and control of spatial and temporal variability in crop growth environmental conditions. Recently, various sensors and control devic... N. Sung, S. Chung, Y. Kim, K. Han, J. Choi, J. Kim, Y. Cho, S. Jang

299. Evaluation of a Single Transect Method for Collecting Grape Samples Based on Sentinel-2 Imagery for the Characterization of Overall Vineyard Performance

Commercial vineyards are streamed into different wine programs based on analysis of grape or juice samples collected from the field, but spatial and temporal variability can lead to sub-optimal tiering of grapes. This is a particularly difficult problem to overcome in the typically large vineyards of California’s Central Valley. Due to economic and laboratory constraints on sample collection, processing, and analysis, a single sample is often expected to represent the overall fruit qual... B. Sams, M. Aboutalebi, L. Sanchez, N. Dokoozlian, R. Bramley

300. Evaluation of Crop Model Based Tools for Corn Site-specific N Management in Nebraska

There is a critical need to reduce the nitrogen (N) footprint from corn-based cropping systems while maintaining or increasing yields and profits. Digital agriculture technologies for site-specific N management have been demonstrated to improve nitrogen use efficiency (NUE). However, adoption of these technologies remains low. Factors such as cost, complexity, unknown impact and large data inputs are associated with low adoption. Grower’s hands-on experience coupled with targeted resear... L. Puntel, L. Thompson , T. Mieno, S. Norquest

301. Evaluation of Fall and Spring Nitrogen Rates Effect on Cereal Rye Forage Crude Protein and Tillering Using NDVI and Canopeo to Make Infield Nitrogen Rate Decisions

Fall applied nitrogen has been used to increase plant tiller and protein in wheat but less research has been done of its effects on cereal rye forage and how NDVI and Canopeo readings can be used to make nitrogen application management decisions. This study took place at the Ohio State University North Central Agricultural Research Station in Fremont, Ohio. The experiment is a randomized complete block split-plot design with four nitrogen rates in the fall (0, 30, 60, and 90 lbs/ac) and in th... K. Stahl, J.M. Hartschuh, A. Gahler

302. Evaluation of Image Acquisition Parameters and Data Extraction Methods on Plant Height Estimation with UAS Imagery

Aerial imagery from unmanned aircraft systems (UASs) has been increasingly used for field phenotyping and precision agriculture. Plant height is one important crop growth parameter that has been estimated from 3D point clouds and digital surface models (DSMs) derived from UAS-based aerial imagery. However, many factors can affect the accuracy of aerial plant height estimation. This study examined the effects of image overlap, pixel resolution, and data extraction methods on estimati... C. Yang, C. Suh, W. Guo, H. Zhao, J. Zhang, R. Eyster

303. Evaluation of Indwelling Rumen Temperature Monitoring System for Dairy Calf Illness Detection and Management

Precision Dairy Farming technology has mostly focused on tools to improve cow care, but new tools are available to improve the care of pre-wean calves and heifers. These technologies apply real-time monitoring to measure individual animal data and detect a deviation from normal. On-farm validation of new technologies remains important for successful deployment of new technologies within commercial farms to understand how the technology can improve dairy calf welfare, performance, and health. ... J.M. Hartschuh, J.P. Fulton, S.A. Shearer, B.D. Enger, G.M. Schuenemann

304. Evaluation of Nitrogen Recommendation Tools for Winter Wheat in Nebraska

Attaining both high yield and high nitrogen (N) use efficiency (NUE) simultaneously remains a current research challenge in crop production. Digital ag technologies for site-specific N management have been demonstrated to improve NUE. This is due to the ability of digital technologies to account for the spatial and temporal distribution of crop N demand and available soil N in the field which varies greatly according t... J. Cesario pereira pinto, L. Thompson, N. Mueller, T. Mieno, G. Balboa, L. Puntel

305. Evaluation of Peanut Response to Soil Water Levels Using the Crop Water Stress Index Generated from Infrared Thermal Sensors and Imagery

In precision agriculture, precise monitoring of crop water stress is crucial for optimizing water use, increasing crop yield, and promoting environmental sustainability. Achieving high water use efficiency in peanut production is key to producing high-quality crop. This study investigates the efficiency of infrared thermal sensors and thermal imagery from satellites and unmanned aerial vehicles (UAVs) for determining peanut crop water stress index (CWSI). Furthermore, this research explores t... B. Parbi, B.V. Ortiz, E. Abban-baidoo , A. Sanz-saez, J.S. Velasco

306. Evaluation of the Effect of Different Herbicide Treatments by Using UAV in Maise (Zea mays L.) Cultivation – First Experiences in a Long-term Experiment at Széchenyi István University, Hungary

As part of the Green Deal, the European Union has set a goal to reduce the use of chemical pesticides by 50 percent until 2030. To achieve this goal, in addition to reducing the amount of pesticide used, attention must also be paid to monitoring the temporal and spatial effects of pesticides on weeds during the cultivation of various crops. Hence, Syngenta Ltd., collaborating with researchers, aimed to monitor the effect of five different types of herbicides by UAV in two tillage treatments (... I.M. Kulmany, B. Horváth, G. Kukorelli, S. Zsebő, D. Stencinger, Z. Borbás, R. Pecze, L. Bede, Z. Varga, A. Kósa, G. Pinke, Z.K. Hashim, G. Hegedűs, J.A. Abdinoor, G.S. Agampodi

307. Evaluation of the Effects of Telone Ii on Nitrogen Management and Yield in Louisiana Delta Cotton

Research indicates that cotton yield on light soils within the alluvial flood plain of the Lower Mississippi delta may be increased by using chemical fumigation applications of Telone II and/or seed treatments to control infestations of plant parasitic nematodes. There is a documented interaction with fumigation and nitrogen and therefore a need to further understand the performance of site- specific treatment strategies for nitrogen (N) and fumigation treatments. In a small plot test conduct... E. Burris, D. Burns, K.S. Mccarter, C. Overstreet, M. Wolcott

308. Evaluation of Unmanned Aerial Vehicle Images in Estimating Cotton Nitrogen Content

Estimating crop nitrogen content is a critical step for optimizing nitrogen fertilizer application. The objective of this study was to evaluate the application of UAV images in estimating cotton (Gossypium hirsutum L.) N content. This study was conducted in a dryland cotton field in Garza County, Texas, in 2020. The experiment was implemented as a randomized complete block design with three N rates of 0, 34, and 67 kg N ha-1. A RedEdge multispectral sensor was used to acqu... R. Karn, H. Gu, O. Adedeji, W. Guo

309. Evaluation of Utilization Potential for Methods of Georeference in the Management of Weed Contamination of Potato Cultures

Combating crop contamination with harmful invasive species is one of the main themes of agricultural research. For the potato cultures, the weed contamination decreases not only the quality but also the quantity of the harvest. The most invasive contamination for this culture is represented by the Agropyron repens and Sorgum halepense, two invasive and very nocive species characterized by underground stems able to penetrate the potato¢s tubercle and decrease their stora... L. Musetescu, M. Gidea

310. Evolving Nexus of Academia, Industry, and Government to Advance and Realize the Benefits of Robotics in Crop Production Agriculture

... E.M. Barnes, M. Scott, S.A. Shearer

311. Explainable Neural Network Alternatives for Ai Predictions: Genetic Algorithm Quantitative Association Rule Mining

Neural networks in one form or another are common precision agriculture artificial intelligence techniques for making predictions based on data. However, neural networks are computationally intensive to train and to run, and are typically “black-box” models without explainable output. This paper investigates an alternative artificial intelligence prediction technique, genetic algorithm quantitative association rule mining, which creates explainable output with impacts directly qua... M. Everett

312. Exploring Crop Suitability in Senegal Across Global Warming Scenarios: an In-silico Approach

Food production systems in Africa are fragile and vulnerable to climate change. In this context, rising temperatures are the primary cause of the anticipated negative climate change impacts on crop yields. Future yield reductions poses a challenging setting for smallholders to attain self-sufficiency, but new opportunities for managing the risk via implementationof decisions towards mitigate these negative effects from an economic, nutritional, and productive standpoint. Therefore, the object... A. Carcedo, I. Ciampitti

313. Exploring the Use of a Model-based Nitrogen Recommendation Tool and Vegetation Indices for In-season Corn Nitrogen Management in Alabama

Efficient nitrogen (N) management is critical for sustainable agriculture. Crop N needs and uptake changes within a field and it is annually influenced by weather conditions. Hence, site-specific in-season N application strategies are important to achieve optimum corn yield while minimizing negative impacts on the environment. This study evaluates the Adapt-N tool for in-season variable rate N application at two farmers’ fields in Alabama. The Adapt-N tool integrates soil and crop-based... P.R. Duarte, B.V. Ortiz, E. Abban-baidoo, E. Francisco, M.F. De oliveira

314. Extension Program Prioritization Guides Web-mapping Application Delivery to Ranchers

Cooperative Extension has a long history of helping agricultural producers address their current needs and emerging public issues; often through training in the use of technologies that are not yet widely adopted. The quality of geospatial data and tools to visualize and analyze that data continues to improve. However, barriers exist to rancher adoption of geospatial decision support tools. These barriers can include costs, ease of use, and privacy concerns. The sustainability of beef ca... W. Boyer

315. EZZone - An Online Tool for Delineating Management Zones

Management zones are a pillar of Precision Agriculture research.  Spatial variability is apparent in all fields, and assessing this variability through measurement devices can lead to better management decisions.  The use of Geographic Information Systems for agricultural management is common, especially with management zones.  Although many algorithms have been produced in research settings, no online software for management zone delineation exists.  This research used a ... G. Vellidis, C. Lowrance, S. Fountas, V. Liakos

316. Farmer Charlie - Low Cost Data Analytics for Farmers Accessible in the Field

Farmer Charlie, a spin-off of AB5 Consulting Ltd, is based on an affordable business model including five elements: a data analytics platform, an agribusiness ecosystem app, capable of connecting with local third-party apps; weather and in field sensors; wi-fi Internet connectivity; and power to the field and farms via solar panels, where necessary. Farmer Charlie brings information to farmers in their own fields, in an easy plug and play solution, affordable to the farmers and addressing the... B. Bonnardel

317. Farmer Charlie - Low Cost Smart Local Data Available to Remote Farmers

Farmer Charlie brings connectivity and information to farmers, who receive tailored agronomic data to improve their agricultural practice. Farmer Charlie is based on on-site sensors through which soil data can be detected, gathered, and processed by a dedicated server. Broadband communication allows farmers to receive real-time, localised information on tablet or mobile phone. Farmer Charlie is a low-cost solution, it can be adapted to various crops and to detect soil humidity, pH, temperatur... B. Bonnardel

318. Farmers’ and Experts’ Perceptions of Precision Farming Impacts on Economic Efficiency, Food Security, Climate and Environmental Sustainability

“Global food security could be in jeopardy, due to mounting pressures on natural resources and to climate change, both of which threaten the sustainability of food systems at large. Excessive fertilizer use can contribute to problems of eutrophication, acidification, climate change and the toxic contamination of soil, water and air. Lack of fertilizer application may cause the degradation of soil fertility. Agricultural production systems need to focus more on the effective co... C.I. Anaba

319. Farming for a Greener Future: the Behavioural Drive Behind German Farmers’ Alternative Fuel Machinery Purchase Intentions

Climate change due to greenhouse gas emissions, e.g. anthropogenic carbon dioxide (CO2), in the atmosphere will lead to damages caused by global warming, increases in heavy rainfall, flooding as well as permafrost melt. One of the main issues for reducing greenhouse gas emissions is the dependence on oil for fueling transportation and other sectors. Accordingly, policy makers aim to reduce dependency on fossil fuels with the accelerated roll-out of renewable energy. Among others, t... M. Michels, V. Bonke, H. Wever, O. Mußhoff

320. Fertigation Management Strategies Effect on Residual Nitrates in the Soil Profile and Ground Water

Nitrogen is an input that is vital for growth and productivity within the corn belt states of the U.S. However, when nitrogen as an input into agricultural cropping systems is often over-applied and thus not optimally utilized by the cropping system. Therefore, it is at risk of loss within the environment through processes of leaching, denitrification, and volatilization. This is a major concern in Nebraska, as the reality is that much of the state’s groundwater has been contaminated wi... K.J. Bathke, T. Cross, J.D. Luck

321. Field Crop Robots - Adoption and Farm Level Economics

... M. Gandorfer

322. Field Mapping for Aflatoxin Assessment in Peanut Crops Using Thermal Imagery

Aflatoxin is a toxic carcinogenic compound produced by certain species of Aspergillus fungi, which has a significant impact on peanut production. Aflatoxin levels above a certain threshold (20 ppb in the USA and 4 ppb in Europe) make peanuts unsuitable for export, resulting in significant financial losses for farmers and traders. Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular for remote sensing applications in agriculture. Leveraging this advancement, UAV-based thermal imag... S. Shrestha, L. Lacerda, G. Vellidis, C. Pilcon, S. Maktabi, M. Sysskind

323. Field Phenotyping Infrastructure in a Future World - Quantifying Information on Plant Structure and Function for Precision Agriculture and Climate Change

Phenotyping in the field is an essential step in the phenotyping chain. Phenotyping begins in the well-defined, controlled conditions in laboratories and greenhouses and extends to heterogeneous, fluctuating environments in the field. Field measurements represent a significant reference point for the relevance of the laboratory and greenhouse approaches and an important source of information on potential mechanisms and constraints for plant performance tested at controlled conditions. In this... O. Muller, M.P. Cendrero mateo, H. Albrecht, F. Pinto, M. Mueller-linow, R. Pieruschka, U. Schurr, U. Rascher, A. Schickling, B. Keller

324. Field Potential Soil Variability Index to Identify Precision Agriculture Opportunity

Precision agriculture (PA) technologies used for identifying and managing within-field variability are not widely used despite decades of advancement. Technological innovations in agronomic tools, such as canopy reflectance or electrical conductivity sensors, have created opportunities to achieve a greater understanding of within-field variability. However, many are hesitant to adopt PA because uncertainty exists about field-specific performance or the potential return on investment. These co... C.W. Bobryk, M. Yost, N. Kitchen

325. Field Sampling and Electrochemical Detection of Nitrate in Agricultural Soils

Nitrate is an essential plant nutrient and is added to farm fields to increase crop yields. While the addition of nitrate is important for production, over-fertilization with nitrate can lead to leaching and contamination of water bodies. Increased nitrate loading in water sources then leads to eutrophication and hypoxia in downstream regions. Many efforts are being made to accurately control nitrate fertilizer additions to fields. Here, we present a soil sampling device that directly samples... J. Brockgreitens, M. Bui, A. Abbas, D. Mulla

326. Field Tests and Improvement of Sensor and Control Interface Modules with Improved Compatibility for Greenhouses

Number of greenhouses has been increased in many countries to control the cultivation conditions and improve crop yield and quality. Recently, various sensors and control devices, and also wireless communication tools have been adopted for efficient monitoring and control of the greenhouse environments. However, there have been farmers’ demands for improved compatibility among the sensors and control devices. In the study, sensor and control interface modules with improved compatibility... K. Han, S. Chung

327. Field Validation of Airblast Spray Advisor Decision Support Web App for Citrus Applications

Field conditions influencing the effectiveness of pesticide application in orchard and vineyard production systems are complex. As a result, growers and pesticide applicators grapple with how to make the right decisions for setting up the sprayer that will lead to the most efficient and effective outcomes. Airblast Spray Advisor, a decision support web app built on MATLAB was designed to assist with planning and evaluation of such applications when using airblast sprayers. It re... P.A. Larbi

328. Field-level Zoning at Regional Scale Using Remote Sensing and GIS: Lessons Learned from the Desert Agriculture Region of Southern California

A decision support tool, SAMZ-Desert, utilizing GIS and remote sensing techniques, was created to delineate management zones (MZs) for a total of 6852 fields in California's Imperial County. Landsat-8 NDVI data from April 27, 2018, was employed for this purpose. Furthermore, 11 cloud-free images captured between 2018 and 2020 were statistically analyzed to assess within-field NDVI variability and the temporal stability of MZs at the regional level. Approximately 37% of the fields in the r... A.K. Verdi, A. Garg, A. Sapkota

329. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor Algorithm

Nitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as ... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan

330. Finnish Future Farm Speeding Up the Uptake of Precision Agriculture

The Finnish Future Farm (FFF) is an innovative concept that seamlessly integrates a physical Smart Farm with a Digital Twin, complemented by educational programs and business development opportunities. This holistic approach aims to propel the evolution of Smart Agriculture in Finland. At its core, FFF is a platform for co-creation with a strong emphasis on User-Centered Design. It employs a Multi-Actor Approach, bringing together companies, experts, researchers, and end users to co... H.E. Haapala

331. First Experiences with the European Remote Sensing Satellites Sentinel-1A/ -2A for Agricultural Research

The Copernicus program headed by the European Commission (EC) in partnership with the European Space Agency (ESA) will launch up to twelve satellites, the so called “Sentinels” for earth and environmental observations until 2020. Within this satellite fleet, the Sentinel-1 (microwave) and Sentinal-2 (optical) satellites deliver valuable information on agricultural crops. Due to their high temporal (5 to 6 days repeating time) and spatial (10 to 20 m) resolutions a continuous monit... H. Lilienthal, H. Gerighausen, E. Schnug

332. FOODIE Data Model for Precision Agriculture

The agriculture sector is a unique sector due to its strategic importance for both citizens (consumers) and economy (regional and global), which ideally should make the whole sector a network of interacting organizations. The FOODIE project aims at building an open and interoperable agricultural specialized platform hub on the cloud for the management of spatial and non-spatial data relevant for farming production. The FOODIE service platform deals with including their thematic, spatial, and ... K. Charvat, T. Reznik, K. Charvat jr., V. Lukas, S. Horakova, M. Kepka

333. Fostering Student Engagement and Leadership Development in Integrative Precision Agriculture Across Borders

Efforts to advance integrative precision agriculture technologies are growing exponentially across the globe with the common interest of upholding food security and developing more sustainable food and fiber production systems. Countries such as the United States and Brazil are among the biggest crop producers in the world and will play an even bigger role in food security in the next decades. It is of utmost importance that countries can advance together to overcome future food production ch... L. Lacerda, A. Felipe dos santos, E. Bedwell, A. Jakhar, T.O. Costa barboza, M. Ardigueri

334. From Fragmented Data to Unified Insights: Leveraging Data Standardization Tools for Better Collaboration and Agronomic Big Data Analysis

The quantity and scope of agronomic data available for researchers in both industry and academia is increasing rapidly. Data sources include a myriad of different streams, such as field experiments, sensors, climatic data, socioeconomic data or remote sensing. The lack of standards and workflows frequently leads agronomic data to be fragmented and siloed, hampering collaboration efforts within research labs, university departments, or research institutes. Researchers and businesses therefore ... S. Sela

335. From Scientific Literature to the End User: Democratizing Access to Data Products Through Interactive Applications

In recent years, the sustained advance in the creation of powerful programming libraries is allowing not only the creation of complex models with predictive capabilities but also revolutionizing visualization processes and the deployment of interactive applications. Some of these tools, such as Streamlit or Shiny frameworks in languages such as Python or R, allow us to create from simple applications with friendly interfaces to complex tools. These interactive digital decision dashboards allo... C. Hernandez, A. Correndo, J. Lacasa, P. Magalhaes cisdeli, G.N. Nocera santiago, I. Ciampitti

336. Fruit Fly Electronic Monitoring System

Insects are a constant threat to agriculture, especially the cultivation of various types of fruits such as apples, pears, guava, etc. In this sense, it is worth mentioning the Anastrepha genus flies (known as fruit fly), responsible for billionaire losses in the fruit growing sector around the world, due to the severity of their attack on orchards. In Brazil, this type of pests has been controlled in most product areas by spraying insecticides, which due to the need for prior knowledge regar... C.L. Bazzi, F.V. Silva, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, R.S. Dos santos, A.M. Hachisuca, F. Franz

337. Functional Soil Property Mapping with Electrical Conductivity, Spectral and Satellite Remote Sensors

Proximal electrical conductivity (EC) and spectral sensing has been widely used as a cost-effective tool for soil mapping at field scale. The traditional method of calibrating proximal sensors for functional soil property prediction (e.g., soil organic matter, sand, silt, and clay contents) requires the local soil sample data, which results in a field-specific calibration. In this large-scale study consisting of 126 fields, we found that the traditional local calibration method had suffered w... X. Xiong, D. Myers, J. Debruin, B. Gunzenhauser, N. Sampath, D. Ye, H. Underwood, R. Hensley

338. Fungicide Application Methods and Corn Variety Effect on Corn Silage Deoxynivalenol Levels

Mycotoxin contamination is a major challenge for dairy producers. Deoxynivalenol, (DON) a mycotoxin produced by the fungus Fusarium graminearum, can infect both the corn stalk and ear. Studies have found that 86% of corn silage samples have some concentration of DON. Deoxynivalenol causes major issues in the dairy industry causing decreased milk production, lower components, higher SCC, and decreased reproductive performance. The objective of this research project was to dete... J.M. Hartschuh, R. Minyo

339. Gamma-ray Spectrometry to Determine Soil Properties for Soil Mapping in Precision Agriculture

Soil maps are critical for various land use applications and form the basis for the successful implementation of precision agriculture in crop production. Soil maps provide the spatial distribution of important soil physical and chemical properties to a farmer. The farmer uses this information to make critical management decisions for profitable and sustainable food production. South Africa is a water scarce country where rainfall is mainly seasonal and unreliable. Under these circumstances, ... J.G. Dreyer, L. Ameglio

340. Generation of Site-specific Nitrogen Response Curves for Winter Wheat Using Deep Learning

Nitrogen response (N-response) curves are tools used to support farm management decisions. Conventionally, the N-response curve is modeled as an exponential function that aims to identify an important threshold for a given field: the economic optimum point. This is useful to determine the nitrogen rate beyond which there is no actual profit for the farmers. In this work, we show that N-response curves are not only field-specific but also site-specific and, as such, economic optimum points sho... G. Morales, J.W. Sheppard, A. Peerlinck, P. Hegedus, B. Maxwell

341. Generative Modeling Method Comparison for Class Imbalance Correction

An image dataset, for use in object detection of hay bales, with over 6000 images of both good and bad hay bales was collected.  Unfortunately, the dataset developed a class imbalance, with more good bale images than bad bales.  This dataset class imbalance caused the bad bale class to over train and the good bale class to under train, severely impacting precision, and recall.  To correct this imbalance and provide a comparison of differing generative modeling methods; three di... B. Vail, Z. Oster, B. Weinhold

342. Geographic Database in Precision Agriculture for the Development of AI Research

Agriculture 4.0 has profoundly transformed production processes by incorporating technologies such as Precision Agriculture, Artificial Intelligence, the Internet of Things, and telemetry. This evolution has enabled more accurate and timely decision-making in agriculture. In response to this movement, the Precision Agriculture Laboratory (AgriLab) of UTFPR, located in Medianeira, proposes the establishment of a consistent and standardized database. This database is continually updated with su... E.N. Avila, C.L. Bazzi, W.K. Oliveira, K. Schenatto, R. Sobjak, D.M. Rocha

343. Global Adoption of Precision Agriculture: an Update on Trends and Emerging Technologies

The adoption of precision agriculture (PA) has been mixed. Some technologies (e.g., Global Navigation Satellite System (GNSS) guidance) have been adopted rapidly worldwide wherever there is mechanized agriculture. Adoption of some of the original PA technologies introduced in the 1990s has been modest almost everywhere (e.g., variable rate fertilizer). New and more advanced technologies based on robotics, uncrewed aerial vehicles (UAVs), machine vision, co-robotic automation, and artificial i... J. Mcfadden, B. Erickson, J. Lowenberg-deboer, G. Milics

344. Grassland System Impacts on Spatial Variability of Soil Phosphorus in Eastern Canada

Phosphorus (P) is an essential nutrient for plants, including grasslands. However, continuous applications of P fertilizer result in P accumulations in the soil, increasing the risk of P losses through runoff and erosion. Since 2008, more than 31 million tonnes of organic fertilizers, representing more than 95,000 tonnes of P2O5, were applied to agricultural fields in Eastern Canada. Thus, grassland systems were fertilized intensively using organic fertilizers with high ... J.D. Nze memiaghe, A. Cambouris

345. Greenhouse Study to Identify Glyphosate-resistant Weeds Based on Canopy Temperature

Development of herbicide-resistant crops has resulted in significant positive changes to agronomic practices, while repeated and intensive use of herbicides with the same mechanisms of action has caused the development of herbicide-resistant weeds. As of 2015, 35 weed species are reported to be resistant to glyphosate worldwide. A greenhouse study was conducted to identify characteristics which can be helpful in field mapping of glyphosate resistant weeds by using UAV imagery. The experiment ... A. Shirzadi, M. Maharlooei, O. Hassanijalilian, S. Bajwa, K. Howatt, S. Sivarajan, J. Nowatzki

346. Growth Analysis on Cotton Using Unoccupied Aerial Systems (UAS) Based Multi-temporal Canopy Features

The use of Unoccupied Aerial Systems (UAS) is rapidly evolving to generate imagery to determine crop growth patterns. A field experiment was conducted with thirty cotton varieties in 2016 and forty-two cotton varieties in 2021. The main objectives were (i) to perform growth analysis by using Canopy Cover (CC) and Canopy Height (CH) measurements obtained from UAS, (ii) to extract growth parameters from CC and CH data, (iii) to assess the relationship between the yield of co... S. Palla, M. Bhandari

347. Hardware Design, Validation & Integration of Wireless Data Communication Platform for Site Specific Liquid Application System

Autonomous farming applications require real-time data handling of information gathered by diverse sensors on the platform. Transmitting dynamic information swiftly is crucial, but currently available systems often lack this capability, resulting in data loss. An urgent need exists for an instant wireless communication platform to capture, relay, and process data efficiently to the central hub for further processing. This study focuses on the development of a wireless data... K. Shende, A. Sharda

348. Harnessing Farmers’, Researchers’ and Other Stakeholders’ Knowledge and Experiences to Create Shared Value from On-farm Experimentation: Lessons from Kenya

Achieving greater sustainability in farm productivity is a major challenge facing smallholder farmers in Kenya. Existing technologies have not solved the challenges around declining productivity because they are one-size-fits-all that doesn’t account for the diverse smallholder contexts. A study was carried out in Kenya by a multi-disciplinary team to assess the value of On-Farm Experimentation (OFE) to tailor technologies to local conditions. The OFE process begun with identification o... J. Muthamia, I. Adolwa, J. Mutegi, S. Zingore, S. Phillips

349. Have Your Steak and Eat It Too: Precision Beef Management to Simultaneously Reduce Ech4 and Increase Profit

Achieving carbon net zero is a clear priority, with beef farmers under significant scrutiny from food system stakeholders. Tools are available to assess greenhouse gas emissions (GHGe), yet adoption is low, and producers are not currently financially incentivised to change management practices. This study used cattle performance data from a commercial beef operation to model the optimal age and weight at slaughter to maximise profit and reduce enteric methane (eCH4) emissions at th... K. Behrendt, J. Capper, L. Ford, E.W. Harris

350. Hay Yield Estimation Using UAV-based Imagery and a Convolutional Neural Network

Yield monitoring systems are widely used commercially in grain crops to map yields at a scale of a few meters. However, such high-resolution yield monitoring and mapping for hay and forage crops has not been commercialized. Most commercial hay yield monitoring systems only obtain the weight of individual bales, making it difficult to map and understand the spatial variability in hay yield. This study investigated the feasibility of an unmanned aerial vehicle (UAV)-based remote sensing system ... K. Lee, K.A. Sudduth, J. Zhou

351. Helvis - a Small-scale Agricultural Mobile Robot Prototype for Precision Agriculture

The use of agricultural robots is emerging in a complex scenario where it is necessary to produce more food to feed a crescent population, decrease production costs, fight plagues and diseases, and preserve nature. Around the world, there are many research institutes and companies trying to apply mobile robotics techniques in agricultural fields. Mostly, large prototypes are being used and their shapes and dimensions are very similar to tractors and trucks. In the present study, a small-scale... M. Becker, A.E. Velasquez, H.B. Guerrero, V.A. Higuti, D.M. Milori, D.V. Magalhães

352. Hierarchical Zoning: Targeted Sampling for Soil Attribute Mapping

The mapping of soil attributes for fertilizer recommendation remains challenging in precision agriculture. Traditionally, this mapping is done through soil sampling in a regular grid, which generally yields good results when done in denser grids. However, due to the high costs associated with sampling and analysis, sparser grids have been adopted, which has not produced good prediction results. Some studies with directed sampling points to obtain more accurate soil maps have been adopted to a... D.D. Melo, I.A. Da cunha, T.L. Brasco, H. Oldoni, L.R. Amaral

353. High Capacity System for Precision Agriculture Reconnaissance and Intelligence

Icaros-Demeter has developed a lightweight, compact remote sensing system with a potential for producing 100,000 acre (400km-2) thematic maps per day with high resolution digital RGB/CIR CMOS sensors. The Icaros- Demeter system enables fast, precise location of multiple area and spots types. The system’s ability for producing high precision Digital Surface Models (DSM) over vast areas, offers a direct method for computing agricultural biomass via volume calculations, instead of common i... E. Ram, M. Shechter, E. Sela

354. High Resolution 3D Hyperspectral Digital Surface Models from Lightweight UAV Snapshot Cameras – Potentials for Precision Agriculture Applications

Precision agriculture applications need timely information about the plant status to apply the right management at the right place and the right time. Additionally, high-resolution field phenotyping can support crop breeding by providing reliable information for crop rating. Flexible remote sensing systems like unmanned aerial vehicles (UAVs) can gather high-resolution information when and where needed. When combined with specialized sensors they become powerful sensing systems. Hyp... H. Aasen

355. High Resolution Hyperspectral Imagery to Assess Wheat Grain Protein in a Farmer's Field

The agricultural research sector is working to develop new technologies and management knowledge to sustainably increase food productivity, to ensure global food security and decrease poverty. Wheat is one of the most important crops into this scenario, being among the three most important cereal commodities produced worldwide. Precision Agriculture (PA) and specially Remote Sensing (RS) technologies have become in the recent years more affordable which has improved the availability and flexi... F.A. Rodrigues jr., I. Ortiz-monasterio, P.J. Zarco-tejada, F.H. Toledo, U. Schulthess, B. Gérard

356. High Resolution Vegetation Mapping with a Novel Compact Hyperspectral Camera System

The COSI-system is a novel compact hyperspectral imaging solution designed for small remotely piloted aircraft systems (RPAS). It is designed to supply accurate action and information maps related to the crop status and health for precision agricultural applications. The COSI-Cam makes use of a thin film hyperspectral filter technology which is deposited onto an image sensor chip resulting in a compact and lightweight instrument design. This paper reports on the agricultural monitor... B. Delauré, P. Baeck, J. Blommaert, S. Delalieux, S. Livens, A. Sima, M. Boonen, J. Goffart, G. Jacquemin, D. Nuyttens

357. High Throughput Phenotyping of the Energy Cane Crop UAV-based LiDAR, Multispectral and RGB Data

Energy cane is a hybrid of sugarcane cultivated for their high biomass and fiber instead of sugar. It is used for production of biofuels and as feedstock for animals. As a relatively new crop, accurate knowledge of biophysical parameters such as height and biomass of different genotypes are pertinent to cultivar development. Such knowledge is also crucial to manage crop health, understand response to environmental effects, optimize harvest schedules, and estimate bioenergy yield. Nonetheless,... B. Ghansah, I. Khuimphukhieo, J.L. Scott, M. Bhandari, J. Foster, J. Da silva, H. Li, M. Starek

358. High-resolution Mapping with On-the-go Soil Sensor and Its Relation with Corn Yield and Soil Acidity in a Dystrophic Red Oxisol

Spatial representations of soil attributes with low resolution can lead to gross errors of recommendation and compromise the efficiency of soil corrections and consequently the grain yield. However, obtaining the spatial variability of soil attributes with high resolution by soil sampling is not recommended because of its large time spent and high cost of laboratory analysis what makes difficult their large-scale application. This way, the on-the-go soil sensing has been used in precision agr... G.M. Corassa, T.J. Amado, R.A. Schwalbert, G.B. reimche, D. Dalla nora, T. . horbe, F.M. tabaldi

359. HOPSY: Harvesting Optimization for Production of Strawberry Using Real-time Detection with YOLOv8

Optimizing the harvesting process presents a continuous challenge within the strawberry industry, especially during peak seasons when precise labor allocation becomes critical for efficiency and cost-effectiveness. The conventional method for addressing this issue has been hindered by an absence of real-time data regarding yield distribution, resulting in less-than-ideal worker assignments and unnecessary expenditures on labor. In response, a novel, portable, real-time strawberry detection sy... Z. Huang, W. Lee, N. Takkellapati

360. How Digital is Agriculture in South America? Adoption and Limitations

A rapidly growing population in a context of land and water scarcity, and climate change has driven an increase in healthy, nutritious, and affordable food demand while maintaining the current cropping area. Digital agriculture (DA) can contribute solutions to meet the demands in an efficient and sustainable way. South America (SA) is one of the main grain and protein producers in the world but the status of DA in the region is unknown. This article presents the results from a systematic revi... G. Balboa, L. Puntel, R. Melchiori, R. Ortega, G. Tiscornia, E. Bolfe, A. Roel, F. Scaramuzza, S. Best, A. Berger, D. Hansel, D. Palacios

361. How Does an Autonomous Tractor See the World

... G. Bansal

362. Hyperspectral Imaging to Measure Pasture Nutrient Concentration and Other Quality Parameters

Managing pasture nutrient requirements on large hill country sheep and beef properties based on information from soil sampling is expensive because of the time and labor involved. High levels of error are also expected as these properties are often greatly variable and it is therefore extremely difficult to sample intensively enough to capture this variation. Extensive sampling was also not considered viable as there was no effective means of spreading fertilizer with a variable rate capabili... I.J. Yule, R.R. Pullanagari, G. Kereszturi, M.E. Irwin, P.J. Mcveagh, T. Cushnahan, M. White

363. Hyperspectral Sensing to Estimate Soil Nitrogen and Reduce Soil Sampling Intensity

Recognizing soil's critical role in agriculture, swift and accurate quantification of soil components, specifically nitrogen, becomes paramount for effective field management. Traditional laboratory methods are time-consuming, prone to errors, and require hazardous chemicals. Consequently, this research advocates the use of non-imaging hyperspectral data and VIS-NIR spectroscopy as a safer, quicker, and more efficient alternative. These methods take into account various soil components, i... W.A. Admasu, D. Mandal, R. Khosla

364. I Call Shotgun: Uncovering Human-System/Robot Gaps in Emerging Technologies

... Y. Salzer

365. Identifying Critical Landscape Areas for Precision Conservation in the Minnesota River Basin

The Minnesota River Basin generates a disproportionately high amount of total suspended sediments to the Upper Mississippi River Basin. Many reaches in the Minnesota River Basin have impaired water quality due to turbidity. Critical landscapes can be divided into depressional areas, riparian areas, highly erodible lands, and areas susceptible to ephemeral gullies or ravines. Geographic Information Systems (GIS) were utilized, and terrain analysis was conducted using digital elevation models i... J. Galzki, J. Nelson, D. Mulla

366. Identifying Key Factors Influencing Yield Spatial Pattern and Temporal Stability for Management Zone Delineation

Management zone delineation is a practical strategy for site-specific management. Numerous approaches have been used to identify these homogenous areas in the field, including approaches using multiple years of historical yield maps. However, there are still knowledge gaps in identifying variables influencing spatial and temporal variability of crop yield that should be used for management zone delineation. The objective of this study is to identify key soil and landscape properties affecting... L.N. Lacerda, Y. Miao, K. Mizuta, K. Stueve

367. Impact of Cover Crop and Soil Apparent Electrical Conductivity on Cotton Development and Yield

Cotton is one of the major crops in the New Madrid Seismic Zone (NMSZ) of the U.S. Lower Mississippi River Valley region. Because cotton production doesn’t leave a lot of crop residue in the field, low soil organic matter levels are common. While the benefits of crop rotation are well known, cotton is often grown year after year in the same fields for economic reasons. Soils in the region are generally quite variable, with areas of very high sand content. Winter cover crops and reduced ... E. Vories, K. Veum, K. Sudduth

368. Impacts of Interpolating Methods on Soil Agri-environmental Phosphorus Maps Under Corn Production

Phosphorus (P) is an essential nutrient for crops production including corn. However, the excessive P application, tends to P accumulation at the soil surface under crops systems. This may contribute to increase water and groundwater pollution by surface runoff. To prevent this, an agri-environmental P index, (P/Al)M3, was developed in Eastern Canada and USA. This index aims to estimate soil P saturation for accurate P fertilizer recommendations, while integrating agronomical aspec... J. Nze memiaghe, A.N. Cambouris, N. Ziadi, M. Duchemin, A. Karam

369. Implementation of Autonomous Material Re-filling Using Customized UAV for Autonomous Planting Operations

This project introduces a groundbreaking use case for customized Unmanned Aerial Vehicles (UAVs) in precision agriculture, focused on achieving holistic autonomy in agricultural operations through multi-robot collaboration.  Currently, commercially available drones for agriculture are restrictive in achieving collaborative autonomy with the growing number of unmanned ground robots, limiting their use to narrow and specific tasks.  The advanced payload capacities of multi-rotor UAVs,... V. Muvva, H. Mwunguzi, S. Pitla, K. Joseph

370. Improving Site-specific Nutrient Management in the Southeastern US: Variable-rate Fertilization Based on Yield Goal by Management Zone

Site-specific nutrient management is a critical aspect of row crop production, especially when aiming to achieve improved yields in the highly variable fields in the Southeastern United States. Variable-rate (VR) fertilizer application is a common practice to implement site-specific nutrient management and relies heavily on the use of precision soil sampling methods (grid or zone) to obtain accurate information on spatial nutrient variability within the fields. Most fields in the southeastern... S. Virk, T. Colley, C. Kamerer, G. Harris, D. Beasley

371. Improving Winter Wheat Nitrogen Status Monitoring Using Proximal Canopy Sensing and Agrometeorological Information with Machine Learning

Timely and accurate diagnosis of winter wheat nitrogen (N) status plays an important role in guiding precision N management. This study aims to combine proximal canopy sensing and agrometeorological information to establish a reliable winter wheat plant N concentration (PNC) monitoring model with seven machine learning (ML) algorithms (Random Forest Regression (RFR), Support Vector Regression (SVR), K-Nearest Neighbors Regression (KNNR), Partial Least Squares Regression (PLSR), Gradient Boost... X. Chen, Y. Miao, K. Yu, Q. Chang, F. Li

372. In Season Estimation of Barley Biomass with Plant Height Derived by Terrestrial Laser Scanning

The monitoring of plant development during the growing season is a fundamental base for site-specific crop management. In this regard, the amount of plant biomass at a specific phenological stage is an important parameter to evaluate the actual crop status. Since biomass is directly only determinable with destructive sampling, methods of recording other plant parameters, such as crop height or density, which are suitable for reliable estimations are increasingly researched. Over the past two ... N. Tilly

373. In-Field and Loading Crop: A Machine Learning Approach to Classify Machine Harvesting Operating Mode

This paper addresses the complex issue of classifying mode of operation (active, idle, stationary unloading, on-the-go unloading, turning) and coordinating agricultural machinery. Agricultural machinery operators must operate within a limited time window to optimize operational efficiency and reduce costs. Existing algorithms for classifying machinery operating modes often rely on heuristic methods. Examples include rules conditioned on machine speed, bearing angle and operational t... D. Buckmaster, J. Krogmeier, J. Evans, Y. Zhang, M. Glavin, D. Byrne, S.J. Harkin

374. In-field Plant Phenotyping Using Multi-view Reconstruction: an Investigation in Eggplant

Rapid methods for plant phenotyping are a growing need in agricultural research to help accelerate improvements in crop performance in order to facilitate more efficient utilization of plant genome sequences and the corresponding advancements in associated methods of genetic improvement. Manual plant phenotyping is time-consuming, laborious, frequently subjective, and often destructive. There is a need for building field-deployable systems with advanced sensors that have both high-speed and h... T. Nguyen, D. Slaughter, B. Townsley, L. Carriedo, J. Maloof, N. Sinha

375. In-field Variability of Terrain and Soils in Southeast Kansas: Challenges for Effective Conservation

A particular challenge for crop production in southeast Kansas is the shallow topsoil, underlain with a dense, unproductive clay layer. Concerns for topsoil loss have shifted production systems to reduced tillage or conservation management practices. However, historical erosion events and continued nutrient and sediment loss still limit the productive capacity of fields. To improve crop production and further adoption of conservation practices, identification of vulnerable areas of fields was... G.F. Sassenrath, T. Mueller, V.J. Alarcon, S.E. Kulesza, D. Shoup

376. In-season Diagnosis of Corn Nitrogen and Water Status Using UAV Multispectral and Thermal Remote Sensing

For irrigated corn fields, how to optimize nitrogen (N) and irrigation simultaneously is a great challenge. A promising strategy is to use remote sensing to diagnose corn N and water status during the growing season, which can then be used to guide in-season variable rate N application and irrigation management. The objective of this study was to evaluate the effectiveness of UAV multispectral and thermal remote sensing in simultaneous diagnosis of corn N and water status. Two field experimen... Y. Miao, A. Kechchour, V. Sharma, A. Flores, L. Lacerda, K. Mizuta, J. Lu, Y. Huang

377. In-season Diagnosis of Rice Nitrogen Status Using Crop Circle Active Canopy Sensor and UAV Remote Sensing

Active crop canopy sensors have been used to non-destructively estimate nitrogen (N) nutrition index (NNI) for in-season site-specific N management. However, it is time-consuming and challenging to carry the hand-held active crop sensors and walk across large paddy fields. Unmanned aerial vehicle (UAV)-based remote sensing is a promising approach to overcoming the limitations of proximal sensing. The objective of this study was to combine unmanned aerial vehicle (UAV)-based remote sensing sys... J. Lu, Y. Miao, Y. Huang, W. Shi

378. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather Data

Nitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by c... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia

379. In-season Nitrogen Management for Wheat in Tunisia Using Proximal and Remote Sensing

While the cereal sector represents an important factor in the social and economic farming structure in Tunisia, the national wheat average yield is very low, estimated to 1.4 t/ha. However, the frequent spreading of nitrogen in large quantities to raise yields can lead to low use efficiency of N and groundwater pollution. In Sweden, digital tools using proximal and remote sensing for variable rate application (VRA) of nutrients were developed and widely used by farmers to optimize fertilizati... M. Mechri, O. Alshihabi, H. Angar, I. Nouiri, M. Soderstrom, K. Persson, S. Phillips

380. In-season Nitrogen Management of Maize Based on Nitrogen Status and Lodging Risk Prediction

Development of effective precision nitrogen (N) management strategies is crucially important for food security and sustainable development. Lodging is one of the major constraints to increasing maize yield that can be induced by strong winds, and is also influenced by management practices, like N rate. When making in-season N application decisions, lodging risk should be considered to avoid yield loss. Little has been reported on in-season N management strategies that also incorporate lodging... R. Dong, Y. Miao, X. Wang

381. In-Season Nitrogen Management: Leveraging Data Visualization for Precision Agriculture

The agricultural sector nitrogen management-related research has been extensively high by experiencing a data revolution, with an increasing influx of information from diverse sources like sensors, satellites, and Unmanned Aerial Vehicles (UAVs) imaging technologies. In this context, effective in-season nitrogen data management has become a critical factor; however, the ability of farmers to visualize the impact of such technologies in field research settings has been limited. This ... C. Narayana, S. vanderplas, K.J. Bathke, J.D. Luck

382. In-season Nitrogen Prediction Evaluation Using Airborne Imagery with AI Techniques in Commercial Potato Production

In modern agriculture, timely and precise nitrogen (N) monitoring is essential to optimize resource management and improve trade benefits. Potato (Solanum tuberosum L.) is a staple food in many regions of the world, and improving its production is inevitable to ensure food security and promote related industries. Traditional methods of assessing nitrogen are labour-intensive, time-consuming, and require subjective observations. To address these limitations, a combination of multispec... B. Javed, A. Cambouris, M. Duchemin, L. Longchamps, P.S. Basran, S. Arnold, A. Fenech, A. Karam

383. Incorporating Return on Investment for Profit-driven Management Zones

Adopting site-specific management practices such as profitability zones can help to stabilize long-term profit while also favoring the environment. Profitability maps are used to standardize data by converting variables into economic values ($/ha) for different cropping systems within a field. Thus, profitability maps can be used to define management zones from several years of data and show the regions within a field which are more profitable to invest in for production, or those that can be... A.A. Boatswain jacques, A.B. Diallo, A. Cambouris, E. Lord, E. Fallon

384. Increasing Precision Irrigation Efficacy for Row Crop Agriculture Through the Use of Artificial Intelligence

The agricultural sector is the largest consumer of the world’s available fresh water resources. With fresh water scarcity increasing worldwide, more efficient use for irrigation water is necessary. Precision irrigation is described as the application of water to meet crop needs of a specific area, at the right amount and at the time that is optimum for crop health and management objectives. Irrigation becomes increasingly efficient through the use of precision irrigation tools. Howe... E. Bedwell

385. Increasing the Accuracy of UAV-Based Remote Sensing Data for Strawberry Nitrogen and Water Stress Detection

This paper presents the methods to increase the accuracy of unmanned aerial vehicles (UAV)-based remote sensing data for the determination of plant nitrogen and water stresses with increased accuracy. As the demand for agricultural products is significantly increasing to keep up with the growing population, it is important to investigate methods to reduce the use of water and chemicals for water conservation, reduction in the production cost, and reduction in environmental impact. UAV-based r... S. Bhandari, A. Raheja

386. Increasing the Resilience and Performance of AI-based Services Through Hybrid Cloud Infrastructures and the Use of Mobile Edge in Agriculture

Agriculture, as an essential part of food production, belongs to the Critical Infrastructures (CRITIS). Accordingly, the systems used must be designed for fail-safe operation. This also applies to the software used in agricultural operations, which must meet security and resilience criteria. However, there is an increase in software that requires a permanent Internet connection, i.e., a stable connection to servers or cloud applications is required for operation. This represents a significant... D. Eberz-eder

387. Influence of Ground Control Points and Processing Parameters on UAS Image Mosaicking for Plant Height Estimation

Digital surface models (DSMs) and 3D point clouds, generated using overlapping images from unmanned aircraft systems (UASs), are often used for plant height estimation in phenotyping and precision agriculture. This study examined the effects of the quantity and placement of ground control points (GCPs) and image processing parameters on the creation of DSMs and 3D point clouds for plant height estimation. A 2-ha field containing multiple experimental plots with four crops (corn, cotton, ... C. Yang, H. Zhao, W. Guo, J. Zhang, C. Suh, B.K. Fritz

388. Influence of Potassium Variability on Soybean Yield

Due to its role as a plant essential nutrient, Potassium (K) serves as a fundamental component for plant growth. Soybeans are heavily reliant upon this nutrient for root growth and the production of pods, so much so that after nitrogen, potassium is the second most in-demand nutrient. Much of the overall soybean crop grown in Oklahoma is not managed with the fertility of K directly in mind. However, as the potential and expectation for greater yield increases, so does interest from produ... J. Derrick, S. Akin, R. Sharry, B. Arnall

389. Integrated Analysis of Multilayer Proximal Soil Sensing Data

Data revealing spatial soil heterogeneity can be obtained in an economically feasible manner using on-the-go proximal soil sensing (PSS) platforms. Gathered georeferenced measurements demonstrate changes related to physical and chemical soil attributes across an agricultural field. However, since many PSS measurements are affected by multiple soil properties to different degrees, it is important to assess soil heterogeneity using a multilayer approach. Thus, analysis of multiple layers of geo... V.I. Adamchuk, N. Dhawale, A. Biswas, S. Lauzon‎, P. Dutilleul

390. Integrated Approach to Site-specific Soil Fertility Management

In precision agriculture the lack of affordable methods for mapping relevant soil attributes is a funda­mental problem. It restricts the development and application of advanced models and algorithms for decision making. The project “I4S - Integrated System for Site-Specific Soil Fertility Management” combines new sensing technologies with dynamic soil-crop models and decision support systems. Using sensors with different measurement principles improves the estimation of soil f... R. Gebbers, V. Dworak, B. Mahns, C. Weltzien, D. Büchele, I. Gornushkin, M. Mailwald, M. Ostermann, M. Rühlmann, T. Schmid, M. Maiwald, B. Sumpf, J. Rühlmann, M. Bourouah, H. Scheithauer, K. Heil, T. Heggemann, M. Leenen, S. Pätzold, G. Welp, T. Chudy, A. Mizgirev, P. Wagner, T. Beitz, M. Kumke, D. Riebe, C. Kersebaum, E. Wallor

391. Integrated Data-driven Decision Support Systems

Site-specific and data-driven decision support systems in agriculture are evolving fast with the rapid advancements in cutting-edge technologies such as Agricultural Artificial Intelligence (AgAI) and big data integration. Data driven decision support systems have the potential to revolutionize various aspects of farming, from crop monitoring and precision management decisions to the way growers interact with complex technologies. The AgAI decision support-based systems excel at ana... L.A. Puntel, P. Pellegrini, S. Joalland , J. Rattalino, L. Vitantonio

392. Integrating Collected Field Machine Vibration Data with Machine Learning for Enhanced Precision in Agricultural Operations

In this research, we provide an innovative combination of the Agricultural Vibration Data Acquisition Platform (avDAQ) with cutting-edge machine learning methods for data collecting from agricultural machinery. The avDAQ system, which has a strong connection to a GPS sensor, provides precise spatial information to the vibration data that has been collected, providing an in-depth explanation of the locations of the vibrations. The objective is to fully utilize avDAQ's potential to extract ... S. Janbazialamdari, E. Brokesh

393. Integration of Post Emergence Herbicide (PoE) with Nano-urea for Optimized Management of Weed in Indian Black Mustard (Brassica Juncea L.)

Nano-urea (NU) is gaining attention due to its environmental benefits and precise application. Unlike traditional urea fertilizers, NU is engineered at the nanoscale, which increases its efficiency and reduces environmental impacts. However, limited research has been done to evaluate the combined effect of herbicides and NU. Therefore, the overarching goal of our study is to conduct field trials to understand the optimization rates of the synergized composition of herbicide and NU. Our hypoth... B. Duary, U. Debangshi, W. Dutta, G. Jha

394. Integration of Precision Agriculture Tools for Variety Optimization and Crop Management Focused on Increasing Productivity in Sugarcane

The offer of precision agriculture tools has increased its popularity in sugarcane, clearly reading needs in the crop. However, obtaining more conclusive results presents difficulties mainly due to the deficiency in the integration of technological tools. The objective of the work is to show an efficient model of use and running of precision agriculture tools that consistently improve planning and agronomic and administrative decision-making that lead to superior results. The importance of th... C. Mosquera

395. Integration of Unmanned Aerial Systems Images and Yield Monitor in Improving Cotton Yield Estimation

The yield monitor is one of the most adopted precision agriculture technologies because it generates dense yield data to quantify the spatial variability of crop yield as a basis for site-specific management. However, yield monitor data has various errors that prevent proper interpretation and precise field management. The objective of this study was to evaluate the application of unmanned aerial systems (UAS) images in improving cotton yield monitor data. The study was conducted in a dryland... H. Gu, W. Guo

396. Interoperability As an Enabler for Principled Decision-making in Irrigation: the Precision Agriculture Irrigation Language (PAIL)

Fresh water is a scarce resource, and agriculture consumes a high fraction of it worldwide. As climate change increases the likelihood of high temperatures and droughts, irrigation becomes an increasingly attractive option for managing crop production risks. Unfortunately, and despite decades of efforts by professional associations to promote the use of a principled, data-driven approach to irrigation scheduling often called scientific irrigation scheduling (SIS), the fraction of far... R. Ferreyra, C.C. Hillyer, H.D. Fuller, B. Craker, K. Watanabe

397. Intuitive Image Analysing on Plant Data - High Throughput Plant Analysis with Lemnatec Image Processing

For digital plant phenotyping huge amounts of 2D images are acquired. This is known as one part of the phenotyping bottleneck. This bottleneck can be addressed by well-educated plant analysts, huge experience and an adapted analysis software. Automated tools that only cover specific parts of this analysis pipeline are provided. During the last years this could be changed by the image processing toolbox of LemnaTec GmbH. An automated and intuitive tool for the automated analysis of huge amount... S. Paulus, T. Dornbusch, M. Jansen

398. Investigating Spatial Relationship of Apparent Electrical Conductivity with Turfgrass and Soil Characteristics in Sand-capped Golf Course Fairways

Turfgrass quality decreases when grown on fine textured soils that are irrigated with poor quality water. As a result, sand-capping (i.e., a sand layer above existing native soil) is now considered during golf course fairway renovation and construction. Mapping spatial variability of soil apparent electrical conductivity (ECa) has recently been suggested to have applications for precision turfgrass management (PTM) in native soil fairways, but sand-capped fairways have received les... C. Straw, B. Wyatt, A.P. Smith, K. Watkins, S. Hong, W. Floyd, D. Williams, C. Garza, T. Jansky

399. Investigating the Potential of Visible and Near-infrared Spectroscopy (VNIR) for Detecting Phosphorus Status of Winter Wheat Leaves Grown in Long-term Trial

The determination of plant nutrient content is crucial for evaluating crop nutrient removal, enhancing nutrient use efficiency, and optimizing yields. Nutrient conventional monitoring involves colorimetric analyses in the laboratory; however, this approach is labor-intensive, costly, and time-consuming. The visible and near-infrared spectroscopy (VNIR) or hyperspectral non-imaging sensors have been an emerging technology that has been proved its potential for rapid detection of plant nutrient... Y. El-mejjaouy, B. Dumont, A. Oukarroum, B. Mercatoris , P. Vermeulen

400. Investigation of Automated Analysis of Snowmelt from Time-series Sentinel 2 Imagery to Inform Spatial Patterns of Spring Soil Moisture in the American Mountain West

Variable rate irrigation of crops is a promising approach for saving water whilst maintaining crop yields in the semi-arid American Mountain West – much of which is currently experiencing a mega drought. The first step in determining irrigation zones involves characterizing the patterns of spatial variation in soil moisture and determining if these are relatively stable temporally in relation to topographic features and soil texture. Characterizing variable rate irrigation zones is usua... I. Turner, R. Kerry, R. Jensen, E. Woolley, N. Hansen, B. Hopkins

401. Is Row-unit Vibration Affected by Planter Speeds and Downforce?

Row-unit vibration is an issue created mainly by planter`s opening disks and gauge-wheels contact with the ground. Variability on row-unit vibration could interfere on seed metering and delivery process, affecting crop emergence and final stand. With the amount of embedded technology present on planters, producers are being encouraged to increase planting speeds, which is also one of the main factors for row-unit vibration increasement. In this way, knowing the proper speeds, and using other ... L.P. Oliveira, B.V. Ortiz, G.T. Morata, T. Squires, J. Jones

402. Key Data Ownership, Privacy and Protection Issues and Strategies for the International Precision Agriculture Industry

Precision agriculture companies seek to leverage technology to process greater volumes of data, greater varieties of data, and at a velocity unfathomable to most. The promises of boundless benefits are coupled with risks associated with data ownership, stewardship and privacy. This paper presents some risks related to the management of farm data, in general, as well as those unique to operating in the international arena.  Examples of U.S. and international laws related to data protectio... J.K. Archer, C.A. Delgadillo, F. Shen

403. Knowledge, Skills and Abilities Needed in the Precision Ag Workforce: an Industry Survey

Precision agriculture encompasses a set of related technologies aimed at better utilization of crop inputs, increasing yield and quality, reducing risks, and enabling information flow throughout the crop supply and end-use chains.  The most widely adopted precision practices have been automated systems related to equipment steering and precise input application, such as autoguidance and section controllers.  Once installed, these systems are relatively easy for farmers and their sup... B. Erickson, D.E. Clay, S.A. Clay, S. Fausti

404. Knowledge-based Approach for Weed Detection Using RGB Imagery

A workflow was developed to explore the potential use of Phase One RGB for weed mapping in a herbicide efficacy trial in wheat. Images with spatial resolution of 0.8 mm were collected in July 2020 over an area of nearly 2000 square meters (66 plots). The study site was on a research farm at the University of Saskatchewan, Canada. Wheat was seeded on June 29, 2020, at a rate of 75 seeds per square meter with a row spacing of 30.5 cm. The weed species seeded in the trial were kochia, wild oat, ... T. Ha, K. Aldridge, E. Johnson, S.J. Shirtliffe, S. Ryu

405. Laboratory Evaluation of Two VNIR Optical Sensor Designs for Vertical Soil Sensing

Visible and near infrared reflectance spectroscopy (VNIR) is becoming an extensively researched technology to predict soil properties such as soil organic carbon, inorganic carbon, total nitrogen, moisture  for precision agriculture. Due to its rapid, non-destructive nature and ability to infer multiple soil properties simultaneously, engineers have been trying to develop proximal sensors based on the VNIR technology to enable horizontal soil sensing and mapping. Since the vertical varia... N. Wijewardane, Y. Ge

406. Lameness Detection in Dairy Cattle Using GPS and Accelerometers Wearable Sensors

Lameness significantly impacts cow health and welfare on dairy farms, yet identifying lamecows remains challenging. Wearable sensors like GPS and accelerometers show promise for automated lameness detection, but their effectiveness outdoors is still unclear. Therefore, there are gaps in understanding their applicability and the necessary features for outdoor settings. Additionally, it is uncertain whether environmental factors, such as temperature and time of day, influence their the model pe... N. Mhlongo, H. De knegt, W.F. De boer, F. Van langevelde

407. Land Cover and Crop Types Classification Using Sentinel-2A Derived Vegetation Indices and an Artificial Neural Network

Developments in remote sensing data acquisition capabilities, data processing and interpretation of ground-based, airborne and satellite observations have made it possible to couple remote sensing technologies and precision crop management systems. Land cover and crop types classification is a fundamental task in remote sensing and is crucial in various environmental and agricultural applications. Accurate and timely information on land cover and crop types is essential for land management, l... B. Bantchina

408. Large-scale UAS Data Collection, Processing and Management for Field Crop Management

North Dakota State University research and Extension personnel are collaborating with Elbit Systems of America to compare the usefulness and economics of imagery collected from a large unmanned aircraft systems (UAS), small UAS and satellite imagery. Project personnel are using a large UAS powered with an internal combustion engine to collect high-resolution imagery over 100,000 acres twice each month during the crop growing season. Four-band multispectral Imagery is also being collected twic... J. Nowatzki, S. Bajwa, D. Roberts, M. Ossowski, A. Scheve, A. Johnson, Y. Chaplin

409. Latin America and the Caribbean Regional Meeting

... R.A. Ortega

410. Leveraging UAV-based Hyperspectral Data and Machine Learning Techniques for the Detection of Powderly Mildew in Vineyards

This paper presents the development and validation of machine learning models for the detection of powdery mildew in vineyards. The models are trained and validated using custom datasets obtained from unmanned aerial vehicles (UAVs) equipped with a hyperspectral sensor that can collect images in visible/near-infrared (VNIR) and shortwave infrared (SWIR) wavelengths. The dataset consists of the images of vineyards with marked regions for powdery mildew, meticulously annotated using LabelImg.&n... S. Bhandari, M. Acosta, C. Cordova gonzalez, A. Raheja, A. Sherafat

411. Limitations of Yield Monitor Data to Support Field-scale Research

Precision agriculture adoption on farms continues to grow globally on farms.  Today, yield monitors have become standard technologies on grain, cotton and sugarcane harvesters.  In recent years, we have seen industry and even academics leveraging the adoption of precision agriculture technologies to conduct field-scale, on-farm research.  Industry has been a primary driver of the increase in on-farm research globally through the development of software to support on-farm resear... J.P. Fulton, S.A. Shearer, A. Gauci, A. Lindsey, D. Barker, E. Hawkins

412. Liquid Flow Control Requirements for Crop Canopy Sensor-Based N Management in Corn: A Project SENSE Case Study

While on-farm adoption of crop canopy sensors for directing in-season nitrogen (N) application has been slow, research focused on these systems has been significant for decades. Much emphasis has been placed on developing and testing algorithms based on sensor output to predict N needs, but little information has been published regarding liquid flow control requirements on equipment used in conjunction with these sensing systems. Addition of a sensor-based system to a standard spray rate cont... J. Luck, J. Parrish, L. Thompson, B. Krienke, K. Glewen, R.B. Ferguson

413. Long-range Bluetooth Smart Stakes and High-gain Receivers for High-density Sensing in Precision Agriculture

To achieve the goals of precision agriculture, accurate spatial-temporal soil information is needed, especially because soil properties can change within and between growing seasons. While remote sensing can provide high coverage, some soil properties must be measured in situ. Current existing industry solutions are too expensive per unit to deploy in sufficiently high density for dynamic management zones, creating a need for low-cost sensor networks.... S. Craven, C. Sandholtz, B. Mazzeo

414. LoRa Flood-messaging Sensor-data Transport

The practice of precision agriculture assumes the ability to place and monitor sensors. Remote monitoring is often employed as a means of alleviating tedious manual data gathering and recording. For remote monitoring to work, there has to be some automated means of reading sensor values and transmitting them to a basestation, someplace where the data is recorded and analyzed. If the data are recorded and analyzed at the point of sensing, some means is still required to send the results to whe... P.G. Raeth

415. Low Cost Smartphone Camera Accessory to Digitally Measure Leaf Color for Crop Nitrogen Status Assessment

Crop nitrogen (N) status is a desirable information for crop nutrition management. In addition to the traditional leaf sampling with subsequent laboratory analysis, the use of chlorophyll meters is a well-studied and accepted practice to indirectly measure crop N status. Nevertheless, chlorophyll meters are dedicated devices that still cost at least a few hundred dollars, thus being unsuitable to large scale use among low budget smallholders. Aiming to address this issue, a new low cost smart... G. Portz, S. Reusch, J. Jasper

416. Machine Learning Algorithms in Detecting Long-term Effect of Climatic Factors for Alfalfa Production in Kansas

The water levels of the Ogallala Aquifer are depleting so much that agricultural land returns in Kansas are expected to drop by $34.1 million by 2050. It is imperative to understand how frequent droughts and the contrasting rates of groundwater withdrawal and recharge are affected by climate shifts in Kansas. Alfalfa, the ‘Queen of Forages’, is a water demanding crop which supplies high nutritional feed for beef industry that offered Kansas producers a $500 million production valu... F. Nazrul, J. Kim, S. Dey, S. Palla, D. Sihi, B. Whitaker, G. Jha

417. Machine Learning Approach to Study the Effect of Weather and Proposed Climate Change Scenarios on Variability in the Ohio Corn and Soybean Yield

Climate is one of the primary factors that affects agricultural production.  Climate change and extreme weather events have raised concerns about its effect on crop yields. Climate change patterns affect the crop yield in many ways including the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days. It is important to analyze the effect of climate change on yield variability for a better understanding of the effec... R. Dhillon, G. Takoo

418. Machine Learning Model to Predict Total Nozzle Volume Delivery for Pulse Width Modulated Flow Controllers

Product flow rate in the Pulse Width Modulation (PWM) variable rate technologies depends on the duty cycle. However, the actual product flow rate at any duty cycle depends on pressure rise, stable pressure during the cycle, fall time and pressure drop across the nozzle body. The current controller does not consider the pressure drops and the estimation of actual flow during each cycle at any duty cycle cannot be estimated with capturing high-frequency pressure data. Knowledge of volume delive... S. Dua, A. Sharda

419. Machine Learning Techniques for Early Identification of Nitrogen Variability in Maize

Characterizing and managing nutrient variability has been the focus of precision agriculture research for decades. Previous research has indicated that in-situ fluorescence sensor measurements can be used as a proxy for nitrogen (N) status in plants in greenhouse conditions employing static sensor measurements. Indeed, practitioners of precision N management require determination of in-season plant N status in real-time at field scale to enable the most efficient N fertiliz... D. Mandal, R.D. Siqueira, L. Longchamps, R. Khosla

420. Machine Vision in Hay Bale Production

The goal of this project is to develop a system capable of real-time detection, pass/fail classification, and location tracking of large square hay bales under field conditions.  First, a review of past and current methods of object detection was carried out.  This led to the selection of the YOLO family of detectors for this project.  The image dataset was collected through help from our sponsor, collection of images from the K-STATE research farm, and images collected from th... B. Vail

421. Machine Vision, AI, and Robotics in Specialty Crop Production

... M. Karkee

422. Maize Seeding Rate Optimization in Iowa Using Soil and Topographic Characteristics.

The ability to collect soil, topography, and productivity information at spatial scales has become more feasible and more reliable with many advancement in precision technologies. This ability, combined with precision services and the accessibility farmers have to equipment capable implementing precision practices, has led to continued interest in making site-specific crop management decisions. The objective of this research was to utilize soil and topographic parameters to optimize seeding r... M.A. Licht, A. Lenssen, R. Elmore

423. Making Irrigator Pro an Adaptive Irrigation Decision Support System

Irrigator Pro is a public domain irrigation scheduling model developed by the USDA-ARS National Peanut Research Laboratory. The latest version of the model uses either matric potential sensors to estimate the plant’s available soil water or manual data input. In this project, a new algorithm is developed, which will provide growers and consultants with much more flexibility in how they can feed data to the model. The new version will also run with Volumetric Water Content sensors, givin... I. Gallios, G. Vellidis, C. Butts

424. Management Zone-specific N Mineralization Rate Estimation in Unamended Soil

Since nitrogen (N) mineralization from soil organic matter is governed by basic soil properties (soil organic matter content, pH, soil texture, …) that are known to exhibit strong in-field spatial variability, N mineralization is also expected to exhibit significant spatial variability at field scale. An ideal and efficient N recommendation for precision fertilization should therefore account for potential soil mineralizable N considering this spatial variability. Therefore, this study... F.Y. Ruma, M.A. Munnaf, S. De neve, A.M. Mouazen

425. Managing Soil Moisture on Turf Grass Using a Portable Wave Reflectometer

The agronomic needs of grass pose many challenges to managing irrigation on golf greens and lawns. Superintendents must keep putting greens as dry and firm as possible without allowing them to die. Commercial and residential landscapes are expected to look lush and green. But soil moisture has high spatial variability, including hot spots that can rapidly become critically low in available water. One common method of measuring soil moisture is to take core samples and assess moisture content ... D.L. Kieffer, T.S. O'connor

426. Map Whiteboard As Collaboration Tool for Smart Farming Advisory Services

Precision agriculture, a branch of smart farming, holds great promise for modernization of European agriculture both in terms of environmental sustainability and economic outlook.  The vast data archives made available through Copernicus and related infrastructures, combined with a low entry threshold into the domain of AI-technologies has made it possible, if not outright easy, to make meaningful predictions that divides  individual agricultural fields into zones where variable rat... K. Charvat, R. Berzins, R. Bergheim, F. Zadrazil, J. Macura, D. Langovskis, H. Snevajs, H. Kubickova, S. Horakova, K. Charvat jr.

427. Map@Syst – Geospatial Solutions for Rural and Community Sustainability

Map@Syst is a part of the USDA Cooperative State Research, Education and Extension Service (CSREES) eXtension online Web information service. eXtension is an educational partnership of more than 70 universities to provide online access to objective, research-based information and educational opportunities. Map@Syst is a Wiki-based Web site assembled and maintained cooperatively by geospatial technology educational specialists and practitioners. Map@Syst is a primary source of geospatial infor... P. Rasmussen, J. Nowatzki

428. Mapping Marginal Crop Land on Millions of Acres in the Canadian Prairies

Crop fields cover more than 250,000 km2 of the Canadian Prairies, and many of these contain areas of marginal soil condition that are farmed annually at a loss. Setting aside these unprofitable areas may represent savings for growers as well as reductions in GHG emissions, while restoring them with perennial vegetation could create new natural carbon sinks. There is high potential for these in-field marginal zones to act as a nature-based climate solution in Alberta, Saskatchewan and Manitoba... S. Shirtliffe, T. Ha, K. Nketia

429. Mapping Soil Health and Grain Quality Variations Across a Corn Field in Texas

Soil health is a key property of soils influencing grain yield and quality. Within-field mapping of soil health index and grain quality can help farmers and managers to adjust site-specific farm management decisions for economic benefits. A study was conducted to map within-field soil health and grain protein and oil content variations using apparent electrical conductivity (ECa) and terrain attributes as their predictors. Two hundred and two topsoil samples were analyzed to determine soil he... K. Adhikari, D.R. Smith, C. Hajda, P.R. Owens

430. Mapping Spatial Production Stability in Integrated Crop and Pasture Systems: Towards Zonal Management That Accounts for Both Yield and Livestock-landscape Interactions.

Precision farming technologies are now widely applied within Australian cropping systems. However, the use of spatial monitoring technologies to investigate livestock and pasture interactions in mixed farming systems remains largely unexplored. Spatio-temporal patterns of grain yield and pasture biomass production were monitored over a four-year period on two Australian mixed farms, one in the south-west of Western Australia and the other in south-east Australia. A production stability index ... P. Mcentee, S. Bennett, M. Trotter, R. Belford, J. Harper

431. Mapping Surface Soil Properties Using Terrain and Remotely Sensed Data in Arsanjan Plain, Southern Iran

Sustainable land management and land use planning require reliable information about the spatial distribution of the physical and chemical soil properties affecting both landscape processes and services. Spatial prediction with the presence of spatially dense ancillary variables has attracted research in pedometrics. The main objective of this research is to enhance prediction of soil properties such electrical conductivity (ECe), exchangeable sodium percentage (ESP), available phosphorus (P)... M. Baghernejad, M. Emadi

432. MDPI - Agriculture and Agronomy Journals

... N. Nišavić

433. Measurement of In-field Variability for Active Seeding Depth Applications in Southeastern US

Proper seeding depth control is essential to optimize row-crop planter performance, and adjustment of planter settings to within field spatial variability is required to maximize crop yield potential. The objectives of this study were to characterize planting depth response to varying soil conditions within fields, and to discuss implementation of active seeding depth technologies in Southeastern US. This study was conducted in 2014 and 2015 in central Alabama for non-irrigated maize (Zea may... A.M. Poncet, J.P. Fulton, T.P. Mcdonald, T. Knappenberger, R.W. Bridges, J. Shaw, K. Balkcom

434. Measuring Height of Sugarcane Plants Through LiDAR Technology

Sugarcane (Saccharum spp.) has an important economic role in Brazilian agriculture, especially in São Paulo State. Variation in the volume of plants can be an indicative of biomass which, for sugarcane, strongly relates to the yield. Laser sensors, like LiDAR (Light Detection and Ranging), has been employed to estimate yield for corn, wheat and monitoring forests. The main advantage of using this type of sensor is the capability of real-time data acquisition in a non-destructive way, p... T.F. Canata, J.P. Molin, A.F. Colaço, R.G. Trevisan, P.R. Fiorio, M. Martello

435. Measuring Pasture Mass and Quality Indices Over Time Using Proximal and Remote Sensors

Traditionally pasture has been measured or evaluated in terms of a dry matter yield estimate, which has no reference to other important quality factors. The work in this paper measures pasture growth rates on different slopes and aspects and pasture quality through nitrogen N% and metabolizable energy and ME concentration. It is known that permanent pasture species vary greatly in terms of quality and nutritional value through different stages of maturity. Pasture quality decreases as grass t... I.J. Yule, M.C. Grafton, L.A. Willis, P.J. Mcveagh

436. Measuring Soil Carbon with Intensive Soil Sampling and Proximal Profile Sensing

Soils have a large carbon storage capacity and sequestering additional carbon in agricultural fields can reduce CO2 levels in the atmosphere, helping to mitigate climate change. Efforts are underway to incentivize agricultural producers to increase soil organic carbon (SOC) stocks in their fields using various conservation practices.  These practices and the increased SOC provide important additional benefits including improved soil health, water quality and – in some cases –... E. Lund, T. Lund, C. Maxton

437. Measuring Soil Carbon with Intensive Soil Sampling and Proximal Profile Sensing

Measuring soil carbon is currently a subject of significant interest due to soil’s ability to sequester carbon and reduce atmospheric CO2. The cost of conventional soil sampling and analysis along with the number of samples required make proximal sensing an appealing option.  To properly evaluate the performance of proximal sensing of soil carbon, a detailed lab-analyzed carbon inventory is needed to serve as the ‘gold standard’ in evaluating sensor estimations.  F... E. Lund

438. Melon Classification and Segementation Using Low Cost Remote Sensing Data Drones

Object recognition represents currently one of the most developing and challenging areas of the Computer Vision. This work presents a systematic study of various relevant parameters and approaches allowing semi-automatic or automatic object detection, applied onto a study case of melons on the field to be counted. In addition it is of a cardinal interest to obtain the quantitative information about performance of the algorithm in terms of metrics the suitability whereof is determined by the f... T. Zhao, Y. Chen, J. Franzen, J. Gonzalez, Q. Yang

439. Memory Based Learning: A New Data Mining Approach to Model and Interpret Soil Texture Diffuse Reflectance Spectra

Successful estimation of spectrally active soil texture with Visible and Near-Infrared (VNIR, 400-1200 nm) and Short-Wave-Infrared (SWIR, 1200-2500 nm) spectroscopy depends mostly on the selection of an appropriate data mining algorithm. The aims of this paper were: to compare different data mining algorithms including Partial Least Squares Regression (PLSR), which is the most common technique in soil spectroscopy, Support Vector Machine Regression (SVMR), Boosted Regression Trees (BRT), and ... A. Gholizadeh, M. Saberioon, L. Borůvka

440. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild Blueberry

Deep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fie... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White

441. Method to Optimize Soil Survey for Multiple Soil Property

The sugarcane production system in Colombia, spanning an area of 241,000 hectares in the geographical valley of the Cauca River, is recognized worldwide due to its high productivity, adoption of advanced technologies, and sustainable management. The natural soil and climate conditions in this region result in significant variability in the chemical and physical soil properties. Consequently, determining the soil variability is crucial to achieving its maximum productive potential through diff... D.F. Sandoval, D.F. Perdomo

442. Micro-climate Prediction System Using IoT Data and AutoML

Microclimate variables like temperature, humidity are sensitive to land surface properties and land-atmosphere connections. They can vary over short distances and even between sections of the farm. Getting the accurate microclimate around the crop canopy allows farmers to effectively manage crop growth. However, most of the weather forecast services available to farmers globally, either by the meteorological department or universities or some weather app,  provide weather forecasts for l... A. Sharma, R.S. Jalem, M. Dash

443. Minnesota Corn Growers Association

With more than 6,500 members, the Minnesota Corn Growers Association is one of the largest grassroots farm organizations in the United States. Working in close partnership with the Minnesota Corn Research & Promotion Council, MCGA identifies and promotes opportunities for Minnesota’s 24,000 corn farmers while building connections with the non-farming public. We accomplish this by investing in third-party research that focuses on water quality and soil health, targeted consumer outre... M. Kazula

444. Misalignment Between Sugar Cane Transshipment Trailers and Tractor

Sugarcane production system is dependent on a continuous cutting and regrowth of cane plants from their roots, on which traffic should be avoided to ensure the physiological integrity of regrowth and productivity.  This need for accuracy in sugarcane machine traffic boosted the adoption of automated steering systems, especially on harvesters. Tractors with the transshipment trailers, which continually accompany the harvesters in the field, yet do not adopt it or use technology with lower... B.P. Passalaqua, J. Molin, J. Salvi, A.P. Aguilera

445. Modeling Spatial and Temporal Variability of Cotton Yield Using DSSAT for Decision Support in Precision Agriculture

The quantification of spatial and temporal variability of cotton yield provides critical information for optimizing resources, especially water. The Southern High Plains (SHP) of Texas is a major cotton (Gossypium hirsutum L.) production region with diminishing water supply. The objective of this study was to predict cotton yield variability using soil properties and topographic attributes. The DSSAT CROPGRO-Cotton model was used to simulate cotton growth, development and yield ... B.P. Ghimire, O. Adedeji, Z. Lin, W. Guo

446. Modelling Hydrological Processes in a Wadi Basin in Egypt: Wadi Kharouba Case Study

Wadi Flash Flood (WFF) is one of the most crucial problems facing the north‐western coastal region in Egypt. Water harvesting (WH) approaches may be an effective tool to reduce the WFF risk while storing the runoff water for agricultural activities. In this study, the Agarma sub-catchment of the Wadi Kharouba was taken as a reference investigation site to study terraced WA systems. The main problem in this area is that local farmers independently build terraces using traditional knowledge t... A.H. Rabia, E. Eldeeb, A. Coppola

447. Modifying the University of Missouri Corn Canopy Sensor Algorithm Using Soil and Weather Information

Corn production across the U.S. Corn belt can be often limited by the loss of nitrogen (N) due to leaching, volatilization and denitrification. The use of canopy sensors for making in-season N fertilizer applications has been proven effective in matching plant N requirements with periods of rapid N uptake (V7-V11), reducing the amount of N lost to these processes. However, N recommendation algorithms used in conjunction with canopy sensor measurements have not proven accurate in making N reco... G. Bean, N.R. Kitchen, D.W. Franzen, R.J. Miles, C. Ransom, P. Scharf, J. Camberato, P. Carter, R.B. Ferguson, F. Fernandez, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan

448. Modulated On-farm Response Surface Experiments with Image-based High Throughput Techniques for Evidence-based Precision Agronomy

Agronomic research is vital to determining optimum inputs for crops to perform profitably at a local scale. However, the small-plot experiment validity is often uncertain due to on-farm variations. Furthermore, the likelihood of conducting a fully randomized trial at a local farm is low given various practical and technical challenges. We propose a new methodology with many inputs to allow for a response surface that fits the yield response to the input levels with higher accuracy to make on-... A.U. Attanayake, E.U. Johnson, H.U. Duddu, S.U. Shirtliffe

449. Modus: a Standard for Big Data

Modus Standard is a system of defined terminology, agreed metadata and file transfer format that has grown from a need to exchange, merge and trend agricultural testing data. The three presenters will discuss steps taken to develop the system, benefits to data exchange, current user base and additions being made to the standard. ... D. Nerpel, J.W. Ellsworth, A. Hunt

450. Monitoring the Effects of Weed Management Strategies on Tree Canopy Structure and Growth Using UAV-LiDAR in a Young Almond Orchard

The primary objective of this study was to assess the potential effect of integrated weed management (IWM) on canopy structure and growth in a young almond orchard using unmanned aerial vehicle (UAV) LiDAR point cloud data. The experiment took place in the Neve Ya’ar Model Farm, with four IWM strategies tested: (1) standard herbicide-based management, (2) physical-mechanical approach, (3) cover crops, and (4) integrated weed management combining herbicide and mowing. In 2019 (pre-treatm... T. Paz kagan, R. Lati , T. Caras

451. Multi-sensor Imagery Fusion for Pixel-by-pixel Water Stress Mapping

Evaluating water stress in agricultural fields is fundamental in irrigation decision-making, especially mapping the in-field water stress variability as it allows real-time detection of system failures or avoiding yield loss in cases of unplanned water stress. Water stress mapping by remote sensing imagery is commonly associated with the thermal or the short-wave-infra-red (SWIR) bands. However, integration of multi-sensors imagery such as radar imagery or sensors with only visible and near-i... O. Beeri, R. Pelta, Z. Sade, T. Shilo

452. Multi-sensor Remote Sensing: an AI-driven Framework for Predicting Sugarcane Feedstock

Predicting saccharine and bioenergy feedstocks in sugarcane enables stakeholders to determine the precise time and location for harvesting a better product in the field. Consequently, it can streamline workflows while enhancing the cost-effectiveness of full-scale production. On one hand, Brix, Purity, and total reducing sugars (TRS) can provide meaningful and reliable indicators of high-quality raw materials for industrial food and fuel processing. On the other hand, Cellulose, Hemicell... M. Barbosa, D. Duron, F. Rontani, G. Bortolon, B. Moreira, L. Oliveira, T. Setiyono, L. Shiratsuchi, R.P. Silva, K.H. Holland

453. Multispectral Assessment of Chickpea in the Northern Great Plains

Chickpea is an increasingly important crop in the Montana agricultural system. From 2017 to 2021 the U.S. has planted an average of about 492,000 acres per year with Montana chickpea production accounting for around 44% of the U.S. total (USDA/NASS QuickStats accessed on 2/11/2021). This has led to an increase in breeding efforts for elite varieties adapted to the unique conditions in the Northern Great Plains. Breeding of chickpea often relies on traditional phenotyping techniques that are l... J.M. Vetch

454. Multispectral Imaging and Elevation Mapping from an Unmanned Aerial System for Precision Agriculture Applications

As the world population continues to grow, the need for efficient agricultural production becomes more pressing.  The majority of farmers still use manual techniques (e.g. visual inspection) to assess the status of their crops, which is tedious and subjective.  This paper examines an operational and analytical workflow to incorporate unmanned aerial systems (UAS) into the process of surveying and assessing crop health.  The proposed system has the potential to significantly red... C. Lum, M. Dunbabin, C. Shaw-feather, M. Mackenzie, E. Luker

455. N-management Using Structural Data: UAV-derived Crop Height As an Estimator for Biomass, N Concentration, and N Uptake in Winter Wheat

In the last 15 years, sensors mounted on Unmanned Aerial Vehicles (UAVs) have been intensively investigated for crop monitoring. Besides known remote sensing approaches based on multispectral and hyperspectral sensors, photogrammetric methods became very important. Structure for Motion (SfM) and Multiview Stereopsis (MVS) analysis approaches enable the quantitative determination of absolute crop height and crop growth. Since the first paper on UAV-derived crop height was published by Bendig e... G. Bareth, A. Jenal, H. Hüging

456. National Agricultural Producers Data Cooperative - Sponsor Presentation

... B.E. Craker

457. Net Returns and Production Use Efficiency for Optical Sensing and Variable Rate Nitrogen Technologies in Cotton Production

This research evaluated the profitability and N use efficiency of real time on-the-go optical sensing measurements (OPM) and variable-rate technologies (VRT) to manage spatial variability in cotton production in the Mississippi River Basin states of Louisiana, Mississippi, Missouri, and Tennessee. Two forms of OPM and VRT and the existing farmer practice (FP) were used to determine N fertilizer rates applied to cotton on farm fields in the four states. Changes in yields and N rates due to OPM... J.A. Larson, M. Stefanini, D.M. Lambert, X. Yin, C.N. Boyer, J.J. Varco, P.C. Scharf , B.S. Tubaña, D. Dunn, H.J. Savoy, M.J. Buschermohle, D.D. Tyler

458. New Technologies in Biological Plant Protection and Its Localization

The sharp increase in the use of pesticides in agrobiocenosis in the background of no-till and minimum tillage called: the growth of costs, the decline of soil fertility, the occurrence of resistance in harmful organisms and change in species composition, a number of other pressing environmental problems. In this regard, the most preferred and safe bipolarization of plant protection. The use of microorganisms in plant protection can reduce the number of harmful organisms in anthropogenic ecos... N. Sedinina, D. Kotlyarov , V. kotlyarov

459. Next in Precision Agriculture: Detecting and Correcting Pixels with Machinery Track Line Within Farms

With more satellites orbiting the earth, monitoring of fields using satellite data has become easier and ubiquitous. Frequent observations of a field can provide vital cues about field health and management practices. However, farm analytical statistics derived from such datasets often need modification to create practical applications. This paper focuses on the detection and removal of field machinery track line pixels to reduce their effect on satellite-based agronomic recommendation and pr... G. Rathee, M. Sielenkemper

460. NIR Spectroscopy to Map Quality Parameters of Sugarcane

Precision Agriculture aims to explore the potential of each crop considering the differences within the field. One information that is considered the most important is the yield or the obtained income in the field. However, in the case of sugarcane, quality will also directly influence farmer’s income. Several studies suggest harvester automation aiming to monitor yield, but few consider the quality analysis in the process. Among the existing methods for measuring sugar content the one ... M.N. Ferraz, J.P. Molin

461. Nitrogen Fertilization of Potato Using Management Zone in Prince Edward Island, Canada

Potato is sensible to nitrogen (N) and optimal N fertilization improve the tuber yield and its quality. Potato crop N response varies widely within fields. It is also well recognized that significant spatial and temporal variation in soil N availability occurs within crop fields. However, uniform application of N fertilizer is still the most common practice under potato production. Management zone (MZ) approach can help growers to achieve a part of this. The goal of the project is to compare ... A. Cambouris, M. Duchemin, N. Ziadi

462. Nitrogen Management in Lowland Rice

Rice is staple diet for more than fifty percent of the world population and nitrogen (N) deficiency is one of the major yields limiting constraints in most of the rice producing soils around the world. The lowland rice N recovery efficiency is <50% of applied fertilizers in most agro-ecological regions. The low N efficiency is associated with losses caused by leaching, volatilization, surface runoff, and denitrification. Hence, improving N use efficiency is crucial for higher yields, low c... N.K. Fageria, A.B. Santos

463. Nitrogen Placement Considerations for Maize Production in the Eastern US Cornbelt

Proper fertilizer placement is essential to optimize crop performance and amount of applied nitrogen (N) along with crop yield potential. There exists several practices currently used in both research within farming operations on how and when to apply N to maize (Zea mays L). Split applications of N in Ohio is popular with farmers and provides an economic benefit but more recently some farmers have been using mid- and late-season N fertilizer applications for their maize production.&... J.P. Fulton, E. Hawkins, S. Shearer, A. Klopfenstein, J. Hartschuh, S. Custer

464. Nitrogen Status Prediction on Pasture Fields Can Be Reached Using Visible Light UAV Data Combined with Sentinel-2 Imagery

Pasture fields under integrated crop-livestock system usually receive low or no nitrogen fertilization rates, since the expectation is that nitrogen demand will be provided by the soybean remaining straw cropped previously. However, keeping nitrogen at suitable levels in the entire field is the key to achieving sustainability in agricultural production systems. In this sense, remote sensing technologies play an essential role in nitrogen monitoring in pastures and crops. With the launch of th... F.R. Pereira, J.P. Lima, R.G. Freitas, A.A. Dos reis, L.R. Amaral, G.K. Figueiredo, R.A. Lamparelli, J.C. Pereira, P.S. Magalhães

465. Non-destructive Plant Phenotyping Using a Mobile Hyperspectral System to Assist Breeding Research: First Results

Hybrid plants feature a stronger vigor, an increased yield and a better environmental adaptability than their parents, also known as heterosis effect. Heterosis of winter oilseed rape is not yet fully understood and conclusions on hybrid performance can only be drawn from laborious test crossings. Large scale field phenotyping may alleviate this process in plant breeding. The aim of this study was to test a low-cost mobile ground-based hyperspectral system for breeding research to e... H. Gerighausen, H. Lilienthal, E. Schnug

466. North America Regional Meeting

Agenda: to discuss PA topics of common interest; to examine potential contributions of country representatives to the ISPA; to formulate suggestions to be examined by the Board for the development of the ISPA.   For your information: What do country representatives do? The intent of Country Representatives is to have champions of ISPA spread all over the world. Country Representativ... A. Cambouris, K.A. Sudduth

467. North American Soil Test Summary

With the assistance and cooperation of numerous private and public soil testing laboratories, the International Plant Nutrition Institute (IPNI) periodically summarizes soil test levels in North America (NA). Soil tests indicate the relative capacity of soil to provide nutrients to plants. Therefore, this summary can be viewed as an indicator of the nutrient supplying capacity or fertility of soils in NA. This is the eleventh summary completed by IPNI or its predecessor, the Potash ... Q. Rund, S. Murrell, A. Erbe, R. Williams, E. Williams

468. North Dakota State University - Sponsor Presentation

... L. Schumacher, P. Flores, R. Sun, A. Reinholz

469. Nystrom-based Localization in Precision Agriculture Sensors

Wireless sensor networks play a pivotal role in a myriad of applications, ranging from agriculture and health monitoring and to tracking and structural health monitoring. One crucial aspect of these applications involves accurately determining the positions of the sensors. In this study, we study a novel Nystrom-based sampling protocol in which a selected group of anchor nodes, with known locations, establish communication with only a subset of the remaining sensor nodes. Leveraging partial d... A. Tasissa, S. Lichtenberg,

470. OATSmobile: a Data Hub for Underground Sensor Communications and Rural IoT

Wireless Underground Sensor Networks (WUSNs) play a crucial role in precision agriculture by providing information about moisture levels, temperature, nutrient availability, and other relevant factors. However, the use of radio-frequency identification (RFID) devices for WUSNs has been relatively unexplored despite their benefits such as low power consumption. In this work, we develop a hardware platform, called OATSMobile, that enables radio-frequency identification (RFID) communications in ... F.A. Castiblanco rubio, A. Arun, B. Lee, A. Balmos, S. Jha, J. Krogmeier, D.J. Love, D. Buckmaster

471. Obstacle-aware UAV Flight Planning for Agricultural Applications

The use of unmanned aerial vehicles (UAVs) has emerged as one of the most important transformational tools in modern agriculture, offering unprecedented opportunities for crop monitoring, management, and optimization. To ensure effective and safe navigation in agricultural environments, robust obstacle avoidance capabilities are required to mitigate collision risks and to ensure efficient operations. Mission planners for UAVs are typically responsible for verifying that the vehicle is followi... K. Joseph, S. Pitla, V. Muvva

472. Ohio State Food, Agricultural and Biological Engineering (FABE) Certificate Program for Digital Agriculture-moving from the Classroom to Online.

Digital Agriculture encompasses Precision Agriculture, Precision Livestock Farming, Controlled Environment Agriculture, On-Farm Research, and Enterprise Agriculture. We started developing teaching modules focused on Precision Agriculture. To start with, we are creating a series of modules focused on Variable Rate Technology (VRT) and Variable Rate Application (VRA). These initial modules were distilled from existing for credit courses offered by FABE and other extension and professi... K. Trefz, J.P. Fulton, S.A. Shearer, R. Venkatesh

473. Oklahoma State University Department of Plant and Soil Sciences - Sponsor Presentation

... R. Sharry

474. On Data-driven Crop Yield Modelling, Predicting, and Forecasting and the Common Flaws in Published Studies

There has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), and abundance of machine learning modelling approaches. This is a particular problem in the field of Precision Agriculture, where many studies will take a crop yield map (or a small number), cr... P. Filippi, T. Bishop, S. Han, I. Rund

475. On Farm Studies to Determine Seeding Rate in Corn

Seeding rate (SDR) is one of the most critical production practices impacting productivity and economic return for corn (Zea mays L.) By changing SDRs in different zones within a field, herein termed as site-specific management, better economic results can be produced as the outcome of reducing SDRs in low productivity areas and increasing SDRs under high-yielding environments, relative to the uniform SDR management performed by the producer. The aim of this study was to analyze yield respons... G. Balboa, S. Varela, I. Ciampitti, S. Duncan, T. Maxwell, D. Shoups, A. Sharda

476. On-combine Near Infrared Spectroscopy Applied to Prediction of Grain Test Weight

Whole grain near infrared (NIR) spectroscopy is a widely accepted method for analysis of the protein and moisture contents of grain, but is seldom applied to predict test weight. Test weight is a widely used specification for grading of wheat and predictor of flour yield. The objective of this study was to determine whether NIR spectroscopy could be used for measuring the test weight of grain. Reference grain samples of hard red spring wheat were obtained from dryland fields in the semiarid N... D.J. Bonfil, I. Mufradi, S. Asido, D.S. Long

477. On-farm Evaluation of a Satellite Remote Sensing-based Precision Nitrogen Management Strategy

Improper management of nitrogen (N) fertilizers in the cropping systems of the U.S. Midwest has resulted in significant N leaching into the Mississippi River Basin that flows to the Gulf of Mexico. The majority of the U.S. Midwest states need to develop a plan for a nutrient loss reduction strategy to decrease N and phosphorous loadings into waters and the Gulf of Mexico by 45% by 2050. In Minnesota, high nitrate concentration and loads have not been significantly reduced in surface and groun... J. Lu, Y. Miao, C.J. Ransom, F. Fernández

478. On-Farm Evaluation of an Active Optical Sensor Performance for Variable Nitrogen Application in Winter Wheat

Winter wheat (Triticum aestivum L.) represents almost 50% of total cereal production in the European Union, accounting for approximately 25% of total mineral nitrogen (N) fertilizer applied to all crops. Currently, several active optical sensor (AOS) based systems for optimizing variable N fertilization are commercially available for a variety of crops, including wheat. To ensure successful adoption of these systems, definitive measurable benefits must be demonstrated. Nitrogen management str... O.S. Walsh, S.M. Samborski, D. Gozdowski, M. Stępień, E. Leszczyńska

479. On-farm Evaluation of the Potential Benefits of Variable Rate Seeding for Corn in Minnesota

Many farmers in Minnesota are interested in adopting variable rate seeding technology for corn, however, little has been reported about their potential benefits. The objectives of this study were to 1) determine within-field variability of optimal seeding rates, and 2) evaluate the potential benefits of variable rate seeding in commercial corn fields in Minnesota. Four on-farm variable rate seeding trials were conducted in Minnesota in 2022 and 2023, with seeding rates ranging from 31,000 to ... Y. Miao, A. Kechchour, S. Folle, K. Mizuta

480. On-farm Experimentation Case Study in Brazil: Evaluation of Soybean Seeding Rate Using Resources Available at the Farm

In order to maximize grain yield in soybean (Glycine max [L.] Merr.) it is necessary that the plant population is correctly defined. Production environments differ spatially, and cultivar holders suggest plant populations across macroregions and in broad ranges. Refinements of planting seasons and populations are carried out through tests on many properties, often costly and sometimes unrepresentative of most fields. Tools for managing spatial variability are ways to conduct mor... M. Rodrigues alves franchi, I. Molina cyrineu, F. Kagami taira, L. Hunhoff, L.M. Gimenez

481. On-Farm Experimentation Community Meeting

Meeting Agenda: Updates for the OFE-C Newsletters  Increased membership Conference  Global OFE Network (GOFEN) Scientists AND Farmers Global Directory Discussion points OFE-C Outreach Country reps for the OFE-C / Entry point... L. Longchamps

482. On-the-go Gamma Spectrometry and Its Evaluation Via Support Vector Machines: Really a Valuable Tool for Site-independent Soil Texture Prediction?

With progressive implementation of precision agriculture (PA) techniques in current agricultural/ viticultural practice, the need for high-resolution information on soil properties at low effort and cost is increasing. Moreover, climate change and extended drought periods do even increase this demand. Evaluating soil fertility and carbon storage potential of arable fields and vineyards, e.g. for future economic assessment of ecosystem services, requires spatially resolved soil data. Soil text... S. PÄtzold, T.W. Heggemann, R. Wehrle

483. On-the-go Measurements of pH in Tropical Soil

The objective of this study was to assess the performance of a mobile sensor platform with ion-selective antimony electrodes (ISE) to determine pH on-the-go in a Brazilian tropical soil. The field experiments were carried out in a Cambisol in Piracicaba-SP, Brazil. To create pH variability, increasing doses (0, 1, 3, 5, 7 and 9 Mg ha-1) of lime were added on the experimental plots (25 x 10 m) one year before the data acquisitions. To estimate soil pH levels we used a Mobile Sensor ... M.T. Eitelwein, R.G. Trevisan, A.F. Colaço, M.R. Vargas, J.P. Molin

484. Onboard Weed Identification and Application Test with Spraying Drone Systems

Commercial spraying drone systems nowadays have the ability to implement variable rate applications according to pre-loaded prescription maps. Efforts are needed to integrate sensing and computing technologies to realize on-the-go decision making such as those on the ground based spraying systems. Besides the understudied subject of drone spraying pattern and efficacy, challenges also exist in the decision making, control, and system integration with the limits on payload and flight endurance... Y. Shi, M. Islam, K. Steele, J.D. Luck, S. Pitla, Y. Ge, A. Jhala, S. Knezevic

485. Open Data for Food Quality and Food Security Control: a Case Study of the Czech Republic

Food quality and food security is of a high public interest in the European Union. In the Czech Republic, food quality and food security is under control of three different public authorities: the Czech Trade Inspection Authority (CTIA) that is affiliated with the Ministry of Industry and Trade of the Czech Republic, the Czech Agriculture and Food Inspection Authority (CAFIA) that is affiliated with the Ministry of Agriculture of the Czech Republic and the regional network of hygienic station... M. Ulman, M. Stoces, J. Jarolimek, P. Simek

486. Operationalization of On-farm Experimentation in African Cereal Smallholder Farming Systems

Past efforts have concentrated on linear or top-down approaches in delivering precision nutrient management (PNM) practices to smallholder farmers. These deliberate attempts at increasing adoption of PNM practices have not yielded the expected outcomes, that is, increased productivity and nutrient use efficiency, at scale. This is because technologies generated by scientists with minimal farmer involvement often are not well tailored to the attendant agro-ecological, socio-economic, and cultu... I. Adolwa, S. Phillips, B.A. Akorede, A.A. Suleiman, T. Murrell, S. Zingore

487. Opportunity Cost of Precision Conservation

Crop production and biodiversity conservation vie for limited agricultural land resources. While biodiversity conservation benefits society as a whole, it is farmers who bear the immediate economic consequences of shifting land from agricultural to conservation use. When parts of a field are put into conservation use, farmers give up the net revenue that they earned from crop production, accepting the “opportunity cost” of losing that revenue stream.  But since crop yields ar... S. Lee, S.M. Swinton

488. Optimal Placement of Soil Moisture Sensors in an Irrigated Corn Field

Precision agricultural practices rely on characterization of spatially and temporally variable soil and crop properties to precisely synchronize inputs (water, fertilizer, etc.) to crop needs; thereby enhancing input use efficiency and farm profitability. Generally, the spatial dependency range for soil water content is shorter near the soil surface compared to deeper depths, suggesting a need for more sampling locations to accurately characterize near-surface soil water content. However, det... D. Mandal, L. Longchamps, R. Khosla

489. Optimization of Batch Processing of High-density Anisotropic Distributed Proximal Soil Sensing Data for Precision Agriculture Purposes

The amount of spatial data collected in agricultural fields has been increasing over the last decade. Advances in computer processing capacity have resulted in data analytics and artificial intelligence becoming hot topics in agriculture. Nevertheless, the proper processing of spatial data is often neglected, and the evaluation of methods that efficiently process agricultural spatial data remains limited. Yield monitor data is a good example of a well-established methodology for data processi... F. Hoffmann silva karp, V. Adamchuk, A. Melnitchouck, P. Dutilleul

490. Optimizing Chloride (Cl) Application for Enhanced Agricultural Yield

The optimization of chloride (Cl-) application rates is crucial for enhancing crop yields and reducing environmental impact in agricultural systems. This study investigates the relationship between chloride application rates and wheat yields, focusing on Club wheat cultivation in a 19.76-hectare field in Washington State. The target yield was set at 3765 kilograms per hectare, with seeding conducted at 67.24 kilograms per hectare using conservation tillage practices. Potassium chlo... F. Pereira de souza, R.P. Negrini, H. Tao

491. Optimizing Corn Irrigation Strategies: Insights from NDVI Trends, Soil Moisture Dynamics, and Remote Sensing

This comprehensive field experiment systematically examines the impact of varied irrigation rates on corn growth and yield across three treatments: 33%, 67%, and 100% irrigation rates. Utilizing the normalized difference vegetation index (NDVI) as a parameter for vegetation health, distinct patterns emerge throughout key growth stages. The 100% irrigation treatment consistently exhibits superior vegetation health, sustaining higher NDVI values across all stages, while the 33% treatment reveal... J.O. Abon, A. Sharda

492. Optimizing Experimental Design for Determining Economic Nitrogen Levels: Insights on the Use of Monte Carlo Simulations

The determination of economic nitrogen levels is a pivotal element in the quest for sustainable agricultural practices. Designing experiments to accurately identify these levels, especially in contexts constrained by limited plot availability, poses a significant challenge. In response to these challenges, this study endeavors to demonstrate  an approach to optimize the experimental design for identifying economic nitrogen levels, even under such constraints. We employed statistical... C. Matavel, A. Meyer-aurich, H. Piepho

493. Optimizing Nitrogen Application in Global Wheat Production by an Integrated Bayesian and Machine Learning Approach

Wheat production plays a pivotal role in global food security, with nitrogen fertilizer application serving as a critical factor. The precise application of nitrogen fertilizer is imperative to maximize wheat yield while avoiding environmental degradation and economic losses resulting from excess or inadequate usage. The integration of Bayesian and machine learning methodologies has gained prominence in the realm of agricultural research. Bayesian and machine learning based methods have great... Z. Liu, X. Liu, Y. Tian, Y. Zhu, W. Cao, Q. Cao

494. Optimizing Nitrogen Application to Maximize Yield and Reduce Environmental Impact in Winter Wheat Production

Field-specific fertilizer rate optimization is known to be beneficial for improving farming profit, and profits can be further improved by dividing the field into smaller plots and applying site-specific rates across the field. Finding optimal rates for these plots is often based on data gathered from said plots, which is used to determine a yield response curve, telling us how much fertilizer needs to be applied to maximize yield. In related work, we use a Convolutional Neural Network, known... A. Peerlinck, J. Sheppard, G.L. Morales luna, P. Hegedus, B. Maxwell

495. Optimizing Soil Nutrient Management: Agricultural Policy/environmental Extender (APEX) Model Simulation for Field Scale Phosphorous Loss Reduction in Virginia

Managing soil nutrients is crucial for enhancing crop productivity and meeting consumptions demands while minimizing environmental impacts. Sustainable agriculture relies on well-planned soil nutrient management strategies. Phosphorous (P) stands out among the 16 essential soil nutrients, particularly in Virginia, where natural P levels are typically low. Adequate amount of P is necessary for the early root formation and plant growth. However, excess amount of P in the soil leads to increase ... S. Kumari, J. Rathore, S. Mitra, M. Gardezi, O. Walsh

496. Optimizing Soybean Management with UAV RGB and Multispectral Imagery: a Neural Network Method and Image Processing

Precision agriculture (PA) has emerged as a fundamental approach in contemporary agricultural management, aimed at maximizing efficiency in the use of resources and improving crop productivity. The transition to so-called "agriculture 4.0" represents a revolution in the way technology is applied in the field, with an emphasis on digital and automated solutions such as UAVs (Unmanned Aerial Vehicles). These devices offer new capabilities for capturing high-resolution images, enabling... F. Pereira de souza, L. Shiratsuchi, H. Tao, M. Acconcia dias, M. Barbosa, T. deri setiyono, S. Campos

497. Optimizing the Connectivity of Wireless Underground Sensor Networks

In the rapidly evolving field of wireless communication, extending this technology into subterranean realms presents a frontier replete with unique challenges and opportunities. This study explores the intricate dynamics of establishing reliable connectivity in underground environments, a critical component for applications in diverse fields including precision agriculture and environmental monitoring. The distinct characteristics of underground settings impose significant obstacles for wirel... M. Han, N. Zhang, P. Armstrong

498. Optimizing Vineyard Crop Protection: an In-depth Study of Spraying Drone Operational Parameters

In modern agriculture, the precise and efficient application of agrochemicals is essential to ensure crop health and increase productivity while minimizing adverse environmental impacts. While traditional spraying methods have long been the cornerstone of crop protection, the introduction of unmanned aerial vehicles (UAVs), commonly known as drones), has led to a revolutionary era in agriculture. UAVs offer novel opportunities to improve agricultural practices by providing precision, efficien... V. Psiroukis, S. Fountas, H. Uyar, A. Balafoutis, A. Kasimati

499. Organ Scale Nitrogen Map: a Novel Approach for Leaf Nitrogen Concentration Estimation

Crop nitrogen trait estimations have been used for decades in the frame of precision agriculture and phenotyping researches. They are crucial information towards a sustainable agriculture and efficient use of resources. Remote sensing approaches are currently accurate tools for nitrogen trait estimations. They are usually quantified through a parametric regression between remote sensing data and the ground truth. For instance, chlorophyll or nitrogen concentration are accurately estimated usi... A. Carlier, S. dandrifosse, B. Dumont, B. Mercatoris

500. Overcoming Educational Barriers for Precision Agriculture Adoption: a University Diploma in Precision Agriculture in Argentina

The lack of educational programs in Precision Agriculture (PA) has been reported as one of the barriers for adoption. Our goal was to improve professional competence in PA through education in crop variability, management, and effective practices of PA in real cases. In the last 20 years different efforts has been made in Argentina to increase adoption of PA. The Universidad Nacional de Rio Cuarto (UNRC) launched in 2021 the first University Diploma in PA, a 9-month program to train agronomis... G. Balboa, A. Degioanni, R. Bongiovanni, R. Melchiori, C. Cerliani, F. Scaramuzza, M. Bongiovanni, J. Gonzalez, M. Balzarini, H. Videla, S. Amin, G. Esposito

501. Ownership and Protections of Farm Data

Farm data has been a contentious point of debate with respect to ownership rights and impacts when access rights are misappropriated. One of the leading questions farmers ask deals with the protections provided to farm data. Although no specific laws or precedence exists, the possibility of trade secret is examined and ramifications for damages discussed. Farm management examples are provided to emphasize the potential outcomes of each possible recourse for misappropriating farm data. ... A. Ellixson, P. Goeringer, T. Griffin

502. Partial Fruitlet Cutting Approach for Robotic Apple Thinning

Early season thinning of apple fruitlets is a crucial task in commercial apple farming, traditionally accomplished through chemical sprays or labor-intensive manual operations. These methods, however, are faced with the challenges of diminishing labor availability as well as environmental and/or economic sustainability. This research examines 'partial fruitlet cutting,' a novel nature-assisted strategy, as an alternative method for automated apple thinning in orchards. The study hypot... R. Sapkota, M. Karkee

503. Participatory Irrigation Extension Programs to Increasing Adoption of Best Irrigation Strategies

Farmers in Alabama, Tennessee, and other US southeastern states lack experience in irrigation water management and adoption of the state-of-the-art technologies and practices to increase irrigation water use efficiency. Several federal and state-funded projects are being implemented to demonstrate and train farmers and consultants on irrigation scheduling strategies and variable rate irritation. Half a dozen on-farm demonstration sites are selected every year to evaluate, demonstrate, and tra... L. Nunes, E. Francisco, R. Prasad, B.V. Ortiz, E. Abban-baidoo , M. Worosz, M. Robinette , C. O'connor, A. Gamble

504. Pessl Instruments

For more than 37 years, Pessl Instruments has been offering tools for informed decision-making. A complete range of wireless, solar powered monitoring systems which support almost all communication standards roofed under the METOS® brand is available to our clients worldwide.    The systems, along with online platform and mobile application Fieldclimate, are applicable in all climate zones and can be used in various industries and for various purposes – from ... D. Brazda

505. Pesticide Application Management Toolset for Improved Worker Protection

The practice of pesticide use has been widely adopted by production agriculture to maximize yields since the 1950s. Even though it provides beneficial economic returns to the farmers, it also enhances the risk of environmental pollution and is directly associated with the risk of poisoning to agricultural workers. While adhering to United States Federal Environmental Protection Agency (EPA) Worker Protection Standard (WPS) guidelines, the current systems need considerable time to provide cruc... C. Narayana, N. Thorson, J.D. Luck

506. Planet Labs' Monitoring Solution in Support of Precision Agriculture Practices

Satellite imagery is particularly useful for efficiently monitoring very large areas and providing regular feedback on the status and productivity of agricultural fields. These data are now widely used in precision farming; however, many challenges to making optimal use of this technology remain, such as easy access to data, management and exploitation of large datasets with deep time series, and sharing of the data and derived analytics with users. Providing satellite imagery through a cloud... K.J. Frotscher, R. Schacht, L. Smith, E. Zillmann

507. Plant and N Impacts on Corn (Zea Mays) Growth: Whats Controlling Yield?

Studies were conducted in South Dakota to assess mechanisms of intraspecific competition between corn (Zea mays) plants. Treatments were two plant populations (74,500 and 149,000 plants ha-1), three levels of shade (0, 40, and 60%) on the low plant population, two water treatments (natural precipitation and natural + irrigation), and two N rates (0 and 228 kg N ha-1). In-season leaf chlorophyll content was measured. At harvest, grain and stover yields were quantified with grain 13C-d... D.E. Clay, S.A. Clay, G. Reicks, D. Horvath

508. Plant Stand Count and Corn Crop Density Assessment Using Texture Analysis on Visible Imagery Collected Using Unmanned Aerial Vehicles

Ensuring successful corn farming requires an effective monitoring program to collect information about stand counts at an early stage of growth and plant damages due to natural calamities, farming equipment, hogs, deer and other animals. These monitoring programs not only provide a yield estimate but also help farmers and insurance companies in assessing the causes of damages. Current field-based assessment methods are labor intensive, costly, and provide very limited information. Manual asse... S. Samiappan, B. Henry, R.J. Moorhead, M.W. Hock

509. Portable Soil EC - Development of an Electronic Device for Determining Soil Electrical Conductivity

Decision-making in agriculture demands continuous monitoring, a factor that propels the advancement of tools within Agriculture 4.0. In this context, understanding soil characteristics is essential. Electrical conductivity (EC) sensors play a pivotal role in this comprehension. Given this backdrop, the core motivation of this research was developing an accessible and effective electronic device to measure the apparent EC of the soil. It provides features like geolocation, recording of the dat... C.L. Bazzi, L.A. Rauber, W.K. Oliveira, R. Sobjak, K. Schenatto, L. Gebler, L.M. Rabello

510. Positioning Strategy of Maize Hybrids Adjusting Plant Population by Management Zones

Choice of hybrid and accurate amount of plants per area determines grain yield and consequently net incomes. Local field adjustment in plant population is a strategy to manage spatial variability and optimize environmental resources that are not under farmer control (like soil type and water availability). This study aims to evaluate the response of hybrids by levels of plant population across management zones (MZ). Six different hybrids and five rates of plant populations were analyzed start... A.A. Anselmi, J.P. Molin, M.T. Eitelwein, R. Trevisan, A. Colaço

511. Possibilities for Improved Decision Making and Operating Efficiency Derived from the Predictability of Autonomous Farming Operations

For the last 6 years, small autonomous agricultural vehicles have been operating on Harper Adams University’s fields in Shropshire.  Starting with a single tractor on a single rectangular hectare (2.5 acres) and moving on to three tractors on 5 irregularly shaped fields covering over 30 hectares (75 acres).  Multiple crops have been grown; planting, tending, and harvesting with autonomous tractors and harvesters.  The fields are worked using a Controlled Traffic Farming s... M. Gutteridge

512. Post Processing Software for Grain Yield Monitoring System Suitable to Korean Full-feed Combines

Precision agriculture (PA) has been adopted in many countries and crop and country specific technologies have been implemented for different crops and agricultural practices. Although PA technologies have been developed mainly in countries such as USA, Europe, Australia, where field sizes are large, need of PA technologies has been also drawn in countries such as Japan and Korea, where field sizes are relatively small (about 1 ha). Although principles are similar, design concept and practical... K. Lee, S. Chung, J. Lee, S. Kim, Y. Kim, M. Choi

513. Potato Disease Detection Using Laser Speckle Imaging and Deep Learning

Early detection of potato diseases is essential for minimizing crop loss. Implementing advanced imaging techniques can significantly improve the accuracy and efficiency of disease detection in potato crops. Leveraging machine learning algorithms can further enhance the speed and precision of disease identification, enabling timely intervention measures. This work presents a novel potato disease detection technique using whole-potato speckle imaging and deep learning. Laser Speckle Imaging (LS... A.H. Rabia, M.A. Salem

514. Potential Benefits of Variable Rate Nitrogen Topdressing Strategy Coupled with Zoning Technique: a Case Study in a Town-scale Rice Production System

Integrating remote sensing (RS)-based variable rate nitrogen (N) recommendation (VRNR) algorithms and management zones (MZs) may improve the accuracy and efficiency of site-specific N management. However, its potential benefits for application in commercial rice production systems can hardly be assessed, since it requires to intervene in common agricultural practices and causes certain economic and environmental consequences. Through a machine learning approach, this study aims to comprehensi... J. Zhang, W. Wang, Z. Fu, Q. Cao, Y. Tian, Y. Zhu, W. Cao, X. Liu

515. Potential for Improving African Smallholder Cereal Farming Using Sentinel-2A Spectral Reflectance

Cereal crops are critical for African smallholder farmers seeking to improve regional food availability, yet many struggle with low productivity from non optimal practices. This present study evaluated the possibility of using the satellite Sentinel-2 Multispectral Instrument data to inform management techniques tailored to African small-scale cereal farms’ local conditions. Improved practices maize, wheat, and rice plots were established respectively in Togo, Tunisia, and Tanzania... A. Biaou, S. Phillips

516. Potential Improvement in Rice Nitrogen Status Monitoring Using Rapideye and Worldview-2 Satellite Remote Sensing

For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large area in a timely fashion. Satellite remote sensing provides a promising technology for crop growth monitoring and precision management over large areas. The FORMOSAT-2 satellite remote sensing imageries with 4 wavebands have been used to estimate rice N status. The objective of this study was to evaluate the potential of using high spatial ... S. Huang, Y. Miao, F. Yuan, M.L. Gnyp, Y. Yao, Q. Cao, V. Lenz-wiedemann, G. Bareth

517. Potential of UAS Multispectral Imagery for Predicting Yield Determining Physiological Parameters of Cotton

The use of unmanned aerial systems (UAS) in precision agriculture has increased rapidly due to the availability of reliable, low-cost, and high-resolution sensors as well as advanced image processing software. Lint yield in cotton is the product of three physiological parameters: photosynthetically active radiation intercepted by canopy (IPAR), the efficiency of converting intercepted active radiation to biomass (RUE), and the ratio of economic yield to total dry matter (HI). The relationship... A. Pokhrel, S. Virk, J.L. Snider, G. Vellidis, V. Parkash

518. Precision Agriculture Economics, Profitability, Adoption, and Risk Community Meeting

Agenda Update on Community Activities Update on membership Announcement and introduction of incoming Deputy Leader Discussion points GIATE Symposium session Other activities AOB ... K. Behrendt, M. Michels

519. Precision Agriculture Education in Africa: Perceptions, Opportunities and Challenges, and the Way Forward

Precision Agriculture is critical for accelerated transformation of the agrifood systems in Africa for shared prosperity and enhanced livelihoods. The paper presents an overview of the perceptions of faculty, undergraduate and postgraduate students from Ghanaian universities about PA education, and its opportunities and challenges. The study involves a case study of two public universities, the University of Cape Coast and the Technical University of Cape Coast, respectively a and a desk revi... K.A. Frimpong

520. Precision Agriculture in Latin America Community Meeting

...

521. Precision Agriculture Practices for Sustaining Productivity and Profitability in Reclaimed Sodic Soils in Northwest India

Indo-gangetic alluvial plain comprising of Punjab, Haryana and Uttar Pradesh states is a food bowl of India. These states contribute significant quantity of food grains particularly rice and wheat to the central pool. However, in the recent past, the productivity of the dominant rice-wheat cropping system in reclaimed alkali (sodic) soils is either stagnating or decreasing with the associated problems of declining water table levels, decreasing levels of organic matter in the soil, nutrient i... G. Singh

522. Precision Agriculture Techniques for Crop Management in Trinidad and Tobago: Methodology & Field Layout

Agriculture in Trinidad and Tobago has not advanced at the same rate at which new agricultural technology has been released. This has led to large-scale abandonment of crop lands as challenges posed by labor availability and their agronomic capability could not meet the technological demands for agricultural production, competitiveness and sustainability. There is an urgent need to develop technology-based agriculture models to meet the demands of a modern agricultural sector and to maintain ... G. Seepersad, T. Sampson, S. Seepersad, D. Goorahoo

523. Precision Agriculture: Forage Chopper Noise Level As an Estimator of Corn Silage Production in Small Farms

The objective of the work carried out in the Registro County, SP, Brazil, in the year 2021, was to study the forage chopper noise level as an estimator of corn silage production in small farms. The corn crop study and characterization were measured plant height (PH), height of first ear insertion (HEI) and green mass production of plants (GM) were studied. The noise (NO) produced by the forage machine during ensiling was collected by recording, considering it as a potential yield estimator du... W.J. Souza, A.N. Silva

524. Precision Application of Seeding Rates for Weed and Nitrogen Management in Organic Grain Systems

In a time of increasing ecological awareness, organic agriculture offers sustainable solutions to many of the polluting aspects of conventional agriculture. However, without synthetic inputs, organic agriculture faces unique challenges such as weed control and fertility management. Precision Agriculture (PA) has been used to successfully increase input use efficiency in conventional systems and now offers itself as a potential tool for organic farmers as well. PA enables on farm experimentati... S. Loewen, B.D. Maxwell

525. Precision Farming Basics Manual - a Comprehensive Updated Textbook for Teaching and Extension Efforts

Today precision agricultural technologies are limited by the lack of a workforce that is technology literate, creative, innovative, fully trained in their discipline, able to utilize and interpret information gained from information-age technologies to make smart management decisions, and have the capacity to convert locally collected information into practical solutions. As part of a grant entitled Precision Farming Workforce Development:  Standards, Working Groups, and Experimental Lea... K. Shannon

526. Precision Farming by Means of Remote Sensing.

In order to improve the wine quality a study has been carried out on a vineyard. From two different types of satellite images, 5 products have been obtained and represented in maps. DMC-UK images, with a resolution of 32 meters and QUICK-BIRD images, with a resolution of 0.6 meters have been used. Through the bands of these images, the following products were obtained: the NDVI, with which users find out which zones in their estates have the worst condition; Mean Vegetation State, which is a ... J.L. Casanova, S. Fraile, A. Romo, J. Sanz, C. Moclán

527. Precision Irrigation Strategies for Climate-resilient Crop Production and Water Resource Management

Deficit irrigation management practices that best optimize the use of limited water resources without impacting crop yield are necessary to ensure the sustainability of agricultural production. This is particularly crucial in regions characterized by semi-arid climate, like Western Kansas, where the challenge of depleting water resources is worsened by the occurrence of extreme climate conditions. Therefore, a data-driven irrigation management strategy such as one developed based on crop evap... K.E. Igwe, I. Onyekwelu, V. Sharda

528. Precision Management of Cattle Feedlot Waste

Open-lot cattle feeding operations face challenges in control of nutrient runoff, leaching, and gaseous emissions. This report investigates the use of precision management of saline soils as found on 1) feedlot surfaces and on a 2) vegetative treatment area (VTA) utilized to control feedlot runoff. An electromagnetic induction soil conductivity meter was used to collect apparent soil electrical conductivity (ECa) from a feedlot pen and a research VTA at the U.S. Meat Animal Research Center, C...

529. Precision Nitrogen and Water Management for Optimized Sugar Beet Yield and Sugar Content

Sugar beet (SB) production profitability is based on maximizing three parameters: beet yield, sucrose content, and sucrose recovery efficiency. Efficient nitrogen (N) and water management are key for successful SB production. Nitrogen deficits in the soil can reduce root and sugar yield. Overapplication of N can reduce sucrose content and increase nitrate impurities which lowers sucrose recovery. Application of N in excess of SB crop need leads to vigorous canopy growth, while compromising ro... O.S. Walsh, S. Shafian

530. Precision Nitrogen Management Based on Nitrogen Removal in Rainfed Wheat

Growers of hard red spring wheat may capture price premiums for maximizing the protein concentration of their grain. Nitrogen (N) nutrition adequacy is crucial to achieving high grain protein concentration. The objective of this study was to determine the usefulness of N removal maps by comparing grain protein, yields, and dollar returns obtained from this precision N management approach with that from conventional uniform N management. Strip plot experiments were designed to compare spatiall... D.J. Bonfil, I. Mufradi, S. Asido, D.S. Long

531. Precision Nitrogen Management Community Meeting

Agenda Welcome to the meeting participants by Dr. Brenda Ortiz (Professor at Auburn University) 2022-2024 community leader and incoming leader Dr. Laila Puntel (Syngenta). Brief update of activities and opportunities for the upcoming years (Brenda Ortiz) Strategies to assess precision nutrient management educational needs and networking opportunities among community members and ISPA in general. Discuss possibilities for colla... B.V. Ortiz, L.A. Puntel

532. Precision Nutrient Management System Based on Ion and Crop Growth Sensing

Automated sensing and variable-rate supply of nutrients in hydroponic solutions according to the status of crop growth would allow more efficient nutrient management for crop growth in closed systems. The Structure from Motion (SfM) method has risen as a new image sensing method to obtain 3D images of plants that can be used to estimate their growth, such as leaf cover area (LCA), plant height, and fresh weight. In this sense, sensor fusion technology combining ion-selective electrodes (ISEs)... W. Cho, D. Kim, C. Kang, H. Kim, J. Son, S. Chung, J. Jiang, H. Yun

533. Precision Nutrient Management Through Drip Irrigation in Aerobic Rice

A field experiment was conducted during kharif 2015 to asses the spatial variability and precision nutrient management through drip irrigation in aerobic rice at ZARS, GKVK, Bangalore. The experimental field has been delineated into 48 grids of 4.5 m x 4.5 m using geospatial technology. Soil samples from 0-15 cm depth were collected and analysed. There was spatial variability for available nitrogen (154 to 277 kg ha-1), phosphorous (45 to 152 kg ha-1) and potass... N. Dr., S. T, M. Giriyappa, H. D.c, B. Patil, D. Prabhudeva, G. Kombali, S. Noorasma, M. Thimmegowda

534. Precision Placement of Corn Gluten Meal for Weed Control in Organic Vegetable Production

Organic vegetable producers rank weeds as one of their most troublesome, time consuming, and costly production problems. As a result of the limited number of organically approved weed control herbicides, the precision placement of these materials increases their potential usefulness in organic production systems. As a non-selective preemergence or preplant-incorporated herbicide, corn gluten meal (CGM) inhibits root development; decreases shoot length, and reduces plant survival. The developm... C.L. Webber iii, M.J. Taylor, J.W. Shrefler

535. Precision Tools for Monitoring Experimental Irrigation Treatments in California Vineyards

Precision farming techniques, such as zonal management and variable rate nutrient delivery, have been used to manage spatial variability in many crops. Wine grapes, and most permanent crops, have been slower than row crops or agronomic crops to take advantage of these techniques, though there are barriers to implementing these methods when compared to agronomic crops. The objective of this project is to show how a suite of monitoring and management tools can be used to evaluate the performanc... B. Sams, P. Previtali, J. Mezger, M. Aboutalebi, L. Sanchez, N. Dokoozlian

536. Predicting Below and Above Ground Peanut Biomass and Maturity Using Multi-target Regression

Peanut growth and maturity prediction can help farmers and breeding programs improving crop management. Remote sensing images collected by satellites and drones make possible and accurate crop monitoring. Today, empirical relations between crop biomass and spectral reflectance could be used for prediction of single variables such as aboveground crop biomass, pod weight (PW), or peanut maturity. Robust algorithms such as multioutput regression (MTR) implemented through multioutput random fores... M.F. Oliveira, F.M. Carneiro, M. Thurmond, M.D. Del val, L.P. Oliveira, B. Ortiz, A. Sanz-saez, D. Tedesco

537. Predicting Corn Emergence Uniformity with On-the-go Furrow Sensing Technology

Integration of proximal soil sensors into commercial row-crop planter components have allowed for a dense quantification of within-field soil spatial variability. These technologies have potential to guide real-time management decisions, such as on-the-go variable seeding rate or depth. However, little is known about the performance of these systems. Therefore, research was conducted in central Missouri, USA to determine the relationship between planter sensor metrics, and corn (Zea mays ... L.S. Conway, C. Vong, N.R. Kitchen, K.A. Sudduth, S.H. Anderson

538. Predicting Secondary Soil Fertility Attributes Using XRF Sensor with Reduced Scanning Time in Samples with Different Moisture Content

To support future in situ/on-the-go applications using X-ray fluorescence (XRF) sensors for soil mapping, this study aimed at evaluating the XRF performance for predicting organic matter (OM), base saturation (V), and exchangeable (ex-) Mg, using a reduced analysis time (e.g., 4 s) in soil samples with different moisture contents. These attributes are considered secondary for XRF prediction because they do not present emission lines in the XRF spectrum. Ninety-nine soil samp... T.R. Tavares, J.P. Molin, T.R. Da silva , H.W. De carvalho

539. Predicting Soil Cation Exchange Capacity from Satellite Imagery Using Random Forest Models

Crop yield variability is often attributed to spatial variation in soil properties. Remote sensing offers a practical approach to capture soil surface properties over large areas, enabling the development of detailed soil maps. This study aimed to predict cation exchange capacity (CEC), a key indicator of soil quality, in the agricultural fields of the Lower Mississippi Alluvial Valley using digital soil mapping techniques. A total of 15,586 soil samples were collected from agricultural field... I. Muller, J. Czarnecki, M. Li, B.K. Smith

540. Predicting Soil Chemical Properties Using Proximal Soil Sensing Technologies and Topography Data: a Case Study

Using proximal soil sensors (PSS) is widely recognized as a strategy to improve the quality of agricultural soil maps. Nevertheless, the signals captured by PSS are complex and usually relate to a combination of processes in the soil. Consequently, there is a need to explore further the interactions at the source of the information provided by PSS. The objectives of this study were to examine the relationship between proximal sensing techniques and soil properties and evaluate the feasibility... F. Hoffmann silva karp, V. Adamchuk, P. Dutilleul, A. Melnitchouck, A. Biswas

541. Predicting Soybean Yield Using Remote Sensing and a Machine Learning Model

Soybean (Glycine max L.), a nutrient-rich legume crop, is an important resource for both livestock feed and human dietary needs. Accurate preharvest yield prediction of soybeans can help optimize harvesting strategies, enhance profitability, and improve sustainability. Soybean yield estimation is inherently complex because yield is influenced by many factors including growth patterns, varying crop physiological traits, soil properties, within-field variability, and weather conditions. The obj... M. Gardezi, O. Walsh, D. Joshi, S. Kumari, D.E. Clay, J. Rathore

542. Predicting the Spatial Distribution of Aflatoxin Hotspots in Peanut Fields Using DSSAT CSM-CROPGRO-PEANUT-AFLATOXIN

Aflatoxin contamination in peanuts (Arachis hypogaea L.) is a persistent concern due to its detrimental effects on both profitability and public health. Several plant stress-inducing factors, including high soil temperatures and low soil moisture, have been associated with aflatoxin contamination levels. Understanding the correlation between stress-inducing factors and contamination levels is essential for implementing effective management strategies. This study uses the DSSAT CSM-CR... S. Maktabi, G. Vellidis, G. Hoogenboom, K. Boote, C. Pilcon, J. Fountain, M. Sysskind, S. Kukal

543. Predicting Water Potentials of Wild Blueberries During Drought Treatment Using Hyperspectral Sensor and Machine Learning

Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) us... Y. Zhang, U.R. Hodeghatta, V. Dhiman, K. Barai, T. Trang

544. Predicting Within-field Cotton Yield Variability Using DSSAT for Decision Support in Precision Agriculture

The quantification of spatial and temporal variability of cotton (Gossypium hirsutum L.)  yield provides critical information for optimizing resources, especially water, in the Southern High Plains (SHP), Texas, with a diminishing water supply. The within-field yield variation is mostly influenced by the properties of soil and their interaction with water and nutrients. The objective of this study was to predict within-field cotton yield variability using a crop growth mode... B. Ghimire, R. Karn, O. Adedeji, W. Guo

545. Predicting, Mapping, and Understanding the Drivers of Grain Protein Content Variability – Utilising John Deere’s New Harvestlab 3000 Grain Sensing System

Grain protein content (GPC) is a key determinant of the prices that grain growers receive, and the rising cost of production is shifting management focus towards optimising this to maximise return on investment. In 2023, John Deere released the HarvestLab 3000TM Grain Sensing system in Australia for real-time, on-the-go measurement of protein, starch, and oil values for wheat, barley, and canola. However, while the uptake of these sensors is increasing, GPC maps are not available f... M.J. Tilse, P. Filippi, T. Bishop

546. Prediction of Field-scale Evapotranspiration Using Process Based Modeling and Geostatistical Time-series Interpolation

Irrigation scheduling depends on the combination of evaporative demand from the atmosphere, spatial and temporal heterogeneity in soil properties and changes in crop canopy during a growing season. This on-farm trial is based on data collected in 72-acre processing tomato field in Central Valley of California. The Multiband Spectrometric Arable Mark 2 sensors at three different locations in the field. Multispectral and thermal imagery provided by Ceres Imaging were collected eight times durin... G. Jha, F. Nazrul, M. Nocco, M. Pagé fortin, B. Whitaker, D. Diaz, A. Gal, R. Schmidt

547. Prediction of Nitrogen Needs with Nitrogen-rich Strips and Ramped Nitrogen Strips

Both nitrogen rich strips and ramped nitrogen strips have been used to estimate topdress nitrogen needs for winter wheat based on in-season optical reflectance data. The ramped strip system places a series of small plots in each field with increasing levels of nitrogen to determine the application rate at which predicted yield response to nitrogen reaches a plateau. The nitrogen-rich strip system uses a nitrogen fertilizer optimization algorithm based on optical reflectance measures from the ... D.C. Roberts, B.W. Brorsen, W.R. Raun, J.B. Solie

548. Prediction of Sugarcane Yields in Commercial Fields by Early Measurements with an Optical Crop Canopy Sensor

As a grass (Poaceae), sugarcane needs supplemental mineral nitrogen (N) to achieve high yields on commercial production areas. In Brazil, N recommendations for sugarcane ratoons are based on expected yield and the results of N response trials, as soil N analyses are not a suitable basis for decisions on optimum N fertilizer rates under tropical conditions. Since the vegetative parts in sugarcane are harvested, yield components such as the number of stalks and stalk height are directly correla... G. Portz, J. Jasper, J.P. Molin

549. Premier Strategy Consulting - Sponsor Presentation

... C. Zhu

550. Prescription Map Creation for Optimal Variable-rate Seeding in Arkansas Fields

Soybean seeding rate selection in Arkansas depends on cultivar, planting date, and soil characteristics. Guidelines were developed to maximize profitability from whole field management and little information is available to optimize smaller-scale management. Nevertheless, Arkansas cropland is expected to be a good candidate for variable-rate seeding (VRS) because of heterogeneous soil parent materials, large field sizes, and added spatial variability introduced by the normalization of land-le... W. France, A. Poncet, U. Sigdel, J. Ross

551. Principal Component Analysis of Rice Production Environment in the Rice Terrace Region

Environmental conditions that affect rice production, such as air temper- ature, relative humidity, solar radiation, effective cation exchangeable capacity (ECEC) of the soil, and total nitrogen in irrigation water, were assessed for 4 paddy fields in Hoshino village, Fukuoka prefecture in Japan. Also, environ- mental factors that affected rice quality (physicochemical properties of rice grains and cooked rice) were identified using data during the beginning of a ripening period (20 days afte... Y. Hirai, Y. Beppu, Y. Mori, K. Tomita, K. Hamagami, K. Mori, S. Uchida, S. Inaba

552. Printed Nitrate Sensors for In-soil Measurements

Managing nitrate is a central concert for precision agriculture, from delineating management zones, to optimizing nitrogen use efficiency through in-season applications, to minimizing leaching and greenhouse gas emissions. However, measurement methods for in-soil nitrate are limiting. State-of-the-art soil nitrate analysis requires taking soil or liquid samples to laboratories for chemical or spectrographic analysis. These methods are accurate, but costly, labor intensive, and cover limited g... C. Baumbauer, P. Goodrich, A. Arias

553. Privacy Issues and the Use of UASs/Drones in Maryland

 According to the Federal Aviation Administration (FAA), the lawful use of Unmanned Aerial Vehicles (UAV), also known as Unmanned Aircraft Systems (UAS), or more commonly as drones, are currently limited to military, research, and recreational applications. Under the FAA’s view, commercial uses of drones are illegal unless approved by the Federal government.  This will change in the future.  Congress authorized the FAA to develop regulations for the use of drones by priva... P. Goeringer, A. Ellixson, J. Moyle

554. Private Simple Databases for Digital Records of Contextual Events and Activities

Farmers’ commitment and ability to keep good records varies tremendously. Records and notes are often cryptic, misplaced, or damaged and for many, remain unused. If such information were recorded digitally and stored in the cloud, we immediately solve some access and consistency issues and make this data FAIR (findable, accessible, interoperable, reusable). More importantly, interoperable digital formats can also enable mining for insights and analysi... M.S. Basir, J. Krogmeier, Y. Zhang, D. Buckmaster

555. Processing Yield Data from Two or More Combines

Erroneous data affect the quality of yield map. Data from combines working close to each other may differ widely if one of the monitors is not properly calibrated and this difference has to be adjusted before generating the map. The objective of this work was to develop a method to correct the yield data when running two or more combines in which at least one has the monitor not properly calibrated. The passes of each combine were initially identified and three methods to correct yield data w... L. Maldaner, J.P. Molin, T.F. Canata

556. Profitability of Regenerative Cropping with Autonomous Machines: an Ex-ante Assessment of a British Crop-livestock Farm

Farmers, agroecological innovators and research have suggested mixed cropping as a way to promote soil health. Mixing areas of different crops in the same field is another form of precision agriculture's spatial and temporal management. The simplest form of mixed cropping is strip cropping. In conventional mechanized farming use of mixed cropping practices (i.e., strip cropping, pixel cropping) is limited by labour availability, rising wage rates, and management complexity. Regenerative a... A. Al amin, J. Lowenberg-deboer, K.F. Franklin, E. Dickin, J. Monaghan, K. Behrendt

557. Proximal Hyperspectral Sensing in Plant Breeding

The use of remote sensing in plant breeding is challenging due to the large number of small parcels which at least actually cannot be measured with conventional techniques like air- or spaceborne sensors. On the one hand crop monitoring needs to be performed frequently, which demands reliable data availability. On the other hand hyperspectral remote sensing offers new methods for the detection of vegetation parameters in crop production, especially since methods for safe and efficient detecti... H. Lilienthal, P. Wilde, E. Schnug

558. Proximal Sensing of Leaf Temperature and Microclimatic Variables to Implement Precision Irrigation in Almond and Grape Crops

Irrigation decisions based on traditional soil moisture sensing often leads to uncertainty regarding the true amount of water available to the plant. Plant based sensing of water stress decreases this uncertainty. In specialty crops grown in California’s Central Valley, precision deficit irrigation based on plant water stress could be used to decrease water use and increase water use efficiency by supplying the necessary quantity of water only when it is needed by the plant. However, th... E. Kizer, S.K. Upadhyaya, F. Rojo, S. Ozmen, C. Ko-madden, Q. Zhang

559. Proximal Sensing of Penetration Resistance at a Permanent Grassland Site in Southern Finland

Proximal soil sensing allows for assessing soil spatial heterogeneity at a high spatial resolution. These data can be used for decision support on soil and crop agronomic management. Recent sensor systems are capable of simultaneously mapping several variables, such as soil electrical conductivity (EC), spectral reflectance, temperature, and water content, in real-time. In autumn 2021, we used a commercial soil scanner (Veris iScan+) to derive information on soil spatial variability for a per... H.E. Ahrends, A. Lajunen

560. Proximal, Drone, and Satellite Sensors for In-season Variable Nitrogen Rate Application in Corn: a Comparative Study of Fixed-rate and Sensor-based Approaches

Effective nitrogen (N) management is essential for optimizing corn yield and enhancing agricultural sustainability. Traditional N application methods, typically uniform split pre-plant and in-season applications, often neglect the spatial and temporal variability of N requirements across different fields and years, potentially leading to N overuse. With the rise of precision agriculture technologies, it is crucial to reassess these conventional practices. This study had two main objectives: f... A. Jakhar, A. Bhattarai, L. Bastos, G. Scarpin

561. Quantifying Boom Movement in Agricultural Sprayer Booms Using Neural Networks for Real-world Field Scenarios

Application rate errors in self-propelled agricultural sprayers remain a significant concern, necessitating a comprehensive understanding of boom movement during actual field operating scenarios. This study introduces new objectives to quantify boom movement across commercial sprayers when operated by different individuals and compares these movements among various machines. The goal is to develop a metric that identifies potential improvement needs for boom height control system. The approac... T. Kaloya, A. Sharda, A. Dalal

562. Quantifying Constant Rate and Sensor-based Variable Rate Nitrogen (N) Fertilizer Response on Crop Vigor and Yield

Agricultural fertilizer application is one of the essential components of crop production. It enhances crop growth, yield, and quality of the crop. The most widely used methods for nutrient application are the constant rate and variable rate application. An improper supply of fertilizer can potentially hamper crop growth and reduce the quality of the crop. Therefore, there is a need to select the best optimum nutrient application method for proper utilization of the nutrients. Therefore, the ... R. Singh, A. Sharda

563. Quo Vadis Precision Farming

The agriculture sector is a unique sector due to its strategic importance for both citizens and economy which, ideally, should make the whole sector a network of interacting organizations. There is an increasing tension, the like of which is not experienced in any other sector, between the requirements to assure full safety and keep costs under control, but also assure the long-term strategic interests of Europe and worldwide. In that sense, agricultural production influences, and is influenc... K. Charvat, T. Reznik, V. Lukas, K. Charvat jr., S. Horakova, M. Splichal, M. Kepka

564. R2B2 Project: Design and Construction of a Low-cost and Efficient Autonomous UGV For Row Crop Monitoring

Driving the adoption of agricultural technological advancements like Unmanned Ground Vehicles (UGVs) by small-scale farmers (SSFs) is a major concern for researchers and agricultural organizations. They aim for the adoption of precision farming (PF) by SSFs to increase crop yield to meet the increasing demand for food due to population growth. In the United States, the cost of purchasing and maintaining rugged UGVs capable of precision agricultural operations stands as a barrier to the a... J.O. Kemeshi, S. Gummi, Y. Chang

565. Rapid Assessment of Yield Using Machine Learning Models and UAV Multispectral Imagery for Soybean Breeding Plots

Advances in precision agriculture in data collection, crop monitoring, screening, and management over the 10-15 years are revolutionizing on-farm agricultural research trials. In crop breeding plots, this approach is called "High Throughput Phenotyping", which uses innovative technology to extract phenotypic data for large populations. Remote sensing has become one of the commonly used platforms for rapid acquisition of imagery data at spatial and temporal scale. Particularly, the u... A. Dua, A. Sharda, W. Schapaugh, R. Hessel, S. Rai

566. Rationale for and Benefits of a Community for On-Farm Data Sharing

Most data sets for evaluating crop production practices have too few locations and years to create reliable probabilities from predictive analytical analyses for the success of the practices. Yield monitors on combines have the potential to enable networks of farmers in collaboration with scientists and farm advisors to collect sufficient data for calculation of more reliable guidelines for crop production showing the probabilities that new or existing practices will improve the efficiency of... T. Morris, N. Tremblay, P.M. Kyveryga, D.E. Clay, S. Murrell, I. Ciampitti, L. Thompson, D. Mueller, J. Seger

567. Real Time Application of Neural Networks and Hardware Accelerated Image Processing Pipeline for Precise Autonomous Agricultural Systems

Modern agriculture is increasingly turning to automation and precision technology to optimize crop management. In this context, our research addresses the development of an autonomous pesticide spraying rover equipped with advanced technology for precision agriculture. The primary goal is to use a neural network for real-time aphid detection in Sorghum crops, enabling targeted pesticide application only to infested plants. To accomplish this, we've integrated cutting-edge technologies and... J. Raitz persch, R. Harsha chepally, N.K. Piya

568. Real-time Detection of Picking Region of Ridge Planted Strawberries Based on YOLOv5s with a Modified Neck

Robotic strawberry harvesting requires machine vision system to have the ability to detect the presence, maturity, and location of strawberries. Strawberries, however, can easily be bruised, injured, and even damaged during robotic harvest if not picked properly because of their soft surfaces. Therefore, it is important to cut or pick the strawberry stems instead of picking the fruit directly. Additionally, real-time detection is critical for robotic strawberry harvesting to adapt to the chan... Z. He, K. Manoj, Q. Zhang, S. Kshetri

569. Real-time Gauge Wheel Load Variability on Planter with Downforce Control During Field Operation

Downforce control allows planters to maintain gauge wheel load across a range of soil resistance within a field. Downforce control is typically set for a target seed depth and either set to manually or automatically control the gauge wheel load. This technology uses load cells to actively regulate downforce on individual row units by monitoring target load on the gauge wheels. However, no studies have been conducted to evaluate the variability in gauge wheel load observed during planter opera... A. Sharda, S. Badua, D. Flippo, I. Ciampitti, T.W. Griffin

570. Real-time Seed Mapping Using Direct Methods

Seed distance estimations are critical for planter evaluation and the prediction of planting parameter performance. However, these estimations are typically not conducted in real-time. In this study, we propose a real-time seed mapping approach using cameras and computer vision networks, augmented by a Kalman filter for vehicle state estimation. This process involves the transformation of pixel coordinates into real-world coordinates. We conduct a comparative analysis between these estimates ... A. Sharda, R. Harsha chepally

571. Realising the Potential of Agricultural Robotics and AI: The Ethical Challenges

Recent advances in AI and robotics may dramatically transform agriculture by greatly expanding the number of contexts in which the techniques of precision agriculture may be applied. Inevitably, this next agricultural revolution will generate profound ethical issues: opportunities as well as risks. Clever applications of AI and robotics may allow agriculture to be more sustainable by facilitating more precise applications of water, fertilisers, and herbicides. Robots may take some of the drud... R. Sparrow

572. Recovery Mechanism for Real-time Precision Agriculture Sensor Networks: a Case Study

Variable rate technologies are lagging behind other precision agriculture technologies in terms of farmer adoption, and sensor networks have been identified as a necessary step to implement these improvements. However, sensor networks face many issues in terms of cost, flexibility, and reliability. In rugged outdoor environments, it cannot be assumed that a sensor network will maintain constant connectivity to a monitoring interface, even if data is still being collected onsite. This paper pr... L. Hunt, M. Everett, J. Shovic

573. Rectification of Management Zones Considering Moda and Median As a Criterion for Reclassification of Pixels

Management zones (MZ) make economically viable the application of precision agriculture techniques by dividing the production areas according to the homogeneity of its productive characteristics. The divisions are conducted through empirical techniques or cluster analysis, and, in some cases, the MZ are difficult to be delimited due to isolated cells or patches within sub-regions. The objective of this study was to apply computational techniques that provide smoothing of MZ, so as to become v... N.M. Betzek, E.G. Souza, C.L. Bazzi, K. Schenatto, A. Gavioli, M.F. Maggi

574. Regional Usefulness of Nitrogen Management Zone Delineation Tools

In the Northern Plains of Montana, North Dakota and Minnesota, a number of site-specific tools have been used to delineate nitrogen management zones. A three-year study was conducted using yield mapping, elevation measurements, satellite imagery, aerial Ektochrome® photography, and soil EC to delineate nitrogen management zones and compare these zones to residual fall soil nitrate. At most of the sites, variable-rate N was applied and compared with uniform N application. The site-specific... D. Franzen, F. Casey, J. Staricka, D. Long, J. Lamb, A. Sims, M. Halvorson, V. Hofman

575. Relationship Between Water Use Efficiency, Daily Stomatal Conductance Trend and Evaporation of Maize and Soybean Crops

Water Use Efficiency (WUE) represents the biomass production per unit of water and is commonly affected by temperature, carbon dioxide concentration, and water availability. Plants regulate the water transpiration efficiency through the opening and closing of stomata. Farmers can save water and maintain yield by improving crop's WUE during the period of drought through proper field management. The calculation of WUE requires the information of crop weight and irrigation volume, which is d... J. Zhang, N. Chamara, G. Bai, Y. Ge

576. Relationship of Activity and Temperature of Dairy Calves As Measured by Indwelling Rumen Boluses

Circadian rhythm of body temperature is naturally occurring in animals with a lower temperature at dawn and higher at dusk. In the past, this work was manually completed by a person using rectal temperature with temperature recorded every 2 or 3 hours. Rumen indwelling boluses allow for continuous temperature monitoring without human intervention. Human intervention can increase animal stress which can elevate temperature. Current literature indicates that the animal’s body temperature ... J.M. Hartschuh, J.P. Fulton, S.A. Shearer, B.D. Enger, G.M. Schuenemann

577. Remote and Proximal Sensing for Sustainable Water Use in Almond Orchards in Southeast Spain in a Digital Farming Context

The increasing expansion of irrigated almond orchards in regions of southeast Spain, facing water scarcity, underscores the need for a more effective and precise monitoring of the crop water status to optimize irrigation scheduling and improve crop water use efficiency. Remote and proximal sensing, combining visible, multispectral and thermal capabilities at different scales allows to estimate water needs, detect and quantify crop water stress, or identify different productivity zones within ...

578. Remote Sensing-based Biomass Maps for an Efficient Use of Fertilizers

For decades the main objective of farmers was to get the highest yields from their farmland. Nowadays, quality of agricultural products is becoming more and more important for the largest returns. In addition, the effects on our environment are also becoming important. These put increasing limitations on modern agriculture. So-called site-specific management can optimize the input of, for instance, nutrients and pesticides to the need of the plants. In this study, the objective was to study w... J.G. P.w clevers, K.H. Wijnholds, J.N. Jukema

579. Report from Finland - How We Speed Up Innovation Uptake in Agriculture in Finland

Finnish agriculture is rapidly digitalizing. While the number of farms is decreasing, those that remain are increasingly adopting new technologies. Finns have a tradition of being early adopters of mobile technologies, with the Finnish phone company Nokia being a notable forerunner. However, in agriculture, users tend to be more conservative, resulting in lower than expected adoption rates of Precision Farming. The reasons for this are not only financial but also related to the usability issu... H.E. Haapala

580. Report on Research and Extension of Precision Agriculture in Japan

The objective of this report is to present the current status of precision agriculture and smart agriculture in Japan. As of 2023, there are approximately 150 precision agriculture-related venture companies in Japan, and the number is increasing every year. Research related to precision agriculture is mainly conducted by the IT and Mechatronics Subcommittee of the Japanese Society for Agricultural and Biological Engineering, which consists of about 1,... E. Morimoto

581. Response of Canola and Wheat to Application of Enhanced Efficiency Nitrogen Fertilizers on Contrasting Management Zones

Investment on nitrogen (N) fertilizers is a major cost of growers, and variable rate (VR) application of N fertilizers could help optimize its usage. In the growing season of 2023, field experiments were conducted at four sites (i.e., Watrous – Saskatchewan SK and two fields in the vicinity of Strathmore, Alberta AB, Canada). The main objectives were to (i) determine performance of Enhanced Efficiency N Fertilizers - EENF (i.e., Coated urea, urea with double inhibitors - DI, urea mixed ... H. Asgedom, G. Hehar, C. Willness, W. Anderson, H. Duddu, P. Mooleki, J. Schoenau, M. Khakbazan, R. Lemke, E. derdall, J. Shang, K. Liu, J. Sulik, E. Karppinen, I. Mbakwe

582. Response of Soybean Cultivars According to Management Zones in Southern Brazil

The positioning of soybean cultivars on fields according your environmental response is new strategy to obtain high soybean yields. The aim of this study was to investigate the agronomic response of six soybean cultivars according management zones in Southern Brazil. The study was conducted in 2013/2014 and in two fields located in Boa Vista das Missões, Rio Grande do Sul, Brazil. The experimental design was a randomized complete block in a factorial arrangement (3x6), with three manag... T.J. Amado, A.L. Santi, G.M. Corassa, M.B. Bisognin, R. Gaviraghi, J.L. Pires

583. Retrieving Crops' Quantitative Biophysical Parameters Through a Newly Developed Multispectral Sensor for UAV Platforms

Today’s intensive agricultural production needs to increase its efficiency in order to keep its profitability in the current market of decreasing prices on one hand, and to reduce the environmental impact on the other. Crop growers are starting to adopt side dressing nitrogen fertilization as part of their fertilization programs, for which they need accurate information about biomass development and nitrogen condition in the crop. This information is usually acquired through ground samp... A. Pimstein, Y. Zur, M. Le roux

584. Retrieving Nitrogen Levels in Almond Trees Using Hyperspectral Data at Leaf and Canopy Level

Almonds are a crucial specialty crop in California, dominating approximately 80 percent of the global almond supply. To enhance nitrogen usage efficiency, reduce groundwater contamination, and optimize resource allocation, ongoing research has been dedicated to improving nitrogen management practices in almond cultivation. This study specifically focused on the retrieval of nitrogen levels with uncertainty estimation at both the leaf and canopy levels of almond trees. Hyperspectral data was c... M. Chakraborty, A. Pourreza

585. RMAPs: an Integrated Tool to Delimitate Homogeneous Management Zones

Management zones are one of the most studied methods in precision agriculture to optimize crop yield from the soil, plant, management, and climate input parameters. We present Rmaps, an R package that integrates soil and crop yield spatial variability using geostatistical methods and one-hidden-layer perceptron (OHLP) to identify how input parameters influence crop yield and delimitate homogenous zones. From georeferenced data of soil, plant, management, climate, and crop yield parameters, Rm... E. Erazo, C. Mosquera, O. Ochoa

586. Robot Safety Issues in Field Crops - EU Regulatory Issues and Technical Aspects

The use of robots in Precision Agriculture is becoming of great interest, but they introduce a new kind of risk in the field due to their self-acting and self-driving capability. Safety issues appear with respect to people working in the same field in human-robot collaboration (HRC) framework or to the accidental presence of humans or animals. A robot out of control may also invade other areas causing unpredictable harm and damage. Currently, the safety of highly automated agricultu... M. Canavari, P. Lattanzi, G. Vitali, L. Emmi

587. Robustness of Pigment Analysis in Tree Fruit

The non-destructive application of spectrophotometry for analyzing fruit pigments has become a promising tool in precise fruit production. Particularly, the pigment contents are interesting to the growers as they provide information on the harvest maturity and fruit quality for marketing. The absorption of chlorophyll at its Q band provides quantitative information on the chlorophyll pool of fruit. As a challenge appears the in-situ measurement at varying developmental stage of the fruit due ... M. Zude-sasse, C. Regen, J. Käthner

588. Safety and Certification Considerations for Expanding the Use of UAS in Precision Agriculture

The agricultural community is actively engaged in adopting new technologies such as unmanned aircraft systems (UAS) to help assess the condition of crops and develop appropriate treatment plans.  In the United States, agricultural use of UAS has largely been limited to small UAS, generally weighing less than 55 lb and operating within the line of sight of a remote pilot.  A variety of small UAS are being used to monitor and map crops, while only a few are being used to apply agricul... H. Verstynen, K. Hayhurst, J. Maddalon, N. Neogi

589. Sampling Bumble Bees and Floral Resources Using Deep Learning and UAV Imagery

Pollinators, essential components of natural and agricultural systems, forage over relatively large spatial scales. This is especially true of large generalist species, like bumble bees. Thus, it can be difficult to estimate the amount and diversity of floral resources available to them. Floral cover and diversity are often estimated over large areas by extrapolation from small scale samples (e.g., a 1-m quadrat) but the accuracy of such estimates can vary depending on the spatial patchiness ... B. Spiesman, I. Grijalva, D. Holthaus, B. Mccornack

590. Sampling-based on Plant Vigor Zones As a Strategy for Creating Soil Attribute Maps

Mapping agronomically relevant soil properties for fertilizer recommendation remains challenging in precision agriculture. Traditionally, this mapping is conducted through soil sampling on a regular grid basis, where points are equally spaced primarily to ensure spatial coverage. However, directing soil sampling points based on plant vigor may be more efficient in capturing soil variability that directly affects plant development. Several commercial platforms offer solutions for defining mana... D.D. Melo, T.L. Brasco, I.A. Da cunha, S.G. Castro, L.R. Amaral

591. Satellite-based On-farm Variable Rate Nitrogen Management on and Main Spatial Drivers of Cotton Yield, Nitrogen Use Efficiency, and Profitability

In the United States of America, Georgia is the second largest cotton producing state, responsible for 2.6 million bales produced in 2022. In Georgia, cotton is the most economically important row crop, with ~514,000 ha harvested and $USD 1.5 billion in economic impact in the state economy in 2022. Nitrogen (N) fertilizer is one of the main inputs required to optimize cotton lint yield and quality, while also being a large input cost representing ~25% of variable costs. As a non N-fixing crop... L. Bastos, W. Porter, G. Scarpin

592. Scaling Up Window-based Regression for Crop-row Detection

Crop-row detection is a central element of weed detection and agricultural image processing tasks. With the increased availability of high-resolution imagery, a precise locating of crop rows is becoming practical in the sense that the necessary data are commonly available. However, conventional image processing techniques often fail to scale up to the data volumes and processing time expectations. We present an approach that computes regression lines ... A.M. Denton, G.E. Hokanson, P. Flores

593. Seasonal Patterns of Vegetative Indices Over Cropping Systems

Remote sensing of reflectance in the visible and near-infrared portions of the spectrum has been used for agronomic applications for a number of years. The combination of different wavelengths into vegetative indices have proven useful for a variety of applications that range from biomass, leaf area, leaf chlorophyll, yield, crop residue, and crop damage. To help refine our understanding of vegetative indices studies were conducted on corn (Zea mays L.), soybean (Glycine max (L.) Merr.), whea... J.L. Hatfield, J.H. Prueger

594. Securing Agricultural Data with Encryption Algorithms on Embedded GPU Based Edge Computing Devices

Smart Agriculture (SA) has captured the interest of both the agricultural business and the scientific community in recent years. Overall, SA aims to help the agricultural and food industry to avoid crop failures, loss of revenues as well as help farmers use inputs (such as fertilizers and pesticides) more efficiently by utilizing Internet of Things (IoT) devices and computing systems. However, rapid digitization and reliance on data-driven technologies create new security threats that can def... M. Alahe, J.O. Kemeshi, Y. Chang, K. Won, X. Yang, M. Sher

595. Securing Agricultural Imaging Data in Smart Agriculture: a Blockchain-based Approach to Mitigate Cybersecurity Threats and Future Innovations

Smart agriculture (SA) is a new technology that combines the Internet of Things (IoT) with a variety of smart devices, such as drones, unmanned ground vehicles (UGVs), and computer systems. The integration of technology improvements in SA has led to an increase in cybersecurity concerns, specifically pertaining to the protection of sensitive agricultural image data. It’s necessary to better understand SA network systems; establish stronger network structures; identify different types an... M. Alahe, S. Gummi, J.O. Kemeshi, Y. Chang

596. Seed Localization System Suite with CNNs for Seed Spacing Estimation, Population Estimation and Doubles

Proper seed placement during planting is critical to achieve uniform emergence which optimizes the crop for maximum yield potential. Currently, the ideal way to determine planter performance is to manually measure plant spacing and seeding depth. However, this process is both cost- and labor-intensive and prone to human errors. Therefore, this study aimed to develop seed localization system (SLS) system to measure seed spacing and seeding depth and providing the geo-location of each planted s... A. Sharda, R. Harsha chepally

597. Seeding and Planting Plots for Crop Performance Evaluation Using Gps-rtk Auto Steering

Crop performance evaluation plots are seeded both on and off the University of Nebraska West Central Research and Extension Center. Plots off the Center must match the producer’s rows for pesticide application, cultivation, ditching, irrigation, fertilization and any other operations performed in the fields. With row crops the producer blank-plants the plot area before we can follow up with planting the plots. This means that we have to wait for the producer to plant in the field. Blank... R.N. Klein, J.A. Golus, A.S. Cox

598. Selection and Utility of Uncooled Thermal Cameras for Spatial Crop Temperature Measurement Within Precision Agriculture

Since previous research used local, single-point measurements to indicate crop water stress, thermography is presented as a technique capable of measuring spatial temperatures supporting its use for monitoring crop water stress. This study investigated measurement accuracy of uncooled thermal cameras under strict environmental conditions, developed hardware and software to implement uncooled thermal cameras and quantified intrinsic properties that impact measurement accuracy and repeatability... D.L. Mangus, A. Sharda

599. Semiautomatization in Open Source Software of a Method for Monitoring the Land Cover Change with GEE and Sentinel-2

Land cover change is a dynamic process that unfolds spatially and temporally. As such, it is imperative to develop semi-automatic methods within freely available software to enhance processing efficiency and reduce costs. The amalgamation of open-source applications, platforms, and software for satellite image processing has emerged as a compelling alternative, fostering advancements in land cover change classification and monitoring. This study introduces a semi-automated methodology using t... S.A. Rubaino sosa, Y. Rubiano, J.H. Bernal riobo

600. Sensor Based Fertigation Management

Sensor-based fertigation management (SBFM) is a relatively new technology for directing nitrogen (N) decisions, specifically tailored for delivery of N via center pivot irrigation systems (fertigation). The development of SBFM began in 2018 at the University of Nebraska-Lincoln with the help of cooperating producers across the state. Over two dozen field sites provided testbeds for the development and evaluation of the technology. The key technique in this fertigation approach is th... J. Stansell, J.D. Luck, T. Cross, K.J. Bathke, T. Smith

601. Sensor Based Soil Health Assessment

Quantification and assessment of soil health involves determining how well a soil is performing its biological, chemical, and physical functions relative to its inherent potential. Due to high cost, labor requirements, and soil disturbance, traditional laboratory analyses cannot provide high resolution soil health data. Therefore, sensor-based approaches are important to facilitate cost-effective, site-specific management for soil health. In the Central Claypan Region, visible, near-infrared ... K. Veum, K. Sudduth, N. Kitchen

602. Sensor-based Nitrogen Applications Out-performed Producer-chosen Rates for Corn in On-farm Demonstrations

Optimal nitrogen fertilizer rate for corn can vary substantially within and among fields.  Current N management practices do not address this variability.  Crop reflectance sensors offer the potential to diagnose crop N need and control N application rates at a fine spatial scale.  Our objective was to evaluate the performance of sensor-based variable-rate N applications to corn, relative to constant N rates chosen by the producer.  Fifty-five replicated on-farm demonstrat... P. Scharf, K. Shannon, K. Sudduth, N. Kitchen

603. Sensor-based Technologies for Improving Water and Nitrogen Use Efficiency

 Limited reports exist on identifying the empirical relationships between plant nitrogen and water status with hyperspectral reflectance. This project is aiming to develop effective system for nitrogen and water management in wheat. Specifically: 1) To evaluate the effects of nitrogen rates and irrigation treatments on wheat plant growth and yield; 2) To develop methods to predict yield and grain protein content in varying nitrogen and water environments, and to determine the minimum nit... O.S. Walsh, K. Belmont, J. Mcclintick-chess

604. Sensor-based Variable-rate N on Corn Reduced Nitrous Oxide Emissions

More nitrogen fertilizer is applied to corn than to all other U.S. crops combined, contributing to atmospheric heat trapping when nitrous oxide is produced.  Higher nitrogen rate is well known to increase nitrous oxide emissions, and earlier N application time may increase the window during which nitrous oxide can form.  An experiment was initiated in 2012 comparing nitrogen management and drainage effects on corn yield and nitrous oxide emissions.  Two nitrogen treatments... P. Scharf

605. Sentinel Fertigation - Sponsor Presentation

Sentinel’s N-Time software leverages imagery and agronomic data to provide nitrogen application scheduling and rate recommendations to agronomists and farmers. Recommendations from the system have demonstrated profitability improvements of $24/ac and Nitrogen use efficiency (NUE) improvements of 30% in on-farm research studies since 2021. This presentation will discuss the function of N-Time, highlight the advantages of the management system it enables, and briefly discuss on-farm resea... J. Stansell

606. Shifting Fertiliser Response Zones in a Four Year, Whole-paddock Cereal Cropping Experiment.

Precision agriculture in cropping areas of dryland Australia has focused on managing within production zones. These are ideally stable, possibly soil- and topography-based areas within fields. There are many different ideas on how to delimit and implement zones, and a four year whole-field experiment, with low, medium and high treatment philosophies applied per 9m seeder/harvester width across the entire field, was established to explore how zones might best be established and used. The treat... B. Jones, T. Mcbeath, N. Wilhelm

607. Should One Phosphorus Extraction Method Be Used for VRT Phosphorus Recommendation in the Southern Great Plains?

Winter Wheat has been produced throughout the southern Great Plains for over 100 years.  In most cases this continuous production of mono-culture lower value wheat crop has led to the neglect of the soils, one such soil property is soil pH. In an area dominated by eroded soils and short term leases, Land-Grant University wheat breeders have created lines of winter wheat which are aluminum tolerant to increase production in low productive soils.  Now the fields in this region can hav... D.B. Arnall, S. Phillips, C. Penn, P. Watkins, B. Rutter, J. Warren

608. Should We Increase or Decrease the Fertilization in the Zones with the Highest Crop Productivity Potential?

Introduction. In traditional farming, fertilizers are applied homogeneously on the agricultural fields taking into account the average crop recommendation. As most fields are not homogeneous, this results in overfertilization of certain zones and underfertilization of other zones. The excess of nitrate leaches to the surface and groundwaters which causes problems with the water quality. Precision fertilizer management has been proposed to reduce these negative e... A. Tsibart, A. Postelmans, J. Dillen, A. Elsen, G. Van de ven, W. Saeys

609. Simulating Climate Change Impacts on Cotton Yield in the Texas High Plains

Crop yield prediction enables stakeholders to plan farming practices and marketing. Crop models can predict crop yield based on cropping system and practices, soil, and other environmental factors. These models are being used for decision support in agriculture in a variety of ways. Cultivar selection, water and nutrient input optimization, planting date selection, climate change analysis and yield prediction are some of the promising area of applications of the models in field level farm man... B. Ghimire, R. Karn, O. Adedeji, G. Ritchie, W. Guo

610. Simulation of Curiosity and Exo Mars Rovers on Agriculture Terrain

Improving agricultural productivity is one of the biggest challenges Agriculture and Engineering face. A possible solution is the creation of soil databases and/or maps to apply precision agriculture techniques, aiming to produce more in the same land, using less agricultural supplies. This practice may be developed with the help of rovers applied to e.g. agricultural data collect, mapping, scouting and supply tasks. However, the rover needs to move and adapt to the terrain to obtain a real a... J.F. Archila-diaz, M. Becker

611. Simultaneously Estimating Crop Biomass and Nutrient Parameters Using UAS Remote Sensing and Multitask Learning

Rapid and accurate estimation of crop growth status and nutrient levels such as aboveground biomass, nitrogen, phosphorus, and potassium concentrations and uptake is critical with respect to precision agriculture and field-based crop monitoring. Recent developments in Uncrewed Aircraft Systems (UAS) and sensor technologies have enabled the collection of high spatial, spectral, and temporal remote sensing data over large areas at a lower cost. Coupled deep learning-based modeling approaches wi... P. Kovacs, M. Maimaitijiang, B. Millett, L. Dorissant, I. Acharya, U.U. Janjua, K. Dilmurat

612. Single-strip Spatial Evaluation Approach: a Simplified Method for Enhanced Sustainable Farm Management

On-farm experimentation (OFE) plays a pivotal role in evaluating and validating the effectiveness of agricultural practices and products. The results of OFE enable farmers to act and make changes that can enhance the farm’s economic and environmental sustainability. Experimental designs can be a barrier to the adoption of OFE. The conventional approach often involves randomized complete block designs with 3 to 5 replications in the field, which can be space-intensive and disrupt workflo... S. Srinivasagan, Q. Ketterings, M. Marcaida, S. Shajahan, J. Ramos-tanchez, J. Cho, , L. Thompson, J. Guinness, R. Goel

613. Site Specific Costs Concerning Machine Path Orientation

Computer algorithms have been created to simulate in advance the orientation/pattern of a machine operation on a field. Undesired impacts were obtained and quantified for these simulations, like: maneuvering and overlap of inputs in headlands; servicing of secondary units; and soil loss by water erosion. While the efforts could minimize the overall costs, they disregard the fact that these costs aren’t uniformly distributed over irregular fields. The cost of a non-productive machine pro... M. Spekken, J.P. Molin, T.L. Romanelli, M.N. Ferraz

614. Site Specific Evaluation of Dynamic Nitrogen Recommendation Tools

Management tools are a potential solution for increased profit and N use efficiency (NUE) in corn production. Most previous studies evaluating these tools used small plot research which does not accurately represent large scale performance and inhibits adoption. Two dynamic model-based N management tools, which were commercially available in 2021 and 2022 (Adapt-N and Granular), were tested at fifteen on-farm research locations in Nebraska. The objective of this study were to evaluate the sit... S. Norquest, L. Puntel, G. Balboa, L. Thompson

615. Site-specific Evaluation of Sensor-based Winter Wheat Nitrogen Tools Via On-farm Research

Crop producers face the challenge of optimizing high yields and nitrogen use efficiency (NUE) in their agricultural practices. Enhancing NUE has been demonstrated by adopting digital agricultural technologies for site-specific nitrogen (N) management, such as remote-sensing based N recommendations for winter wheat. However, winter wheat fields are often uniformly fertilized, disregarding the inherent variability within the fields. Thus, an on-farm evaluation of sensor-based N tools is needed ... J. Cesario pinto, L. Thompson, N. Mueller, T. Mieno, L. Puntel, P. Paccioretti, G. Balboa

616. Site-specific Irrigation of Peanuts on a Coastal Plain Field

Irrigator-Pro is an expert system that prescribes irrigation for corn (Zea mays L.), cotton (Gossypium hirsutum L.) and peanut (Arachis hypogaea). We conducted an experiment in 2007 to evaluate Irrigator-Pro as a tool for variable rate irrigation of peanut using a site-specific center pivot irrigation system. Treatments were irrigation of whole plots based on the expert system, irrigation of individual soils within plots based on the expert system, irrigation of ind...

617. Site-specific Scale Efficiency Determined by Data Envelopment Analysis of Precision Agriculture Field Data

Since its inception and acceptance as a benchmarking tool within the economics literature, data envelopment analysis (DEA) has been used primarily as a means of calculating and ranking whole-farm entities marked as decision making units (DMU) against one another.  Within this study, instead of ranking the entire farm operation against similar peers that encompass the study, individual data points from within the field are evaluated to analyze the site-specific technical efficiencies esti... J.L. Maurer, T.W. Griffin, A. Sharda

618. Small UAS Integrated Sensing Tools for Abiotic Stress Monitoring in Irrigated Pinto Beans

Precision agriculture is a practical approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of specific and high resolution crop data at critical growth stages is a key for real-time data driven decision support for precision agriculture management during the production season. The goal of this study was to evaluate the feasibility of using small unmanned aerial system (UAS) integrated remote sensing tools to monitor the abiotic stress of eight i... L. Khot, J. Zhou, R. Boydston, P.N. Miklas, L. Porter

619. Smart Agriculture: A Futuristic Vision of Application of the Internet of Things (IoT) in Brazilian Agriculture

With the economy based on agribusiness, Brazil is an important representative on the world stage in agricultural production, either in terms of quantity or cultivated diversity due to a scenario with vast arable land and favorable climate. There are many crops that are adapteble to soils of the country. Despite the global representation, it is known that the Brazilian agricultural production does not yet have a modern agriculture by restricting the use of new technologies to farmers with bett... C.L. Bazzi, R. Araujo, E.G. Souza, K. Schenatto, A. Gavioli, N.M. Betzek

620. Smart Food Oases: Development of a Distributed Point-to-point Urban Food Ecosystem in Food Desert Areas

Urban agriculture has been getting much attention in the past decade as a solution to overcome food insecurity and accessibility of food for urban residents and to have better green environments in cities. Urban agriculture is expected to provide better nutrients to residents, reduce transportation and environmental costs, and help urban dwellers access food efficiently. The present study is to build a collaborative ecosystem among urban growers/producers and create bridges from these farmers... J. Lee, S. Song, S. Oh, K. Krishnaswamy, C. Sun, Y. Adu-gyamfi

621. SmartAgriHubs FIE20 - Groundwater and Meteo Sensors and Earth Observation for Precision Agriculture

The solution developed under the SmartAgriHubs project in the scope of the Flagship Innovation Experiment FIE20 Groundwater and meteo sensors is an expert system to support farmers in decision-making process and planning process of field interventions. This FIE20 solution integrates various data sources and different analytical processes in a complete system and provides users an easy-to-use web map application as a common user interface. The FIE20 system integrates components developed durin... K. Charvat, M. Kepka, R. Berzins, F. Zadrazil, D. Langovskis, M. Musil

622. SMARTfarm Learning Hub: Next Generation Precision Agriculture Technologies for Agricultural Education

The industry demands on higher education agricultural students are rapidly changing. New precision agriculture technologies are revolutionizing the farming industry but the education sector is failing to keep pace. This paper reports on the development of a key resource, the SMARTfarm Learning Hub (www.smartfarmhub.com) that will increase the skill base of higher education students using a range of new agricultural technologies and innovations. The Hub is a world first; it links real industry... M. Trotter, S. Gregory, T. Trotter, T. Acuna, D. Swain, W. Fasso, J. Roberts, A. Zikan, A. Cosby

623. Snap-shot Hyperspectral Camera for Potassium Prediction of Peach Trees Using Multivariate Analysis

Hyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or pest disease detection. As sensing technology advances, measuring nutrient levels and disease detection also progresses. This study aimed to predict the levels of potassium (K) content in peach leaves with the new snapshot hyperspectral camera. The study was conducted at the Clemson University Musser Fruit Research Farm (Seneca, SC, USA, 34.... J.J. Maja, M. Abenina, M. Cutulle, J. Melgar, H. Liu

624. Soil and Crop Factors to Site-specific Nitrogen Management on Sugarcane Fields

Nitrogen (N) is one of the most widely used fertilizers in crops and the most harmful to the environment. The increase fertilizers consumption, mainly N sources (one of the most widely fertilizer used in sugarcane fields), is one of the main factors underlying the sustainability of the entire production process. Currently, N recommendations in sugarcane are based only on the expected yield. However, there is little agronomic support for nitrogen (N) recommendations based on expected yield, de... G.M. Sanches, R. Otto, F.R. Pereira

625. Soil Attributes Estimation Based on Diffuse Reflectance Spectroscopy and Topographic Variability

The local management of crop areas, which is the basic concept of precision agriculture, is essential for increasing crop yield. In this context, diffuse reflectance spectroscopy (DRS) and digital elevation modelling (DEM) appears as an important technique for determining soil properties, on an adequate scale to agricultural management, enabling faster and less costly evaluations in soil studies. The objective of this work was to evaluate the use of DRS together with topographic parameters fo... J.V. fontenelli, L.R. Amaral, J.M. Demattê, P.G. Magalhães, G. Sanches

626. Soil Microbial Biomass and Bacterial Diversity Enhanced Through Winter Cover Cropping in Paddy Fields

Rice production is typically based on input-intensive and often environmentally unsustainable monoculture system. Alternatives are increasing, such as fallow cover cropping and rice–fish coculture (RFC). However, options of fallow cover cropping in RFC are scarcely explored, and the soil microbial response strategies to cover cropping remain unclear. Here, we evaluated soil-plant-microbe interactions under three cover cropping systems: Chinese milk vetch single cropping (CM), rapeseed s... S. Cai, S. Xu, D. Zhang, H. Zhu, L. Longchamps

627. Soil Moisture Variability on Golf Course Fairways Across the United States: an Opportunity for Water Conservation with Precision Irrigation

Fairways account for an average of 11.3 irrigated hectares on each of the 15,000+ golf courses in the US. Annual median water use per hectare on fairways is between ~2,800,000 and 14,000,000 liters, depending on the region. Conventional fairway irrigation relies on visual observation of the turfgrass, followed by secondary considerations of short-term weather forecasts, which oftentimes lead to “blanket” applications to the entire area. The concept of precision irrigation is a str... C. Straw, C. Bolton, J. Young, R. Hejl, J. Friell, E. Watkins

628. Soil Moisture, Organic Matter and Potassium Influences on Eca Measurement

Spatial variability of soil physical and chemical properties is a fundamental element of site-specific soil and crop management. Since its early implementation in agriculture as a method of measuring soil salinity, the acceptance of Apparent Electrical Conductivity (ECa) in agriculture has been popular as a method of determining the spatial variability of soil physical and chemical properties that influence the ECa estimates. It was the objective of this study to examine the spatial-temporal ... R.R. Struthers, C.J. Johannsen, D.K. Morris

629. Soil Variability Mapping with Airborne Gamma-ray Spectrometry and Magnetics

The knowledge of spatial distribution of agricultural soils physical and chemical properties is critical for profitable and sustainable crop and food production. The collection of soil data presents however obvious problems arising from sampling a dense, opaque and very heterogeneous medium. Conventional methods consisting of ground-based grid survey are laborious, expensive and lack appropriate spatial resolution to allow best farm management decision. Over the past 50 years, airborne geophy... L. Ameglio, E. Stettler, D. Eberle

630. Soil, Landscape, and Weather Affect Spatial Distributions of Corn Population and Yield

As more planters are equipped with the technology to vary seeding rate, evaluation of the within-field relationships between plant stand density (or population) and yield is needed. One aspect of this evaluation is determining how stand loss and yield are related to soil and landscape factors, and how these relationships vary with different weather conditions. Therefore, this research examined nine site-years of mapped corn yield, harvest population, and soil and landscape data obtained for a... K.A. Sudduth, N.R. Kitchen, L.S. Conway

631. SoilView, LLC

SoilView is an independent provider specializing in precision sampling and field services for agriculture retail, research groups, universities, and the evolving carbon market. Our areas of expertise include sampling for soil nutrients, carbon sampling, soil health and biology, and custom sampling processes for field research. We aim to remove the burden of sample collection for our customers by expertly managing all steps from field collection to final data delivery.   Our... R. Shorkey

632. Sources of Information to Delineate Management Zones for Cotton

Cotton in Brazil is an input-intensive crop. Due to its cultivation in large fields, the spatial variability takes an important role in the management actions. Yield maps are a prime information to guide site-specific practices including delineation of management zones (MZ), but its adoption still faces big challenges. Other information such as historical satellite imagery or soil electrical conductivity might help delineating MZ as well as predicting crop performance. The objective of this w... R.G. Trevisan, M.T. Eitelwein, A.F. Colaço, J.P. Molin

633. Soybean Production Components As Indicators of Soil Variability As a Subsidy for Precision Agriculture

The soil variability in its physical, chemical and biological parameters can be analyzed using direct methods applicable to each variable studied. Plant responses, manifested in the establishment of the final population, biomass production and grain productivity can reflect the soil conditions, associating them with the variability observed in the area. Localized soil management and the use of machines with variable rate applications, including drones for applications in specific sites, depen... E. Apolinário, W.J. Souza

634. Soybean Variable Rate Planting Simulator Using Economic Scenarios

Soybean seed costs have increased considerably over the past 15 years, causing a growing interest in variable rate planting (VRP) to optimize seeding rates within soybean fields. We developed a publicly available online Soybean Variable Rate Planting Simulator (http://analytics.iasoybeans.com/cool-apps/SoybeanVRPsimulator/) tool to help farmers, agronomists, and other agriculturalists to understand the essential prerequisite agronomic or economic conditions necessary for profitable VRP implem... B. Mcarthor , A. Prestholt, P. Kyveryga

635. Sparse Coding for Classification Via a Locality Regularizer: with Applications to Agriculture

High-dimensional data is commonly encountered in various applications, including genomics, as well as image and video processing. Analyzing, computing, and visualizing such data pose significant challenges. Feature extraction methods become crucial in addressing these challenges by obtaining compressed representations that are suitable for analysis and downstream tasks. One effective technique along these lines is sparse coding, which involves representing data as a sparse linear combination ... A. Tasissa, L. Li, J.M. Murphy

636. Spatial Analysis of Soil Moisture and Turfgrass Health to Determine Zones for Spatially Variable Irrigation Management

The Western United States is currently experiencing a “Mega Drought”. This makes efficient water use more important than ever. Turfgrass is a major vegetation type in urban areas and performs many ecosystem services such as cooling through evapotranspiration, fixing carbon from the atmosphere and reducing wild-fire risk. There are now more acres of irrigated turfgrass (>40 million) in the USA than irrigated corn, wheat and fruit trees combined (Milesi et al., 2005). It has been... R. Kerry, S. Shumate, B. Ingram, K. Hammond, D. Gunther, R. Jensen, S. Schill, N. Hansen, B. Hopkins

637. Spatial and Temporal Factors Impacting Incremental Corn Nitrogen Fertilier Use Efficiency

Current tools for making crop N fertilizer recommendations are primarily based on plot and field studies that relate the recommendation to the economic optional N rate (EONR).  Some tools rely entirely on localized EONR (e.g., MRTN). In recent years, tools have been developed or adapted to  account for within-field variation in crop N need or variable within season factors. Separately, attention continues to elevate for how N fertilizer recommendations might account for environmenta... N.R. Kitchen, C.J. Ransom, J.S. Schepters, J.L. Hatfield, R. Massey

638. Spatial and Temporal Variability of Soil Biological and Chemical Parameters Following the Introduction of Cover Crops into a Conventional Corn-cotton Rotational System

Methods to characterize soil microbial diversity and abundance are labor intensive and require destructive sampling that incurs a per unit cost. There are advantages to replacing current methods with remote sensing approaches; the most obvious of which is spatially explicit representation of microbes on agricultural landscapes. Such a method will ultimately address open questions related to (1) the spatial scale of variability in soil microbial activity, and (2) the behavior of microbes in co... J. Czarnecki, J.P. Brooks, M.C. Reeks, J. Hu

639. Spatial and Temporal Variation of Soil Nitrogen Within Winter Wheat Growth Season

This study aims to explore the spatial and temporal variation characteristics of soil ammonium nitrogen and nitrate nitrogen within winter wheat growth season. A nitrogen-rich strip fertilizer experiment with eight different treatments was conducted in 2014. Soil nitrogen samples of 20-30cm depth near wheat root were collected by in-situ Macro Rhizon soil solution collector then soil ammonium nitrogen and nitrate nitrogen content determined by SEAL AutoAnalyzer3 instrument. Classical statisti... X. Song, G. Yang, Y. Ma, R. Wang, C. Yang

640. Spatial Distribution of Dry Matter in Avocado Fruits and Its Relationship with Fruit Load

The quality and post-harvest life of avocado fruits is strongly conditioned by their oil content, accumulated before harvest. Oil content can be estimated through the dry matter content of the fruit. Thus, to start the harvest, a minimum of 22% dry matter (DM) must be reached, with an optimum between 22 and 28%, while with a DM above 28% the fruit loses its storage condition. The spatial variability of the dry matter of avocado fruits was studied in an 8-hectare field. A 20-poi... H.P. Poblete, R.A. Ortega

641. Spatial Patterns of Nitrogen Response Within Corn Production Fields

Corn (Zea mays L.) yield response to nitrogen (N) application is critical to being able to develop management practices that reduce N application or improve N use efficiency. Nitrogen rate studies have been conducted within small plots; however, there have been few field scale evaluations. This study was designed to evaluate N response across 14 corn fields in central Iowa using different rates of N applied in strips across fields. These fields ranged in size from 15 to 130 ha with N... J.L. Hatfield

642. Spatial Predictive Modeling to Quantify Soybean Seed Quality Using Remote Sensing and Machine Learning

In recent years, the advancement of artificial intelligence technologies combined with satellite technology is revolutionized agriculture through the development of algorithms that help producers become more sustainable. This could improve the conditions of farmers not only by maximizing their production and minimizing environmental impact but also due to better economic benefits by allowing them to access high-value-added markets. Furthermore, the use of predictive tools that could improve t... C. Hernandez, P. Kyveryga, A. Correndo, A. Prestholt, I. Ciampitti

643. Spatial Variability and Correlations Between Soil Attributes and Productivity of Green Corn Crop

In Brazil, the progressive development in the cultivation of the corn for consumption in the green stadium stands by the relevant socio-economic role that this related to multiple applications, the attractive market price and continuous demand for the product in nature. Therefore, this study was to analyze the correlations and spatial variability of the productivity of the culture of the green corn in winter, in alluvial soil of the type Cambisols eutrophic in the amount areas and Hydromorphi... W.J. Souza, S.H. Benez, P.H. Nakazawa, A.J. Santana neto, L.C. Citon, V.S. Akune

644. Spatial Variability of Canopy Volume in a Commercial Citrus Grove

LiDAR (light detection and ranging) sensors have shown good potential to estimate canopy volume and guide variable rate applications in different fruit crops. Oranges are a major crop in Brazil; however the spatial variability of geometrical parameters remains still unknown in large commercial groves, as well as the potential benefit of sensor guided variable rate applications. Thus, the objective of this work was to characterize the spatial variability of the canopy volume in a commercial or... A.F. Colaço, J.P. Molin, R.G. Trevisan, J.R. Rosell-polo, A. Escolà

645. Spatial Variability of Soil Nutrients and Precision Nutrient Management for Targeted Yield Levels of Groundnut (Arachis Hypogaea L.)

A field study was conducted during rabi / summer 2014-15 to know the spatial variability and precision nutrient management practices on targeted yield levels of groundnut. The experimental field has been delineated into 36 grids of 9 m x 9 m using geospatial technology. Soil samples from 0-15 cm were collected and analysed. Spatial variability exists for available nitrogen, phosphorous and potassium and they varied from 99 to 197 kg N, 12.1 to 64.0 kg P2O5 and 1... H. D.c, S. Dr., N. Dr., M. Giriyappa, S. T

646. Spatial Variability of Soil Nutrients and Site Specific Nutrient Management in Maize

A field study was conducted during kharif 2014 and rabi 2014-15 at Southern Transition Zone of Karnataka under the jurisdiction of University of Agricultural Sciences, GKVK, Bangalore, India to know the spatial variability for available nutrient content in cultivator’s field and effect of site specific nutrient management in maize. The farmer’s fields have been delineated with each grid size of 50 m x 50 m using geospatial technology. Soil samples from 0-15 cm we... S. T, M. Giriyappa, D. Hanumanthappa, N. Dr., S. K, S. Yogananda, A. Kiran

647. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)

Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson

648. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)

Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson

649. Spatially Explicit Prediction of Soil Nutrients and Characteristics in Corn Fields Using Soil Electrical Conductivity Data and Terrain Attributes

Site specific nutrient management (SSNM) in corn production environments can increase nutrient use efficiency and reduce gaseous and leaching losses. To implement SSNM plans, farmers need methods to monitor and map the spatial and temporal trends of soil nutrients. High resolution electrical conductivity (EC) mapping is becoming more available and affordable. The hypothesis for this study is that EC of the soil, in conjunction with detailed terrain attributes, can be used to map soil nutrient... S. Sela, N. Graff, K. Mizuta, Y. Miao

650. Spatio-temporal Analysis of Soil Moisture and Turfgrass Health to Investigate the Temporal Stability of Variable Rate Irrigation Zones

The western USA has been experiencing severe drought conditions for at least the last 20 years. The population in many areas of the west, like Utah, has also increased greatly in this time putting greater strain on the limited freshwater supply. While agriculture is generally the sector consuming the largest proportion of freshwater, conversion of agricultural land to urban areas with lawns, parks and playing fields may result in some reduction of water use, but the EPA have estimated that as... R. Kerry, K. Sanders, A. Swenson, A. Henrie, N. Hansen, B. Hopkins, B. Ingram

651. Spatio-temporal Variability of Intra-field Productivity Using Remote Sensing

Understanding the spatiotemporal variability in intra-farm productivity is crucial for management in making agronomic decisions. Furthermore, these decision-making processes can be enhanced using spatial data science and remote sensing. This study aims to develop a framework to asses the spatio-temporal variability of intra-farm productivity through historical satellite data and climate data. Historical satellite data and rainfall information from diverse fields across the United States (2016... E. Van versendaal, C. Hernandez, P. Kyveryga, I. Ciampitti

652. Spectral Imaging Deep Learning Mapper for Precision Agriculture

With the growing variety of RGB cameras, spectral sensors, and platforms like field robots or unmanned aerial vehicles (UAV) in precision agriculture, there is a demand for straightforward utilization of collected field data. In recent years, deep learning has gained significant attention and delivered impressive results in the realm of computer vision tasks, such as semantic segmentation. These models have also found extensive applications in research related to precision agriculture and spe... L. Thomas, B. Jakimow, A. Janz, P. Hostert, A. Lajunen

653. Spectral Response of Six Treatments of Soil Fertilization in Potato (Solanum tuberosum L.) Var. Diacol Capiro with UAS

In Colombia, potato cultivation occupies the third place among the transient crops in the country, covering approximately 160,000 hectares. It holds the first place in terms of production value, reaching US $500 million, and ranks as the second crop with the highest demand for fertilizers, constituting 20% of production costs. The departments of Cundinamarca, Boyacá, Nariño, and Antioquia are the primary potato producers, accounting for 87.8% of the total production. Traditional... S.A. Rubaino sosa, O.Y. Cristancho rojas, W.A. Leon rueda, O.G. Montero pinilla, J.C. Roa bello, I.A. Lizarazo salcedo

654. Spectral Vegetation Indices to Quantify In-field Soil Moisture Variability

Agriculture is the largest consumer of water globally. As pressure on available water resources increases, the need to exploit technology in order to produce more food with less water becomes crucial. The technological hardware requisite for precise water delivery methods such as variable rate irrigation is commercially available. Despite that, techniques to formulate a timely, accurate prescription for those systems are inadequate. Spectral vegetation indices, especially Normalized Differenc... J. Siegfried, R. Khosla, L. Longchamps

655. Spotweeds: a Multiclass UASs Acquired Weed Image Dataset to Facilitate Site-specific Aerial Spraying Application Using Deep Learning

Unmanned aerial systems (UASs)-based spot spraying application is considered a boon in Precision Agriculture (PA). Because of spot spraying, the amount of herbicide usage has reduced significantly resulting in less water contamination or crop plant injury. In the last demi-decade, Deep Learning (DL) has displayed tremendous potential to accomplish the task of identifying weeds for spot spraying application. Also, most of the ground-based weed management technologies have relied on DL techniqu... N. Rai, Y. Zhang, J. Quanbeck, A. Christensen, X. Sun

656. Spray Deposition and Efficacy of Pesticide Applications with Spray Drones in Row Crops in the Southeastern US

The use of spray drones for pesticide applications is expanding rapidly in agriculture, with one of the top uses currently being in the row crop production. Several research studies were undertaken in 2022 and 2023 to measure and assess spray deposition and efficacy of pesticides applied with spray drones in the major row crops (corn, cotton and peanuts) grown in the southeastern US. These studies also evaluated and compared the deposition and pesticide efficacy of spray drones with tradition... C. Byers, R. Meena, J. Kichler, R.C. Kemerait, L. Hand, S. Virk

657. Spray Deposition Characterization of Uniform and Variable-rate Applications with Spray Drones

The use of unmanned aerial application systems (also known as spray drones) has seen rapidly increasing interest in recent years due to their potential to allow for timely application of pesticides and being able to apply in areas inaccessible to ground application sprayers. Newer spray drone models’ have improved application systems such as rotary atomizers for creating spray droplets and capabilities such as variable-rate (VR) application for site-specific pesticide applications. An i... C. Byers, S. Virk, R.K. Meena, G. Rains

658. Springer

Springer is a leading global scientific, technical and medical publisher, providing researchers in academia, scientific institutions and corporate R&D departments with quality content via innovative information products and services. Springer is part of Springer Nature, one of the world’s leading global research, educational and professional publishers. ...

659. Stakeholder Inclusion for Responsible Robotics: Who, How, and Why?

... D. Rose

660. Standards for Data-driven Agrifood Systems, One Year After the ISO Strategic Advisory Group for Smart Farming

The lack of data interoperability is a major obstacle for the data-driven, principled multi-objective decision-making required for modern agrifood systems to help meet the UN Sustainable Development Goals. Aware of this, the International Organization for Standardization (ISO) chartered a Strategic Advisory Group for Smart Farming (SAG-SF) to survey the existing standardization landscape of the domain within ISO, to identify gaps where additional standardization is needed, and to provide a st... R. Ferreyra, J. Lehmann, J.A. Wilson

661. Static and In-field Validation of Application Accuracy of Commercial Spray Drones at Varying Rates and Speeds

The emerging application of spray drones in agriculture for pesticide delivery has seen significant interest recently. Currently, various spray drone platforms with advanced capabilities such as variable-rate application and edge-spraying are commercially available; however, limited research and information is available regarding the application accuracy of these systems. Pesticide applications with spray drones in several research studies conducted at the University of Georgia in 2023 indica... S. Virk, R.K. Meena, C. Byers

662. Static and Kinematic Tests for Determining Spreaders Effective Width

Spinner box spreaders are intensively used in Brazil for variable rate applications of lime in agriculture. The control of that operation is a challenging issue because of the complexity involved on the interactions between product and machine. Quantification of transverse distribution of solids thrown from the spinner box spreaders involves dynamic conditions tests where the material deposited on trays is evaluated along the pass of the machinery. There is a need of alternative testing metho... L. Maldaner, T. Canata, J. Molin, B. Passalaqua, J.J. Quirós

663. Statistical Variability of Crop Yield, Soil Test N and P Within and Between Producer’s Fields

Soil test N and P significantly affect crop production in the Canadian Prairies, but vary considerably within and between producer's fields.  This study describes the variability of crop yield, soil test N and P within and between producer's fields in the context of variable fertilizer rates.  Yield, terrain attribute, soil test N and P data were collected for 10 fields in Alberta, Saskatchewan and Manitoba Canada in 2014 and 2015.  The influence of ... A. Moulin, M. Khakbazan

664. Steering Strategy Selection of a Robotic Platform for Bin Management in Orchard Environment

For a robotic bin-managing system working in an orchard environment, especially in modern narrow row spaced orchards in the Pacific Northwest (PNW) region of the U.S., path planning is an essential function to achieve highly efficient bin management. Unlike path planning for a car-like vehicle in an open field, path planning for a four-wheel-independent-steered (4WIS) robotic bin-managing platform in orchard environment is much more challenging due to the very confined working space between t... Y. Ye, L. He, Q. Zhang

665. Stem Characteristics and Local Environmental Variables for Assessment of Alfalfa Winter Survival

Alfalfa (Medicago sativa L.) is considered the queen of forage due to its high yield, nutritional qualities, and capacity to sequester carbon. However, there are issues with its relatively low persistency and winter survival as compared to grass. Winter survival in alfalfa is affected by diverse factors, including the environment (e.g., snow cover, hardiness period, etc.) and management (e.g., cutting timing, manure application, etc.). Alfalfa's poor winter survival reduces the number of ... M. Saifuzzaman, V. Adamchuk, M. Leduc

666. Strawberry Pest Detection Using Deep Learning and Automatic Imaging System

Strawberry growers need to monitor pests to determine the options for pest management to reduce damage to yield and quality.  However, manually counting strawberry pests using a hand lens is time-consuming and biased by the observer. Therefore, an automated rapid pest scouting method in the strawberry field can save time and improve counting consistency. This study utilized six cameras to take images of the strawberry leaf. Due to the relatively small size of the strawberry pest, six cam... C. Zhou, W. Lee, A. Pourreza, J.K. Schueller, O.E. Liburd, Y. Ampatzidis, G. Zuniga-ramirez

667. Studies on Soil Spatial Variability and Its Impact on Cane Yield Under Precision Nutrient Management System

In present investigation an attempt was made to quantify the soil variability of 30 grids of 10 m x 10 m dimension at research farm of Nandi Sahakari Sakkare Karkhane (NSSK), Krishna Nagar, District. Bijapur. Each grid (10 m x 10 m) showed variation with available nitrogen as low as 140 kg ha-1 to as high as 245 kg/ha with a range of 105 kg/ha, phosphorus as low as 53 kg P2O5 ha-1 and as high as 89.3 kg P2O5 ha-1 wit... M. Kumar r, M. Kumar r, D. Nadagouda

668. Sugarcane Yield Mapping Using an On-board Volumetric Sensor

Few alternatives are available to the sugarcane sector for monitoring crop productivity. However, in recent years, research has been dedicated to developing methods ranging from estimation based on engine parameters to using sensors and artificial intelligence. This study aims to present a new tool for monitoring productivity applied to sugarcane cultivation, which utilizes a volumetric optical sensor, in contrast to other methods already used for this measurement, and is recently being intro... G. Balboa, J.C. Masnello, F. De oliveira moreira, R. Canal filho, E.R. Da silva, J.P. Molin

669. Suitability of ML Algorithms to Predict Wild Blueberry Harvesting Losses

The production of wild blueberries (Vaccinium angustifolium.) is contributing 112.2 million dollars to the Canada’s revenue which can be further increased through controlling harvest losses. A precise prediction of blueberry harvesting losses is necessary to mitigate such losses. In this study, the performance of three machine learning (ML) models was evaluated to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada we... H. Khan, T. Esau, A. Farooque, F. Abbas

670. Summary of Forty Years of Grid Sampling Research

Between the years of 1961 and 2001, two 12.5-ha fields in Illinois were sampled for soil pH, and available P and K in a 24.3-m grid. One field was sampled beginning in 1961 while the other field was sampled from 1982. At each sampling, the samples were obtained in the same grid. This resulted in the ability not only to compare grid sample density to delineate fertility patterns within the fields, but also to determine the rate of soil test change with P and K applications, the change in ferti... D.W. Franzen

671. Sun Effect on the Estimation of Wheat Ear Density by Deep Learning

Ear density is one of the yield components of wheat and therefore a variable of high agronomic interest. Its traditional measurement necessitates laborious human observations in the field or destructive sampling. In the recent years, deep learning based on RGB images has been identified as a low-cost, robust and high-throughput alternative to measure this variable. However, most of the studies were limited to the computer challenge of counting the ears in the images, without aiming to convert... S. Dandrifosse, E. Ennadifi, A. Carlier, B. Gosselin, B. Dumont, B. Mercatoris

672. Supervised Feature Selection and Clustering for Equine Activity Recognition

In this paper we introduce a novel supervised algorithm for equine activity recognition based on accelerometer data. By combining an approach of calculating a wide variety of time-series features with a supervised feature significance test we can obtain the best suited features using just 5 labeled samples per class and without requiring any expert domain knowledge. By using a simple cluster assignment algorithm with these obtained features, we get a classification algorithm that achieves a m... T. De waele, D. Peralta, A. Shahid, E. De poorter

673. Supervised Hyperspectral Band Selection Using Texture Features for Classification of Citrus Leaf Diseases with YOLOv8

Citrus greening disease (HLB), a disease caused by bacteria of the Candidatus Liberibacter group, is characterized by blotchy leaves and smaller fruits. Causing both premature fruit drop and eventual tree death, HLB is a novel and significant threat to the Florida citrus industry.  Citrus canker is another serious disease caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri) and causes economic losses for growers from fruit drops and blemishes. Citrus cank... Q. Frederick, T. Burks, P.K. Yadav, M. Dewdney, J. Qin, M. Kim

674. SurePoint Ag Systems - Sponsor Presentation

... B. Downing

675. Surplus Science and a Non-linear Model for the Development of Precision Agriculture Technology

The advent of ‘big data technologies’ such as hyperspectral imaging means that Precision Agriculture (PA) developers now have access to superabundant and highly  heterogeneous data.  The authors explore the limitations of the classic science model in this situation and propose a new non-linear process that is not based on the premise of controlled data scarcity. The study followed a science team tasked with developing highly advanced hyperspectral techniques for a &lsquo... M.Z. Cushnahan, I.J. Yule, B.A. Wood, R. Wilson

676. Survey of Pesticide Application Practices and Technologies in Georgia Agricultural Crops

Georgia is a leading producer of numerous crops including cotton, peanut, blueberries, pecans, bell peppers, cabbage, watermelons, and peaches in the United States. Pesticide applications are critical for the successful production of these crops. Pesticide regulations and application technologies are changing rapidly due to growing concerns around off-target movement and increased focus on improving the efficiency and efficacy of pesticide applications. In order to provide suitable ... S.S. Virk, E.P. Prostko

677. Survey Shows Specialty and Commodity Crop Retailers Use Precision Agriculture Differently

The 2021 CropLife-Purdue Survey of precision agricultural practices by US agricultural input dealers serving the American grain and oilseed sector shows that most of them use GPS guidance and related technologies like sprayer boom control, most provide variable rate fertilizer services, and the majority say that fertilizer decisions are influenced by grower data. In contrast, dealers serving horticultural and specialty crop farms indicate comparatively modest adoption of many precision agricu... B.J. Erickson, J. Lowenberg-deboer

678. Swarm Farming is the Future

... C. Rupp

679. Symposium Welcome and Introductions

... J. Lowenberg-deboer

680. Synchronized Windrow Intelligent Perception System (SWIPE)

The practice of bale production, in forage agriculture, involves various machines that include tractors, tedders, rakers, and balers. As part of the baling process, silage material is placed in windrows, linearly raked mounds, to drive over with a baler for easy collection into bales. Traditionally, a baler is an implement that is attached on the back of a tractor to generate bales of a specific shape. Forage agricultural equipment manufacturers have recently released an operator driven, self... E.M. Dupont, P.R. Kolar

681. System Development for Application and Testing of Spray-on Biodegradable Mulch

Plastic mulch films have long been a staple in agriculture and plays a critical part in the specialty crop production. Plastic mulch provides benefits such as conserving soil moisture, suppress weed growth and increase soil temperature. However, the widespread use of petroleum based plastic mulch films have raised concerns due to challenges associated with their removal and environmental impact. Plastic mulch has to be removed after every growing season. During the removal process, microplast... N.K. Piya, A. Sharda, D. Flippo

682. Teaching Critical Thinking Skills Using Geospatial Technology As Instructional Tools

Techniques in data collection and analysis of data are important concepts for students of precision farming. Also needed in conjunction with these concepts are critical thinking and problem solving skills. Employers often list critical thinking skills as one of the most important characteristics for new employees. Helping students experience and acquire critical thinking skills can be difficult. Geospatial technologies are not only useful precision farming tools, they are also educational too... T.A. Brase

683. Teaching Mathematics Towards Precision Agriculture Through Data Analysis and Models

Precision agriculture is used in a wide variety of field operations and agricultural practices that affect our daily lives. Many fields of agriculture are increasingly adopting equipment automation, robotics, and machine learning techniques. These all lead to recognize that data collection and exploitation is a valuable tool assisting in real-time farming and livestock decisions. Thus, the immediate need to empower students in Agriculture Sciences with mathematical tools using data analysis i... R. Sviercoski

684. Technological Improvement on Sugar Cane Yield Monitor

This paper presents the technological improvement on sugar cane yield monitor. The system designed employs load cells as an instrument for weighing billets, set up on the side conveyor of the harvester before the sugar cane billets are dropped into a field transport wagon. This data, along with the information gathered by GPS installed on the harvester, enabled the elaboration of a digital yield map using GIS. In order to improve the yield monitor a re-design of the first prototype was accomp... D.G. Cerri, G.R. Gray, P.S. Magalhães

685. Technology Support for Game Monitoring As a Tool for Damages Reduction of Field Crops

Wild boars (Sus scrofa) are increasingly becoming the main cause of field crops damage in Czech Republic and central Europe area. There are many reasons why wild boars population is growing. The major reason is most likely change in the composition of field crops. In some areas in particular there is focus on oilseed rape and maize, for which there are also recorded the biggest losses. One of the key discussion topics is the issue of estimation of animal quantities and its traceabil... J. Jarolimek, M. Stočes, M. Ulman, J. Vaněk

686. TEG Automation Solutions - Sponsor Presentation

... V. Oliveira

687. Temperature Effect on Wild Blueberry Fruit Quality During Mechanical Harvest

Mechanical harvesters, utilizing a range of technologies, have been developed for timely operations and remain the most cost-effective means of picking the wild blueberry crop. Approximately 95% of wild blueberries in Atlantic Canada are immediately frozen and processed, while only a small percentage is sold in the fresh market. However, the producers can benefit by increasing the value of their harvested crop through fresh market sales. The objective of this study was to determine the optimu... T.J. Esau, A.A. Farooque, F. Abbas

688. Terrain Modeling to Improve Soil Survey in North Dakota

Users of site-specific technologies would prefer to use digitized soil survey boundaries to help in delineating management zones for nutrient application. However, the present scale of soil type does not allow meaningful zone delineation. A project was conducted to use terrain modeling and other site- specific tools to delineate smaller-scale soil type boundaries that would be more useful for directing within-field nutrient management. Topography, soil EC, yield mapping and satellite imagery ... D.W. Franzen, J.L. Boettinger

689. The Agriculture Operations Center: the Answer to “What If...”

Can’t farming be simpler?  Yes…with an Agriculture Operations Center -- we call it the AGOC, and it’s the next big step for precision agriculture.  Leveraging decades of lessons from the US Air Force, the AGOC provides the ability to schedule, execute, collect, consolidate, and distribute all the support a farmer needs from satellites, piloted aircraft, unmanned aircraft, sensing, modeling, and analysis…all packaged into “one stop shopping.”&nbs... M. Zamzow

690. The Daily Erosion Project - High Resolution, Daily Estimates of Runoff, Detachment, Erosion, and Soil Moisture

Runoff and sediment transport from agricultural uplands are substantial threats to water quality and sustained crop production. Farmers, conservationists, and policy makers must understand how landforms, soil types, farming practices, and rainfall affect soil erosion and runoff in order to improve management of soil and water resources. A system was designed and implemented a decade ago to inventory precipitation, runoff, and soil erosion across the state of Iowa, United States. That system u... B.K. Gelder, R. Cruse, D. James, D. Herzmann, C. Sandoval-green, T. Sklenar

691. The Device of Air-assisted Side Deep Precision Fertilization for Rice Transplanter

Rice is the most important crop in China, which has the largest plant area. Fertilization is an important process of rice production, which directly affects the yield of crops, reasonable and effective use of chemical fertilizer can improve the yield of crops. At present, the mechanization level of rice fertilization is very low in China, and the artificial fertilization requires a large amount of fertilizer which caused the uneven distribution. The rice side deep fertilizing is an ideal way ... C. Zhao, G. Wu, Z. Meng, W. Fu, L. Li, X. Wei

692. The Effect of Slope Gradient on the Modelling of Soil Carbon Dioxide Emissions in Different Tillage Systems at a Farm Using Precision Tillage Technology in Hungary

Understanding the role of natural drivers in greenhouse gas (GHG) emitted by agricultural soils is crucial because it contributes to selecting and adapting acceptable eco-friendly farming practices. Hence, Syngenta Ltd. collaborating with researchers, aimed to investigate the effect of two tillage treatments, conventional-tillage (CT) and minimum-tillage (MT) on soil carbon dioxide (CO2) emissions. The research field is in Hungary. Soil columns were derived from different tillage s... I.M. Kulmany, S. Benke, L. Bede, R. Pecze, V. Vona

693. The Evaluation of NDVI Response Index Consistency Using Proximal Sensors, UAV and Satellites

The Response Index NDVI (RINDVI) is described as the response of crops to additional nitrogen (N) fertilizer. It is calculated by dividing the NDVI of the high-N plot (N-rich strip) by the NDVI of the zero-N plot or farmer's practice where less pre-plant N was applied (Arnall and al., 2016). RI values are used to predict yield and monitor top dress N fertilization. Many research has been carried out to d... S. Phillips, B. Arnall, M. Maatougui

694. The Evaluation of Spatial Response to Potassium in Soybeans

In agriculture, the nutrients that are in the largest demand are nitrogen (N), phosphorus (P), and potassium (K), as product demand increases  so does demand for fertilizers. In the case of potassium, most soils can provide potassium in amounts that exceed crop demand; however the potassium within the soil is not always readily available to the crop, this leads to producers apply potassium to their crops even though soil tests suggests otherwise. One such crop where potassium is in deman... S. Akin, B. Arnall

695. The Impact of Row Unit Position on Planter Toolbar on Corn Crop Development: an Experimental Study

Precision planting techniques are essential to grow corn successfully. Monitoring planter speed, row-unit bounce, and gauge-wheel load ensures high-quality seeding. Vertical vibration during planting can impede seed metering and delivery, causing planting variability. Row unit vibration increases with planting speed and can lead to spatial variability in planting. Therefore, the goals of this study were to 1) understand the influence of row unit location on its vertical vibration; and 2) comp... J. Peiretti, A. Sharda, S. Badua

696. The ISO Strategic Advisory Group for Smart Farming: a Multi-pronged Opportunity for Greater Global Interoperability

Agriculture is becoming increasingly complex and producers must secure their profitability, sustainability, and freedom to operate under a progressively more challenging set of constraints such as climate change, regulatory pressure, changes in consumer preferences, increasing cost of inputs, and commodity price volatility. We have not, however, yet reached the level of data interoperability required for a truly "smart" farming that can tackle the aforementioned probl... R. Ferreyra, J. Lehmann

697. The Methods and Applications of Artificial Intelligence Used in the Technologies of Precision Agriculture

The methods and applications of artificial intelligence more and more are linking with technologies of precision agriculture. The classical and modern approaches to artificial intelligence used for problem solving in the technologies of precision agriculture. Searching methods include uninformed and informed search methods which is better way to achieve optimality. Expert systems are typical classical approaches to artificial intelligence and they can be applied for problem solutions. Decisio... A. Gailums

698. The New Digital Soil Map of Sweden -Derived for Free Use in Precision Agriculture

The Digital Soil Map of Sweden (DSMS) was finalized in 2015. The present paper describes the mapping strategy, the estimated uncertainty of the primary map layers and its potential use in precision agriculture. The DSMS is a geodatabase with information on the topsoil of the arable land in Sweden. The spatial resolution is 50 m × 50 m and it covers > 90% of the arable land of the country (~2.5 million ha). Non-agriculture land and areas with organic soil are excluded. Access to a num... K. Piikki, M. Söderström

699. The Ohio State University - Sponsor Presentation

... J.P. Fulton

700. The Relationship Between Vegetation Indices Derived from UAV Imagery and Maturity Class in Potato Breeding Trials

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. Unmanned aerial vehicles (UAVs) equipped with sensors can acquire images with high spatial and temporal resolution. The objectives of this study were to 1) establish the relationship between vegetation indices (VIs) derived from UAV imagery at ... S.M. Samborski, U. Torres, R. Leszczyńska, A. Bech, M. Bagavathiannan

701. The Review of Studying and Using Advanced Technologies for Site Specific Management in Konya, Turkey

Using advanced (information) technologies in agriculture is increasing rapidly especially in the developed countries such as USA, Japan, and some members of EU. Advanced technologies in agriculture are mostly based on sensors. Site specific management is a form of agricultural management, which is governed by optimum use of variables. Input such as chemical, water, and seed in agricultural production can be managed by using the technologies. Geographic information systems (GIS), Global Positi... K. Pecker, F.M. Botsali, A. Topal, M. Zengin

702. The Role of Imaging Spectroscopy in Monitoring Soil Quality for Precision Agriculture

Imaging Spectroscopy (IS) is a key application in precision agriculture, offering insights into soil quality spatiotemporal variability. This technology's integration into soil quality mapping enables farmers and agricultural managers to make decisions that elevate efficiency, productivity, and sustainability within farming operations. With ongoing advancements in remote sensing technology, the role of IS in precision agriculture is poised for further expansion, promising enhanced benefit... T. Paz kagan

703. The Use of Spatial and Temporal Measures to Enhance the Sensitivity of Satellite-based Spectral Vegetation Indices to (Water) Stress in Maize Fields

Climate change and water scarcity are reducing the available irrigation water for agriculture thus turning it into a limited resource. Today calculating and estimating crop water requirements are achieved through the ETc FAO-56 model where the effect of climate on crop water requirement is determined through the water evaporation from the soil and plant (ETref), and a calendar crop coefficient (Kc). Models t... Y. Goldwasser, V. Alchanati, E. Goldshtein, Y. Cohen, A. Gips, I. Nadav

704. Thermal Characterization and Spatial Analysis of Water Stress in Cotton (Gossypium Hirsutum L.) and Phytochemical Composition Related to Water Stress in Soybean (Glycine Max)

Studies were designed to explore spatial relationships of water and/or heat stress in cotton and soybeans and to assess factors that may influence yield potential. Investigations focused on detecting the onset of water/heat stress in row crops using thermal and multispectral imagery with ancillary physicochemical data such as soil moisture status and photosynthetic pigment concentrations. One cotton field with gradations in soil texture showed distinct patterns in thermal imagery, matching pa... S.J. Thomson, S.L. Defauw, P.J. English, J.E. Hanks, D.K. Fisher, P.N. Foster, P.V. Zimba

705. Time Series Analysis of Vegetation Dynamics and Burn Scar Mapping at Smoky Hill Air National Guard Range, Kansas Using Moderate Resolution Satellite Imagery

Military installments are import assets for the proper training of armed forces. To ensure the continued viability of the training grounds, management practices need to be implemented to sustain the necessary environmental conditions for safe and effective training. This analysis uses satellite imagery over time to gain insight into vegetation conditions over a large military installment. MODIS imagery was collected multiple times a year for 11 years at Smoky Hill Air National Guard Range (Sm... E. Williams

706. Time Series Study of Soybean Response Based on Adjusted Green Red Index

Four time-lapse cameras, Bushnell Nature View HD Camera (Bushnell, Overland Park, KS) were installed in a soybean field to track the response of soybean plants to solar radiation, air temperature, relative humidity, soil surface temperature, and soil temperature at 5-cm depth. The purpose was to confirm if visible spectroscopy can provide useful data for tracking the condition of crops and, if so, whether game and trail time-lapse cameras can serve as reliable crop sensing and monitoring devi... P.A. Larbi, S. Green

707. Toward Geopolitical-Context-Enabled Interoperability in Precision Agriculture: AgGateway's SPADE, PAIL, WAVE, CART and ADAPT

AgGateway is a nonprofit consortium of 240+ businesses working to promote, enable and expand eAgriculture. It provides a non-competitive collaborative environment, transparent funding and governance models, and anti-trust and intellectual property policies that guide and protect members’ contributions and implementations. AgGateway primarily focuses on implementing existing standards and collaborating with other organizations to extend them when necessary. In 2010 AgGateway id... R. Ferreyra, D.B. Applegate, A.W. Berger, D.T. Berne, B.E. Craker, D.G. Daggett, A. Gowler, R.J. Bullock, S.C. Haringx, C. Hillyer, T. Howatt, B.K. Nef, S.T. Rhea, J.M. Russo, S.T. Nieman, P. Sanders, J.A. Wilson, J.W. Wilson, J.W. Tevis, M.W. Stelford, T.W. Shearouse, E.D. Schultz, L. Reddy

708. Toward Smart Soybean Variety Selection Using UAV-based Imagery and Machine Learning

The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield and resilience to stress, achieved in one year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials due to their large population, complex genetic behavior, and high genotype-en... J. Zhou, J. Zhou

709. Towards a Digital Peanut Profile Board: a Deep Learning Approach

Artificial intelligence techniques, particularly deep learning, offer promising avenues for revolutionizing object detection and counting algorithms in the context of digital agriculture. The challenges faced by peanut farmers, particularly the precise determination of optimal maturity for digging, have prompted innovative solutions. Traditionally, peanut maturity assessment has relied on the Peanut Maturity Index (PMI), employing a manual classification process with the aid of a peanut profi... M.F. Freire de oliveira, B.V. Ortiz, J.B. Souza, Y. Bao, E. Hanyabui

710. Towards Calibrated Vegetation Indices from UAS-derived Orthomosaics

Crop advisors and farmers increasingly use drone data as part of their decision making. However, the vast majority of UAS-based vegetation mapping services support only the calculation of a relative NDVI derived from compressed JPEG pixel values and do not include the possibility to include more complex aspects like soil correction. In our ICPA12 contribution, we demonstrated the effects and consequences of the above shortcomings. Here, we present the stepwise development of a solution to ens... K. Pauly

711. Towards Data-intensive, More Sustainable Farming: Advances in Predicting Crop Growth and Use of Variable Rate Technology in Arable Crops in the Netherlands

Precision farming (PF) will contribute to more sustainable agriculture and the global challenge of producing ‘More with less’. It is based on the farm management concept of observing, measuring and responding to inter- and intra-field variability in crops. Computers enabled the use of Farm Management Information Systems (FMIS) and farm and field specific Decision Support Systems (DSS) since mid-1980s. GIS and GNSS allowed since ca. 2000 geo-referencing of data and controlled traff... C. Kempenaar, F. Van evert, T. Been, C. Kocks, K. Westerdijk, S. Nysten

712. Towards Precision Microbiology

In the recent years, the use of organic matter (OM) and microorganisms is increasing beyond organic agriculture, into conventional horticultural systems, in order to achieve high yields and quality through a more sustainable soil management. Thus, Integrated Nutrient Management (INM), that includes the use of diagnostic tools, high quality OM, microbial inoculants, highly-efficient fertilizer, and site-specific management in gaining space in intensive production systems. Precision m... V. Gutiérrez, R. Ortega

713. Transforming Precision Agriculture Education, Research and Outreach in Sub-saharan Africa Through Intra-africa Cooperation

Productivity and profitability of sub-Saharan (SSA) agriculture can be enhanced greatly through the adoption of precision agriculture technologies and tools. However, until 2020 when the African Plant Nutrition Institute (APNI) established the African Association for Precision Agriculture (AAPA), most SSA PA enthusiast worked in isolation.  The AAPA was formed to innovate Africa’s agricultural industry by connecting PA science to its practice and disseminate PA tailored to the need... K.A. Frimpong, S. Phillips, V. Aduramigba-modupe, N. Fassinou hotegni, M. Mechri, M. Mishamo, J.M. Sogbedji, Z. hazzoumi, R. Chikowo, M. Fodjo kamdem

714. Transforming Row Crop Agriculture: Harnessing Computer Vision and AI for Automation and Autonomy

... A. Sharda

715. Translating Data into Knowledge - Precision Agriculture Database in a Sugarcane Production.

The advent of Information Technology in agriculture, surveying and data collection became a simple task, starting the era of "Big Data" in agricultural production. Currently, a large volume of data and information associated with the plant, soil and climate are collected quick and easily. These factors influence productivity, operating costs, investments and environment impacts. However, a major challenge for this area is the transformation of data and in... G.M. Sanches, O.T. Kolln, H.C. Franco, P.S. Magalhaes, D.G. Duft

716. Treetop Tech: Uplifting German Foresters' Drone Perspectives Through the Technology Acceptance Model

Forests play a key role in nature as they purify water, stabilize soil, cycle nutrients, store carbon and also provide habitats for wildlife. Economically, forest product industries provide jobs and economic wealth. Sustainable forest management and planning requires foresters’ understanding of the forests dynamics for which the collection of field data is necessary, which can be time consuming and expensive. Unmanned aerial vehicles or drones can improve the efficiency of tradition acq... M. Michels, H. Wever, O. Mußhoff

717. Trends in Agricultural Technology Advancements: Insights from US Patent Analysis

Meeting the demand for food, fiber, and fuel production while addressing environmental concerns and enhancing societal benefits underscores the need to transition to conservation approaches and sustainable intensification pathways in current agricultural cropping systems. Technological advances in agriculture offer promising opportunities to facilitate this transition. Following this rationale, this study aims to analyze prevailing trends in agricultural technology advancements. Active patent... P.B. Cano, A. Carcedo, F. Gomez, C. Hernandez, V. Gimenez, I. Ciampitti

718. UAV Multispectral Data As a Suitable Tool for Predicting Sweetness, Size, and Yield of Vidalia Onions

Vidalia onions is a specialty crop cultivated solely within the southeastern region of Georgia. The key distinguishing characteristic of Vidalia onions is its high sugar content, making them highly prized and widely consumed. Ten thousand acres are grown with Vidalia Onions each year approximately, and the market value (~$150Mi/year) makes the crop very important for the State of Georgia. Traditionally, the planting, weeding, spraying, harvesting, and post-harvesting operations are usually do... M. Barbosa, L. Oliveira, C. Tyson, A. Shirley, R. Santos, L. Sales, R. Vargas

719. UAV-based Crop Scouting for Precision Nutrient Management

Precision agriculture – is one of the most substantial markets for the Unmanned Aerial Vehicles (UAVs). Mounted on the UAVs, sensors and cameras enable rapid screening of large numbers of experimental plots to identify crop growth habits that contribute to final yield and quality in a variety of environments. Wheat is one of the Idaho’s most important cereal crops grown in 42 of 44 Idaho counties. We are working on establishing a UAV-based methodology for in-season prediction of w... O.S. Walsh, K. Belmont, J. Mcclintick-chess, J. Marshall, C. Jackson, C. Thompson, K. Swoboda

720. UAV-based Hyperspectral Monitoring of Peach Trees As Affected by Silicon Applications and Water Stress Status

Previous research has shown that the application of reduced doses of Silicon (Si) improves crop tolerance to water stress, which is common in commercial young peach trees because irrigation is not usually applied during their first two years. In this study, aerial images were used to monitor the impact of different Si and water treatments on the hyperspectral response of peach trees. An experiment with 60 young (under 1 year old) peach trees located at the Musser Fruit Research Center (Seneca... J. Peña, J. Melgar, A. De castro, J. Maja, K. Nascimento-silva

721. UAV-based Phenotyping of Nitrogen Responses in Winter Wheat: Grain Yield and Nitrogen Use Efficiency

In the face of escalating global demand for wheat, influenced by burgeoning populations and changing consumption patterns, a profound understanding of determinants like precision nutrient management becomes indispensable. In an on-farm experiment conducted at the Dürnast Research Station in southern Bavaria from 2022 to 2023, we investigated the effects of nitrogen (N) treatments on 18 European winter wheat (Triticum aestivum) cultivars. The field trial design encompassed three dist... J. Zhang, K. Yu

722. Understanding Complex Soil Variability: the Application of Archaeological Knowledge to Precision Agriculture Systems in the UK.

As higher resolution datasets have become more available and more accessible within commercial agriculture, there has been an increasing expectation that more data will bring more answers to questions surrounding soil, crop and yield variability. When this does not happen, trust and confidence in data can be lost, affecting the uptake and use of precision agriculture. This research presents a novel approach for understanding complex soil variability at a variety of different scales.... H. Webber

723. University of Georgia's Institute for Integrative Precision Agriculture - Sponsor Presentation

... R.P. Ramasamy

724. University of Nebraska-Lincoln - Sponsor Presentation

... J.D. Luck

725. Use of a Cropping System Model for Soil-specific Optimization of Limited Water

In the arena of modern agriculture, system models capable of simulating the complex interactions of all the relevant processes in the soil-water-plant- atmosphere continuum are widely accepted as potential tools for decision support to optimize crop inputs of water to achieve location specific yield potential while minimizing environmental (soil and water resources) impacts. In a recent study, we calibrated, validated, and applied the CERES-Maize v4.0 model for simulating limited-water irriga... L.R. Ahuja, S.A. Saseendran, L. Ma, D.C. Nielsen, T.J. Trout, A.A. Andales, N.C. Hansen

726. Use of Crop and Drought Spectral Indices to Support Harvest Decisions of Peanut Fields in Alabama

Harvest efficiency expressed in quantity and quality of peanut fields could increase if farmers are provided with tools to support harvest decisions. Peanut farmers still rely on a visual and empiric method to assess the right time of peanut maturity but this method does not account for within-field variability of crop growth and maturity. The integration of spectral vegetation indices to assess drought, soil moisture, and crop growth to predict peanut maturity can help farmers strengthen dec... M.F. Oliveira, B.V. Ortiz, E. Hanyabui, J.B. Costa souza, A. Sanz-saez, S. Luns hatum de almeida , C. Pilcon, G. Vellidis

727. Use of Crop Canopy Reflectance Sensor in Management of Nitrogen Fertilization in Sugarcane in Brazil

Given the difficulty to determine N status in soil testing and lack of crop parameters to recommend N for sugarcane in Brazil raise the necessity of identify new methods to find crop requirement to improve the N use efficiency. Crop canopy sensor, such as those used to measure indirectly chlorophyll content as N status indicator, can be used to monitor crop nutritional demand. The objective of this experiment was to assess the nutritional status of the sugarcane fertilized with different nitr... S.G. Castro, G.M. Sanches, G.M. Cardoso, A.E. Silva, H.C. Franco, P.S. Magalhães

728. Use of MLP Neural Networks for Sucrose Yield Prediction in Sugarbeet

INTRODUCTION Sugar beet is one of the more technified agro industries in Spain. In the last years, it has leaded as well the digital transformation with the objective of maintaining sugar beet competitivity both national and internationally. Among other lines, very high potential has been identified in determining the sucrose content using a combination of Artificial Intelligence and Remote Sensing. This work presents the conclusions of an extensive data acquisition task, creation o... M. Cabrera dengra, C. Ferraz pueyo, V. Pajuelo madrigal, L. Moreno heras, G. Inunciaga leston, R. Fortes

729. Use of Precision Technologies to Conduct Successful Within-field, On-farm Trials

Performing randomized replicated trials in row crop field environments has the potential to increase crop production in environmentally sustainable ways.  Successful implementation requires an understanding of implement capabilities and sources of potential systematic error, including operator error.  Equipment capabilities can be thought of as a series of several critical “links in a chain,” each with implications that propagate downstream.   We will... M. Stelford, A. Krmenec

730. Use of Radar SAR Images to Assess Soil Moisture in Cane Crops: Practical Implications in Agricultural Operation

Sugar cane cultivation in the geographical region of the Cauca River Valley is a key industry for the local economy. However, this crop faces constant challenges related to the management of agricultural machinery for soil cultivation in conditions of high soil moisture. In this context, the synthetic aperture radar (SAR Radar) of the Sentinel 1 satellite emerges as a promising technology. The purpose of this work is to explore the use of the Sentinel 1 satellite SAR radar sensor in su... O.J. Munar-vivas, S. Anderson guerrero, D.F. Angrino chiran, J.F. Mateus-rodriguez

731. Use of Remotely Measured Potato Canopy Characteristics As Indirect Yield Estimators

Prediction of potato yield before harvest is important for making agronomic and marketing decisions. Active optical sensors (AOS) are rarely used together with other hand-held instruments for monitoring potato growth, including yield prediction. The aim of the research was to determine the relationship between manually and remotely measured potato crop characteristics throughout the growing season and yield in commercial potato fields. Objective was also to identify crop characteristics that ... S.M. Samborski, J. Szatylowicz, T. Gnatowski, R. Leszczyńska, M. Thornton, O. Walsh

732. Use of Satellite Data to Improve Damage Assessment Process for Agricultural Insurance Scheme in Indonesia

Goal is to develop new method utilizing satellite data for assessment of damage in paddy field which can contribute toward substantial reduction of the damage assessment time and costs in framework of agricultural insurance in Indonesia. For the damage assessment, estimation of yield in each paddy plot is a key, so the research on the estimation of rice yield was carried out using satellite data which was acquired in harvesting season. Multiple linear regression analysis was conducted for the... C. Hongo, C. Ogasawara, E. Tamura, G. Sigit

733. Use of the Active Sensor Optrx to Measure Canopy Changes to Evaluate Foliar Treatments and to Identify Soil Quality in Table Grape

Table Grape (Vitis vinifera L.) is the main exporting horticultural crop in Chile, with the country being one of the top exporters at the world level. Commonly, grape producers perform trials of different commercial products which are not evaluated in an objective way. On the other hand they do not have the tools to easily identify areas within the field that may have some limiting factor. The use of active ground sensors that pass under the canopy several times during the season ma... R.A. Ortega, M.M. Martinez, H.P. Poblete

734. Use of Unmanned Aerial Vehicles to Inform Herbicide Drift Analysis

A primary advantage of unmanned aerial vehicle-based imaging systems is responsiveness.  Herbicide drift events require prompt attention from a flexible collection system, making unmanned aerial vehicles a good option for drift analysis.  In April 2015, a drift event was documented on a Mississippi farm.  A combination of corn and rice fields exhibited symptomology consist with non-target injury from a tank mix of glyphosate and clethodim.  An interesting observation was t... J.M. Prince czarnecki, D.B. Reynolds, R.J. Moorhead

735. Use of Watering Hole Data As a Decision Support Tool for the Management of a Grazing Herd of Cattle

Establish grazing practices would improve the welfare of the animals, allowing them to express more natural behaviours. However, free-range reduces the ability to monitor the animals, thus increase the time needed to intervene in the event of a health problem. To ease the adoption of grazing, farmer would benefit from autonomously collected indicators at pasture that identify abnormal behaviours possibly related to a health problem in a bovine. These indicators must be individualised and coll... J. Plum, B. Quoitin, I. Dufrasne, S. Mahmoudi, F. Lebeau

736. Using AI to Estimate Vineyards and Vegetables Vigour and Yield

... S. Fountas

737. Using Dynamic Crop Growth Data to Assess Early Season N Status in Maize

Nitrogen (N) is perhaps the most important mineral nutrient determining crop growth and yield. Fertilizer sources can vary, but it is used in practically all cropping systems, and accounts for one of the highest input costs. Farmers often overapply N to their fields as a simple "insurance policy" to guarantee maximum yields. This can be problematic due to the volatile nature of N in the environment, as well reducing potential profits by not optimizing the rates. ... A. Yore, P. Lanza, L. Longchamps

738. Using Informative Bayesian Priors and On-farm Experimentation to Predict Optimal Site-specific Nitrogen Rates

Most U.S. Corn Belt states now recommend the Maximum Return to Nitrogen (MRTN) method for determining optimal nitrogen rates, which is based on 15 years of on-farm yield response to nitrogen trials. The MRTN method recommends a uniform rate for a region of a state. This study combines Illinois MRTN data, Bayesian methods, and on-farm experimentation from the Data Intensive Farm Management (DIFM) project to provide site-specific nitrogen recommendations. On-farm trials are now being used to pr... W. Brorsen, D. Poursina, C. Patterson, T. Mieno, B. Edge, E.D. Nafziger

739. Using Machine Vision to Build Field Maps of Forage Quality and the Need for Agriculture-specific Machine Vision Networks

Machine vision systems have truly come of age over the past decade. These networks are relatively simple to implement with systems such as YOLOv5 or the more recent YOLOv8. They are also relatively easy and computationally cheap to retrain to a custom data set, allowing for customization of these networks to new object detection and classification tasks. With this ease, it is no surprise that we are seeing an explosion of these networks and their application through all aspects of a... P. Nugent, J. Neupane

740. Using On-the-Go Soil Sensors to Assess Spatial Variability within the KS Wheat Breeding Program

In plant breeding the impacts of genotype by environment interactions and the challenges to quantify these interactions has long been recognized. Both macro and microenvironment variations in precipitation, temperature and soil nutrient availability have been shown to impact breeder selections. Traditionally, breeders mitigate these interactions by evaluating genotype performance across varying environments over multiple years. However, limitations in labor, equipment and seed availably can l... B. Evers, M. Rekhi, G. Hettiarachchi, S. Welch, A. Fritz, P.D. Alderman, J. Poland

741. Using Prescription Maps for in Field Evaluations of Parameteres Affecting Spraying Accuracy of Self-propelled Sprayer

Weed presence continues to reemerge year over year, chemical costs continue to increase, and chemical usage continuing to face increasing government oversight, are just a few of the challenges that site-specific weed management intends to address by minimizing wasted application of chemicals and reducing environmental load of active ingredients. Thus, sprayer system manufacturers have developed precision spray systems that allow the individual spray nozzles to be controlled precisely. These s... J. Mayer, P. Flores, J. Stenger

742. Using Pricise Gps/gis Based Barley Yield Maps to Predict Site-specific Phosphorus Requirements

Three fundamental stages and technologies as main parts of a precision farming project should be considered precisely. These are access to actual multi- dimensional variability detail or variable description on farms, creating a suitable variable-rate technology, and finally providing a decision support system. Some results of a long term practical research conducted by the author in Upon-Tyne Newcastle University of UK for reliable yield monitoring and mapping were utilised to prepare this p... A. Sanaei

743. Using Remote Sensing to Benchmark Crop Coefficient Curves of Sweet Corn Grown in the Southeastern United States

Irrigation is responsible for over 75% of global freshwater use, making it the largest consumer of the world’s freshwater resources. With freshwater scarcity increasing worldwide, increased efficient irrigation water use is necessary. Smart irrigation is described as ‘the linking of technology and fundamental knowledge of crop physiology to significantly increase irrigation water use efficiency'. Irrigation scheduling tools such as smartphone applications have become... E. Bedwell, L. Lacerda, T. Mcavoy, B.V. Ortiz, J. Snider, G. Vellidis, Z. Yu

744. Using Remote Sensing to Evaluate Cover Crop Performance and Plan Variable Rate Management

The adoption of cover crops (CC) in row-crop production, particularly in states like Indiana, has surged due to their recognized benefits in nutrient scavenging, soil health improvement, and erosion prevention. However, the spatial and temporal dynamics of CC performance pose challenges for efficient assessment and management. Traditional methods of quantifying CC production involve labor-intensive and time-consuming processes, creating a lag between data collection and decision-making for fa... S.A. Rubaino sosa, D. . Quinn, S. Armstrong

745. Using Remote Sensing to Quantify Biomass in Alfalfa

Satellite images are a useful decision support tool to optimize management practices at on-farm scale. Based on this, the development of predictive tools to estimate pasture biomass can be a promising framework to determine the best cutting time, maximizing biomass without compromising yield parameters. Therefore, the main objective of this study was to develop a regression model that allows estimating a value of biomass to give as a recommendation to farmers. To collaborate in their decision... M.F. Lucero, A. Zajdband, C. Hernandez, I. Ciampitti, A. Carcedo

746. Using Simulation Modeling to Evaluate the Corn Response to Deficit Irrigation Imposed During Reproductive Period

In Alabama, as in many regions of the southeastern states, flash droughts and rising temperatures present significant challenges to the sustainability of agricultural systems. Specifically maize, a crop with a high water demand, faces production risks due to these adverse conditions. The study explores the optimum irrigation scheduling strategies on maize (Zea mays L.) in the reproductive growth stages through the evaluation of the impact of three irrigation treatments, defined by Maximum All... J.S. Velasco, B.V. Ortiz, L. Nunes, R. Prasad, G. Hoogenboom

747. Using Soil Samples and Soil Sensors to Improve Soil Nutrient Estimations

Estimating soil nutrient levels, especially immobile nutrients like P and K, has been a primary activity for providers of precision agriculture services.  Soil nutrients often vary widely within fields and growers have been eager to manage them site-specifically.  There are many causes of the variability, including pedogenic factors such as soil texture, organic matter, landscape position and other factors that have resulted in an accumulation of unused nutrients in some areas of th... C.R. Maxton, T. Lund, E. Lund

748. Using the Adapt-N Model to Inform Policies Promoting the Sustainability of US Maize Production

Maize (Zea mays L.) production accounts for the largest share of crop land area in the U.S. It is the largest consumer of nitrogen (N) fertilizers but has low N Recovery Efficiency (NRE, the proportion of applied N taken up by the crop). This has resulted in well-documented environmental problems and social costs associated with high reactive N losses associated with maize production. There is a potential to reduce these costs through precision management, i.e., better application timing, use... S. Sela, H. Van-es, E. Mclellan, J. Melkonian, R. Marjerison , K. Constas

749. Using the Open Data Farm As a Digital Twin of a Farm in an Innovative School Setting to Increase Data Literacy and Awareness

In recent years, the number of digital applications and data streams has steadily increased, but knowledge and expertise in dealing with them has not increased to the same extent. The Open Data Farm is intended to make a significant contribution to education and training in order to increase data literacy in agriculture. The Open Data Farm (ODF) represents a twin of a real agricultural business as a 3D model in which existing data streams in various branches of the business are visu... D. Eberz-eder, E. Wölbert, J. Hinze, C. Weiß

750. Utilization of UASs to Predict Sugarcane Yields in Louisiana Prior to Harvest

One of the most difficult tasks that both sugarcane producers and processors face every year is estimating the yields of sugarcane fields prior to the start of harvest. This information is needed by processors to determine when the harvest season is to be initiated each year and by producers to decide when each field should be harvested. This is particularly important in Louisiana because the end of the harvest season is often affected by freeze events. These events can severely damage the cr... R.M. Johnson, B. Ramachandran

751. Utilizing ArUco Markers to Define Implement Boundaries

John Deere and Blue River Technology’s autonomous tillage system combines multidisciplinary efforts and cutting-edge technology to achieve Level 5—Unsupervised Autonomy. To create this engineering marvel, countless parameters need defined to ensure safe operation of the system; some of these parameters are static, while other of these parameters are dynamic. One particular set of parameters define the tillage implement’s boundaries for the software stack to utilize, and toda... R. Sleichter

752. Utilizing Hyperspectral Field Imagery for Accurate Southern Leaf Blight Severity Grading in Corn

Crop disease detection using traditional scouting and visual inspection approaches can be laborious and time-consuming. Timely detection of disease and its severity over large spatial regions is critical for minimizing significant yield losses. Hyperspectral imagery has been demonstrated as a useful tool for a broad assessment of crop health.  The use of spectral bands from hyperspectral data to predict disease severity and progression has been shown to have the capability of enhancing e... G. Vincent, M. Kudenov, P. Balint-kurti, R. Dean, C.M. Williams

753. Utilizing Image-based Artificial Intelligence for Grading Bovine Oocytes

For years, proper oocyte selection has been carried out with the precision of a lab technician’s eyes. The classification of oocytes using image-based artificial intelligence is a new technology that IVF lab technicians, cattle genetics companies, and veterinarians can utilize. Via the aspiration of the follicles on a cow’s ovaries, oocytes are able to be collected. Once oocytes are obtained from the ovaries of a cow, they are sent to an IVF lab to be cleaned and evaluated by a la... G. Koppelman, J.P. Fulton, S. Khanal, T. Berger-wolf

754. Utilizing Space-based Technology for Cotton Irrigation Scheduling

Accurate soil moisture content measurements are vital to precision irrigation management. Electromagnetic sensors such as capacitance and time domain reflectometry have been widely used for measuring soil moisture content for decades. However, to estimate average soil moisture content over a large area, a number of ground-based in-situ sensors would need to be installed, which would be expensive and labor intensive. Remote sensing using the microwave spectrum (such as GPS signals) has been us... A. Khalilian, X. Qiao, J.O. Payero, J.M. Maja, C.V. Privette, Y.J. Han

755. Utilizing Thermal and RGB Imaging for Nutrient Deficiency and Chlorophyll Status Evaluation in Plants

As global population growth and climate change continue to challenge food security, addressing agricultural issues efficiently and cost-effectively is vital for enhancing productivity. Integrating technology into agriculture, particularly through timely interventions, offers promising solutions to mitigate challenges before they escalate. This study investigates the feasibility of using thermal and RGB imaging as efficient, non-destructive methods to assess nutrient deficiencies and chlorophy... A.H. Rabia, D.G. Allam, E.F. Abdelaty, E.A. Abderaouf

756. Value of Map Sharing Between Multiple Vehicles Using Automated Section Control in the Same Field

Large area farms and even moderate sized farms employing custom applicators and harvesters have multiple machines in the same field at the same time conducting the same field operation.  As a method to control input costs and minimize application overlap, these machines have been equipped with automatic section control (ASC). Over application is a concern especially for more irregularly shaped fields; however modern technology including automated guidance combined with automatic section ... J. Bennett, C. Wilson, A. Sharda, T. Griffin

757. Variability in Observed and Sensor Based Estimated Optimum N Rates in Corn

Recent research showed that active sensors such as Crop Circle can be used to estimate in-season N requirements for corn. The objective of this research was to identify sources of variability in the observed and Crop Circle-estimated optimum N rates. Field experiments were conducted at two locations for a total of five sites during the 2007 growing season using a randomized complete block design with increasing N rates applied at V6-V8 (NV6) as the treatment factor. Field sites were selected ... R.P. Sripada, J.P. Schmidt

758. Variability in Yield Response of Maize to N, P and K Fertilization Towards Site-specific Nutrient Recommendations in Two Maize Belts in Togo

Savannah and central regions are the major maize production zones in Togo, but with maize grain yields at a threshold of only 1.5 Mg ha-1. We use a participatory approach to assess the importance of the major three macro elements (N, P and K) for maize cropping in the two regions in order to further allow for site-specific and scalable fertilizer recommendations. Thirty farmers’ fields served as pilot sites, allocated within the two regions to account for spatial variability ... J.M. Sogbedji, M. Lare, A.K. Lotsi, K.A. Amouzou, T. Agneroh

759. Variable Rate Application to Improve Cro Protection in Orchards and Vineyards. Prescription Maps and Satellites to Accomplish EU Farm to Fork Strategy

Accurate canopy characterization is crucial for a targeted application of plant protection products following variable rate application (VRA) concept. Remote sensing offers a robust and rapid monitoring tool that allows determining the characteristics of the vegetation from aerial platforms at different spatial resolutions. Previous work have demonstrated that drone-based imagery can be used to estimate canopy height, width, and canopy volume accurately enough to allow a full automation of VR... E. Gil, F. Garcia-ruíz, J. Biscamps, R. Salcedo, J. Campos

760. Variable Rate Fertilization in a High-yielding Vineyard of Cv. Trebbiano Romagnolo May Reduce Nitrogen Application and Vigour Variability Without Loss of Crop Load

The site-specific management of vineyard cultural practices may reduce the spatial variability of vine vigor, contributing to achieve the desired yield and grape composition. In this framework, variable rate fertilization may effectively contribute to reduce the different availability of mineral nutrients between different areas of the vineyard, and so achieving the vine’s aforementioned performances. The present study was aimed to apply a variable rate fertilization in a high... G. Allegro, R. Martelli, G. Valentini, C. Pastore, R. Mazzoleni, F. Pezzi, I. Filippetti, A. Ali

761. Variable Rate Nitrogen Approach in a Potato-wheat-wheat Cropping System

Nitrogen application in agriculture is a vital process for optimal plant growth and yield outcomes. Different factors such as topography, soil properties, historical yield, and crop stress affect nitrogen (N) needs within a field. Applying variable N within a field could improve precision agriculture. Optimal N management is a system that involves applying a conservative variable base rate at or shortly after planting followed by in-season assessment and, if needed, variable rate application&... E.A. Flint, M. Yost, B.G. Hopkins

762. Vegetation Coverage Specific Flower Density Estimation in Blackberry Using Unmanned Aerial Vehicle (UAV) Remote Sensing

The effective management of agricultural systems relies on the utilization of accurate data collection techniques to analyze essential crop attributes to enhance productivity and ensure profits. Data collection procedures for specialty horticultural crops are mostly subjective, time consuming and may not be accurate for management decisions in both phenotypic studies and crop production. Reliable and repeatable standard methods are therefore needed to capture and calculate attributes of horti... A. Tagoe, C. Koparan, A. Poncet, D.M. Johnson, M. Worthington, D. Wang

763. Veris Technologies - Sponsor Presentation

Veris Technologies, Inc. designs, builds, and markets sensors and software for precision agriculture. ... T. Lund

764. Vis/NIR Spectroscopy to Estimate Crude Protein (CP) in Alfalfa Crop: Feasibility Study

The fast and reliable quality determination of alfalfa crop is of interest for producers to make management decisions, the dealers to determine the price, and the dairy producers for livestock management. In this study, the crude protein (CP), one of the main quality indices of alfalfa, was estimated using the visible and near-infrared (Vis/NIR) spectroscopy. A total of 68 samples from various variety trials of alfalfa crop were collected under the irrigated and rainfed conditions. The diffus... M. Maharlooei, S. Bajwa, S.A. Mireei, A. Shirzadi, S. Sivarajan, M. Berti, J. Nowatzki

765. Voronoi-based Ant Colony Optimization Approach: Autonomous Robotic Swarm Navigation for Crop Disease Detection

The early detection of agricultural diseases is essential for sustaining food production and economic viability over the long term. To improve disease detection in agriculture, this paper presents an innovative computational approach that utilizes the Voronoi-based Ant Colony Optimization (V-ACO) algorithm with Swarm Robotics (SR). Inspired by the social behaviors observed in insect colonies such as honeybees and ants, SR offers new opportunities for precision farming. SR utilizes the coordin... S. Gummi, M. Alahe, Y. Chang, C. Pack

766. Water Stress Assessment for a Better Within-field Nitrogen and Irrigation Management

Swedish crops production is predominantly rain fed; and until now, food security has been safeguarded by relying on imports if seasonal variations of rainfall reduce yield quantity and quality. In Sweden, based on climate change scenarios, farmers organizations and representatives consider water to be a critical factor that potentially will limit the yield levels to a larger extent in the future. In the last decades, it is registered very dry seasons (e.g. 2018 and 2019) and long dry spells i... O. Alshihabi, B. Stenberg, J. Barron

767. Weather Impacts on UAV Flight Availability for Agricultural Purposes in Oklahoma

This research project analyzed 21 years of historical weather data from the Oklahoma Mesonet system.  The data examined the practicality of flying unmanned aircraft for various agricultural purposes in Oklahoma.  Fixed-wing and rotary wing (quad copter, octocopter) flight parameters were determined and their performance envelope was verified as a function of weather conditions.  The project explored Oklahoma’s Mesonet data in order to find days that are acceptable for fly... P. Weckler, C. Morris, B. Arnall, P. Alderman, J. Kidd, A. Sutherland

768. Web Application for Automatic Creation of Thematic Maps and Management Zones - AgDataBox-Fast Track

Agriculture is challenging to produce more profitably, with the world population expected to reach some 10 billion people by 2050. Such a challenge can be achieved by adopting precision agriculture and digital agriculture (Agriculture 4.0). Digital agriculture (DA) has become a reality with the availability of cheaper and more powerful sensors, actuators and microprocessors, high-bandwidth cellular communication, cloud communication, and Big Data. DA enables information to flow from used agri... J. Aikes junior, E.G. Souza, C. Bazzi, R. Sobjak, A. Hachisuca, A. Gavioli, N. Betzek, K. Schenatto, W. Moreira, E. Mercante, M. Rodrigues

769. Wheat Spikes Counting Using Density Prediction Convolution Neural Network

Vision-based wheat spikes counting can be valuable for pre-harvest yield estimation for growers and researchers. In this study, wheat spike counting convolutions neural networks were implemented to solve the problem of vision-based wheat yield prediction problem. Encoder-decoder style convolutional neural networks (CNN) were developed with a Global Sum Pooling (GSP) layer as its output layer and trained to produce a density map which predicts the pixelwise wheat spikes density.  Thi... C. Liew, S. Pitla

770. Where to Put Treatments for On-farm Experimentation

On-farm experimentation has become more and more popular due to advancements in technology. These experiments are not as costly as before, as current machinery can allocate different levels of treatment to specific plots. The main goal of this kind of experiment is to obtain a site-specific nutrient level. The yield behavior is different based on the researcher’s treatment. One unanswered question for on-farm experimentation is how the treatments should be allocated in the first place s... D. Poursina, W. Brorsen

771. Who Are the Data Stewards: Moving Data Driven Agriculture Forward

Nearly a decade ago agricultural equipment manufacturers, service providers, retailers, land grant universities, and grower organizations came together to begin discussing the growing needs for producers to manage their farm data. This discussion was partly fueled by the industry shifting from moving data via physical media to cloud API connections. Several initiatives including the Agricultural Data Coalition (ADC) were subsequently launched focusing on addressing data privacy and security c... B.E. Craker, D. Bierman

772. Window-based Regression Analysis of Field Data

High-resolution satellite and areal imagery enables multi-scale analysis that has previously been impossible.  We consider the task of localized linear regression and show that window-based techniques can return results at different length scales with very high efficiency.  The ability of inspecting multiple length scales is important for distinguishing factors that vary over different length scales.  For example, variations in fertilization are expected to occur on shorter len... A.M. Denton, H. Chavan, D.W. Franzen, J.F. Nowatzki

773. Winter Wheat Genotype Effect on Canopy Reflectance: Implications for Using NDVI for In-season Nitrogen Topdressing Recommendations

Active optical sensors (AOSs) measure crop reflectance at specific wavelengths and calculate vegetation indices (VIs) that are used to prescribe variable N fertilization. Visual observations of winter wheat (Triticum aestivum L.) plant greenness and density suggest that VI values may be genotype specific. Some sensor systems use correction coefficients to eliminate the effect of genotype on VI values. This study was conducted to assess the effects of winter wheat cultivars and growing conditi... O.S. Walsh, S.M. Samborski, M. Stępień, D. Gozdowski, D.W. Lamb, E.S. gacek, T. Drzazga

774. Within Field Cotton Yield Prediction Using Temporal Satellite Imagery Combined with Deep Learning

Crop yield prediction at the field scale plays a pivotal role in enhancing agricultural management, a vital component in addressing global food security challenges. Regional or county-level data, while valuable for broader agricultural planning, often lacks the precision required by farmers for effective and timely field management. The primary obstacle in utilizing satellite imagery to forecast crop yields at the field level lies in its low temporal and spatial resolutions. This study aims t... R. Karn, O. Adedeji, B.P. Ghimire, A. Abdalla, V. Sheng, G. Ritchie, W. Guo

775. Within-field Profitability Assessment: Impact of Weather, Field Management and Soils

Profitability in crop production is largely driven by crop yield, production costs and commodity prices. The objective of this study was to quantify the often substantial yet somewhat illusive impact of weather, management, and soil spatial variability on within-field profitability in corn and soybean crop production using profitability indices for profit (net return) and return-on-investment (ROI) to produce estimates. We analyzed yield and cropping system data provided by 42 farmers within ... P.M. Kyveryga, S. Fey, J. Connor, A. Kiel, D. Muth

776. Within-field Spatial Variability in Optimal Sulfur Rates for Corn in Minnesota: Implications for Precision Sulfur Management

The ongoing decline in sulfur (S) atmospheric depositions and high yield crop production have resulted in S deficiency and the need for S fertilizer applications in corn cropping systems. Many farmers are applying S fertilizers uniformly across their fields. Little has been reported on the within-field spatial variability in optimal S rates and the potential benefits of variable rate S applications. The objectives of this study were to 1) assess within-field variability of optimal S rates (OS... R.P. Negrini, Y. Miao, K. Mizuta, K. Stueve, D. Kaiser, J.A. Coulter

777. X-ray Imaging in Breeding and Harvesting Processes

The application of X-ray technology has a long tradition in different medical and technical fields. Compared to other sensor systems, its advantages lie in the capability to reveal structures within objects non-destructively. The analysis of X-ray images with image processing methods is applied for quality control, the detection of foreign objects or damages and other anomalies (e.g. in organs or bones). Until recently, the application of X-ray was mainly constrained to stationary application... M. Weule, E. Hufnagel, J. Claussen, A. Berghaus, S. Burkhart, P. Noack, S. Gerth

778. Yield Analysis in Sugarcane Harvesters Using Design of Experiments (DoE) Methodology

The sugarcane crop is highlighted in national agribusiness, Brazil is the world’s largest producer of the plant, and the prospection of specialists is of strong growth for the next years. However, in order to increase productivity, technological interventions through of precision agriculture must be implemented. Among them, the management of inputs guided by yield spatial variability for otmizing production and income. This project approaches the implementation of the methodology of ana... M.L. Da silva, J. . Alves de lima, A. Balbinot, J.P. Molin

779. Yield Estimation for Avocado Using Systematic Sampling Techniques

Avocado is a high value crop ranking fourth among the planted fruit species in Chile with more than 32,000 ha. Yield estimation is an important challenge in avocado due to its phenology, the size of the tree, and to the large variability usually observed within the orchards. Due to the practical difficulties to sample the trees we use the following approach: 1) establish a systematic, non-aligned grid with > 20 sampling points (trees)/field, 2) previous to harvest, and ... H.P. Poblete, R.A. Ortega

780. Yield Mapping in Fruit Farming

Due to the importance of increasing the quantity and quality of world agricultural production, the use of technologies to assist in production processes is essential. Despite this, a timid adoption by precision agriculture (PA) technologies is verified by the Brazilian fruit producers, even though it is one of the segments that had been stood out in recent years in the country's economy. In the PA context, yield maps are rich sources of information, especially by species harvested through... C.L. Bazzi, M.R. Martins, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, A. . Hachisuca, F. Franz

781. Yield Monitoring System for Radish and Cabbage Under Korean Field Conditions

Yield monitoring is considered an essential tool to optimize resource utilization and provide an accurate assessment of crops for drylands. The objective of this study was to assess mass-based and volume-based yield monitoring under laboratory-simulated and field conditions for cabbage and radish. During the experiment, impact plate angles, conveyor speeds, and falling heights were systematically varied to investigate the effects on cabbage and radish yield during harvesting. Digital filterin... M. Gulandaz, M. Kabir, K. Shafik, S. Chung

782. Yield Potential Zones and Their Relationship with Soil Taxonomic Classes and Management Zones

The use of management zones (MZ) to subdivide agricultural areas based on the variability of yield potential and production factors is increasingly being explored by scientific research and demanded by farmers. However, there is still much uncertainty about which layers of information and procedures should be adopted for this purpose. Thus, our goal was to demonstrate whether simplistic approaches to creating MZ can satisfactorily address the variability of yield potential and soil classes. F... L.R. Amaral, H. Oldoni, D.D. Melo, N.A. Rosin, M.R. Alves, J.M. Demattê

783. Yield, Residual Nitrogen and Economic Benefit of Precision Seeding and Laser Land Leveling for Winter Wheat

Rapid socio-economic changes in China, such as land conversion and urbanization etc., are creating new scopes for application of precision agriculture (PA). It remains unclear the application effective and economic benefits of precision agriculture technologies in China. In this study, our specific goal was to analyze the impact of precision seeding and laser land leveling on winter wheat yield,... J. Chen , P.L. Chen, J.C. Zhao, S.Y. Wang, J.C. Li, Q. Zhang, T.H. Hu, G.L. Shi

784. You Can Not Manage What You Dont Measure

The problem of variability in soil nutrient analysis has been studied for years by a number of industry experts; unable to decipher and commercialize hyperspectral soil sensing. Many studies have taken years of testing to account for variability thathas a dramatic impacts on precision of recommendations. The main tradeoff we have identified is between accuracy and precision. Large quantities of raw data are requir... K. Fleming, N. Schottle, P. Nagel, G. Koch

785. Zone Mapping Application for Precision-farming: a Decision Support Tool for Variable Rate Application

We have developed a web-based decision support tool, Zone Mapping Application for Precision Farming (ZoneMAP, http://zonemap.umac.org), which can automatically determine the optimal number of management zones and delineate them using satellite imagery and field survey data provided by users. Application rates, say for fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming applicators. ZoneMAP is linked to Digital Nor... X. Zhang, C. Helgason, G. Seielstad, L. Shi