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2024

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Authors

Abban-Baidoo, E

Abbasi, E

Abdalla, A

Abdelaty, E.F

Abderaouf, E.A

Abdinoor, J.A

Abney, M

Abon, J.O

Aboutalebi, M

Acconcia Dias, M

Acharya, I

Achigan-Dako, E

Acosta, M

Adamchuk, V

Adedeji, O

Adil, M

Admasu, W.A

Adolwa, I

Aduramigba-Modupe, V

Agampodi, G.S

Agarwal, D

Akin, S

Akorede, B.A

Al Amin, A

Al-Shammari, D

Alahe, M

Alchanatis, V

Alexandroff, V

Aliloo, J

Allam, D.G

Allegro, G

Allen, M

Almeida, S.L

Alshihabi, O

Alves de Lima, J.

Alves, M.R

Alwaseela, H

Amaral, L.R

Amely, N

Ampatzidis, Y

Amri, M

Andales, A

Anderson Guerrero, S

Anderson, W

Anderson-Guerrero, S

Angar, H

Angrino Chiran, D.F

Antunes de Almeida, L.F

Anup, A

Apolinário, E

Archontoulis, S

Ardigueri, M

Armstrong, P

Armstrong, P.R

Armstrong, S

Arnall, B

Arnold, S

Arun, A

Aryal, B

Asci, S

Asgedom, H

Avemegah, E

Avila, E.N

Ayipio, E

Azzam, T

B K, A

BAdua, S

BHATTARAI, A

BISCAMPS, J

Badua, S

Bagavathiannan, M

Bai, F

Bai, G

Bailey, J

Bakshi, A

Balabantaray, A

Balafoutis, A

Balasundram, S.K

Balbinot, A

Balboa, G

Balint-Kurti, P

Balmos, A

Bansal, G

Bantchina, B

Bao, Y

Barai, K

Barbosa, M

Bari, M.A

Barnes, E.M

Baroni, G

Barron, J

Basir, M

Basir, M.S

Basran, P.S

Bastos, L

Bathke, K.J

Batuman, O

Bazzi, C.L

Beasley, D

Bech, A

Becker, M

Bede, L

Bedwell, E

Behera, S

Behrendt, K

Bello, N

Benjamin, M

Bennett, B

Berger-Wolf, T

Berghaus, A

Bernal Riobo, J.H

Berretta, B.G

Bhandari, M

Bhandari, S

Bhattarai, A

Bhattarai, B

Biaou, A

Bierman, D

Bindish, R

Bishop, T

Biswas, A

Boatswain Jacques, A.A

Boersma, S

Bonfil, D.J

Bonke, V

Boote, K

Borbás, Z

Bortolon, G

Bourlai, T

Boyer, W

Bradacova, K

Bramley, R

Brasco, T.L

Brinton, C

Brokesh, E

Bromfield, C

Brooks, J.P

Brorsen, W

Brown, A.J

Brown, P

Buckmaster, D

Bui, T

Bullock, D

Burkhart, S

Burks, T

Burlai, T

Busby, S

Busch, G

Byers, C

Byrne, D

CAMPOS, J

CARCEDO, A

Caballero-Rodriguez, A.M

Cafaro La Menza, N

Cai, S

Cambouris, A

Cammarano, D

Campos, S

Canal Filho, R

Canavari, M

Cano, P.B

Cao, Q

Cao, W

Cappelleri, D

Capper, J

Caragea, D

Caras, T

Carcedo, A

Castiblanco Rubio, F.A

Castro, S.G

Cesario Pinto, J

Chakraborty, M

Chamara, N

Chang, Y

Chen, C

Chen, X

Chikowo, R

Cho, J

Choton, J

Choudhury, S.D

Chung, S

Ciampitti, I

Cisdeli Magalhães, P

Clark, J

Clark, N

Claussen, J

Clay, D.E

Cohen, Y

Colbert, J

Colley, T

Coppola, A

Corassa, G

Cordova Gonzalez, C

Correndo, A

Costa Barboza, T.O

Costa Souza, J.B

Costa, O.P

Coulter, J.A

Craker, B

Craker, B.E

Craven, S

Crawford, M

Cristancho Rojas, O.Y

Cross, T

Culman, S

Czarnecki, J

DEBANGSHI, U

Da Silva, E.R

Da Silva, J

Da Silva, M.L

Daggupati, P

Dalal, A

Dalla Betta, M.M

Davis, G

De Oliveira Moreira, F

Dean, C

Dean, R

Deleon, E

Demattê, J.M

Derdall, E

Deri Setiyono, T

Derrick, J

Dewdney, M

Dey, S

Dhillon, R

Dhiman, V

Diallo, A.B

Diatta, A

Diaz, D

Dickin, E

Dill, T

Dilmurat, K

Djighaly, P

Dokoozlian, N

Dorissant, L

Dossou-Yovo, E.R

Downing, B

Drewry, D

Dua, A

Dua, S

Duarte, P.R

Duary, B

Duchemin, M

Duddu, H

Duron, D

Dutilleul, P

Dutta, W

E. Flores, A

Eberz-Eder, D

Edge, B

Eldeeb, E

Eldefrawy, M

Emamalizadeh, S

Emmons, A

Enger, B.D

Engle, J

Erazo, E

Erickson, B

Esau, T.J

Eshel, G

Estrada, A

Evans, J

Everett, M

Ewanik, C

Fallon, E

Farooque, A

Fassinou Hotegni, N

Fathololoumi, S

Felderhoff, T

Felipe dos Santos, A

Fenech, A

Feng, G

Fernandez, O

Fernando, H

Fernández, F

Ferraz, C

Ferreyra, R

Filippetti, I

Filippi, P

Firozjaei, M.K

Flippo, D

Flores, A

Flores, P

Fodjo Kamdem, M

Folle, S

Ford, L

Foster, J

Fountain, J

Fountas, S

France, W

Francisco, E

Franklin, K.F

Franz, F

Frederick, Q

Freire de Oliveira, M.F

Frimpong, K

Frimpong, K.A

Fritz, B.K

Fu, Z

Fuller, H.D

Fulton, J.P

Gadhwal, M

Gahler, A

Gal, A

Galeano, S.A

Gamble, A

Gan, H

Gandorfer, M

Garcia-Ruíz, F

Gardezi, M

Garg, A

Ge, Y

Gebler, L

Gerken, A.R

Gerth, S

Ghanbari Parmehr, E

Ghansah, B

Ghimire, B

Ghimire, B.P

Gigena, B

Gil, E

Gilson, A

Gimenez, L.M

Gimenez, V

Glavin, M

Goel, R

Gomez, F

González Piqueras, J

Grant, R.H

Grassini, P

Green, O

Grijalva Teran, I.A

Grijalva, I

Guan, H

Guinness, J

Gulandaz, M

Gummi, S

Guo, W

Gupta, S

Gómez-Candón, D

Ha, T

Haapala, H.E

Hachisuca, A

Han, M

Han, S

Hand, L

Hansen, J

Hansen, N

Hanyabui, E

Harari, A

Harkin, S.J

Harris, E.W

Harris, G

Harris, W.E

Harsha Chepally, R

Hartschuh, J.M

Hashim, Z.K

Hassan, M

Hawkins, E

Hazzoumi, Z

Hefley, T

Hegedűs, G

Hehar, G

Heil, K

Henrie, A

Henties, T

Hernandez, C

Herrmann, I

Hessel, R

Hillyer, C.C

Hintz, G.D

Hinze, J

Hodeghatta, U.R

Hoffmann Silva Karp, F

Holland, K.H

Holthaus, D

Hong, C

Hoogenboom, G

Hopkins, B

Horbe, T

Horváth, B

Hostert, P

Hovio, H

Hu, J

Huang, Y

Huang, Z

Huender, L

Hufnagel, E

Hunhoff, L

Hunt, L

Igwe, K.E

Ingram, B

Inácio, F.D

Islam, M

JANBAZIALAMDARI, S

Jagadish, K

Jakhar, A

Jakimow, B

Jamei, M

Janjua, U.U

Janz, A

Javed, B

Jha, G

Jha, S

Jhala, A

Jiménez Castaño, V

Joalland, S

Johnson, D.M

Johnson, J

Jones, N

Joseph, K

Joshi, D

Joshi, N

Joshi, R

Jørgensen, R.N

KABIR, M

KC, K

Kaboré, J.P

Kagami Taira, F

Kaiser, D

Kaloya, T

Kalra, A

Kamerer, C

Karam, A

Karamidehkordi, E

Karangwa, A

Karkee, M

Karn, R

Karppinen, E

Kasimati, A

Katari, S

Kaushal, S

Kechchour, A

Keil, F

Kelley, J

Kemerait, R.C

Kemeshi, J.O

Kerry, R

Ketterings, Q

Khakbazan, M

Khanal, S

Khosla, R

Khuimphukhieo, I

Kichler, J

Killer, A

Kim, J

Kim, M

Kisekka, I

Kittemann, D

Klapp, I

Klopfenstein, A

Knezevic, S

Kopanja, M

Koparan, C

Koppelman, G

Kovacs, A

Kovacs, P

Krogmeier, J

Kudenov, M

Kukal, S

Kukorelli, G

Kulhandjian, H

Kulhandjian, M

Kulmany, I.M

Kumari, S

Kunwar, S

Kyveryga, P

Kósa, A

Lacasa, J

Lacerda, L

Lajunen, A

Landivar, J

Landivar-Scoot, J.L

Lanza, P

Laor, Y

Larbi, P.A

Lati, R

Lavagnino, M

Lee, B

Lee, S

Lee, W

Lehmann, J

Leininger, A

Lemes Bosche, L

Lemke, R

Lemus, S

Leon Rueda, W.A

Lesueur, C

Leszczyńska, R

Lexow, T

Li, H

Li, L

Li, M

Li, X

Li, Y

Lichtenberg,, S

Liew, C

Lindsey, L

Lingua, L.N

Linker, R

Liu, K

Liu, P

Liu, W

Liu, Z

Lizarazo Salcedo, I.A

Longchamps, L

Lord, E

Love, D

Love, D.J

Lovejoy, K

Lowenberg-DeBoer, J

Lu, J

Lu, Y

Lucero, M.F

Luck, J.D

Ludewig, U

Lukwesa, D

Lund, E

Lund, T

Luns Hatum de Almeida, S

López-Urrea, R

MECHRI, M

Maatougui, M

Machiraju, R

Maddonni, G

Maestrini, B

Magalhaes Cisdeli, P

Maimaitijiang, M

Makarov, J

Maktabi, S

Mandal, D

Manyatsi, A

Marcaida, M

Maritan, E

Martinez Martinez, L.J

Marx, S

Marziotte, L

Masnello, J.C

Matavel, C

Mateus-Rodriguez, J.F

Maxton, C.R

Mazzeo, B

Mazzoleni, R

Mbakwe, I

McAvoy, T

McCornack, B

McFadden, J

McGlinch, G

McIntyre, J

McPherson, T

Meena, R

Meena, R.K

Melnitchouck, A

Melo, D.D

Menegasso, A.E

Menendez III, H

Meyer, L

Meyer, T

Meyer-Aurich, A

Mezger, J

Mhlongo, N

Miao, Y

Michels, M

Mieno, T

Miguez, F

Milics, G

Millett, B

Mimić, G

Minyo, R

Mishamo, M

Mitra, S

Mizuta, K

Moghadham, A

Mokhtari, A

Molin, J.P

Molina Cyrineu, I

Mommen, D

Monaghan, J

Monroe, T

Montero Pinilla, O.G

Montoya Sevilla, F

Mooleki, P

Morad-Talab, N

Morales, A.C

Moreira, B

Moreno, L.A

Morgan, S

Morimoto, E

Morris, D

Mosquera, C

Moulay, H

Mueller, N

Muller, I

Munar Vivas, O

Munar-Vivas, O.J

Murphy, J.M

Murrell, T

Mutegi, J

Muthamia, J

Muvva, V

Mußhoff, O

Mwunguzi, H

Müller, T

Nafziger, E.D

Nagarajan, L

Nagle, M

Nandi, A

Narayana, C

Natarajan, B

Nazrul, F

Negrini, R.P

Neils, W

Neumann, G

Neupane, J

Ng, C

Nguyen, A

Nieman, S.T

Nkebiwe, M

Nketia, K

Noack, P

Nocco, M

Nocera Santiago, G.N

Norquest, S

Nouiri, I

Nugent, C.I

Nugent, P

Nunes, L

Nze Memiaghe, J

Nze Memiaghe, J.D

O'Connor, C

Ochoa, O

Odoom, E

Oldoni, H

Oliveira, L

Oliveira, M.F

Oliveira, R

Oliveira, V

Oliveira, W.K

Ome Narvaez, J.D

Onyekwelu, I

Orlando Costa Barboza, T

Ortega, R.A

Ortez, O

Ortiz, B.V

Oster, Z

Ottley, C

PHILLIPS, S

Paccioretti, P

Pack, C

Paglia, C

Pagé Fortin, M

Pal, P

Palla, S

Parbi, B

Pathak, H

Patterson, C

Paz Kagan, T

Paz-Kagan, T

Pecze, R

Peduzzi, A

Peets, S

Peiretti, J

Pellegrini, P

Perdomo, D.F

Pereira de Souza, F

Persch, J.R

Persson, K

Phillips, S

Pidaparti, R

Piepho, H

Pietrzyk, P

Pilcon, C

Pinke, G

Pitla, S

Piya, N.K

Poblete, H.P

Pokharel, P

Poncet, A

Pordesimo, L.O

Porter, C

Porter, W

Potlapally, A

Pott, L.P

Pourreza, A

Poursina, D

Pramanik, S

Prasad, R

Prasad, V

Prestholt, A

Previtali, P

Pronk, A

Psiroukis, V

Puntel, L

Puntel, L.A

Purcell, L

Pérez García, Y

Qin, J

Quinn, D.

Rabello, L.M

Rabia, A.H

Raeth, P.G

Raheja, A

Rahman, M

Rai, S

Rains, G

Raitz Persch, J

Ramasamy, R.P

Ramirez-Gonzalez, D.A

Ramos-Tanchez, J

Ransom, C.J

Rathore, J

Rattalino, J

Rauber, L.A

Raupp, M

Reeks, M.C

Rehman, T

Reinholz, A

Ritchie, G

Roa Acosta, G

Roa Bello, J.C

Roberts, T

Robinette, M

Roby, M

Rocha, D

Rocha, D.M

Rodrigues Alves Franchi, M

Rontani, F

Rose, D

Rosen, C

Rosin, N.A

Ross, J

Rossi, C

Rozenstein, O

Ru, S

Rubaino Sosa, S.A

Rubiano, Y

Ruiz Diaz, D

Rupp, C

Rutter, M.S

SALCEDO, R

Safranski, T.J

Sahoo, M

Saito, K

Salem, M.A

Sales, L

Salunga, N.G

Salzer, Y

Samborski, S.M

Sams, B

Sanchez, L

Sanders, K

Sandholtz, C

Sandoval, D.F

Santos, R

Sanz-Saez, A

Sapkota, A

Sapkota, R

Scarpin, G

Scarpin, G.J

Schad, J

Schapaugh, W

Schenatto, K

Schmidt, R

Schoenau, J

Scholz, O

Schuenemann, G.M

Schumacher, L

Schwalbert, R.A

Scott, J.L

Scott, M

Scudiero, E

Serfa Juan, R.O

Setiyono, T

Shafik, K

Shajahan, S

Shang, J

Sharda, A

Sharda, V

Sharma, A

Sharma, V

Sharry, R

Shearer, S.A

Shende, K

Sheng, V

Sher, M

Sherafat, A

Shi, Y

Shiratsuchi, L

Shirley, A

Shirtliffe, S

Shovic, J

Shrestha, S

Sigdel, U

Sihi, D

Siliveru, K

Silva, A.N

Silva, J.E

Silva, R.P

Silva, W

Singh, R

Skovsen, S

Sleichter, R

Smith, B.K

Smith, T

Snider, J

Sobjak, R

Soderstrom, M

Sogbedji, J.M

Souza, E

Souza, J.B

Souza, W.J

Spiesman, B

Spina, A.N

Srinivasagan, S

Stahl, K

Stansell, J

Starek, M

Steele, K

Stenberg, B

Stencinger, D

Stewart, C

Stewart, Z

Stueve, K

Subramoni, H

Sudduth, K.A

Suh, C

Suleiman, A.A

Sulik, J

Sun, R

Swenson, A

Swinton, S.M

Syed, H.H

Sysskind, M

Sysskind, M.N

Sánchez Tomás, J

Sánchez Virosta, �

Sørensen, C.G

T.Meyer, S

Tagoe, A

Takkellapati, N

Takoo, G

Tarapues, H.B

Tarshish, R

Tasissa, A

Taylor, J

Tevis, J

Tharzeen, A

Thippareddi, H

Thomas, A

Thomas, A.D

Thomas, L

Thompson, L

Thorson, N

Tian, Y

Tietje, R

Tilse, M.J

Tobaldo, B

Torres, U

Trang, T

Trefz, K

Tyson, C

Uhrmann, F

Ungar, E.D

Unruh, R

Uyar, H

Vail, B

Valencia Ramirez, P

Valencia-Correa, J.M

Van Langevelde, F

Van Oort, P

VanderPlas, S

Varga, Z

Vargas, R

Velasco, J.S

Vellidis, G

Venkatesh, R

Verdi, A.K

Verhoff, K

Vincent, G

Vinod, S

Vinzio, F

Virk, S

Vitali, G.-

Vitantonio, L

Vona, V

Wakahara, S

Walsh, O

Waltz, L

Wang, C

Wang, D

Wang, D.R

Wang, J

Wang, W

Wang, Y

Wardle, E

Warren, C.J

Watanabe, K

Weber, N

Weinhold, B

Weinmann, M

Weiß, C

Wells, D

Wells, G

Weule, M

Wever, H

Whitaker, B

Wieber, E

Wieber, E.N

Williams, C

Williams, C.M

Willness, C

Wilson, D

Wilson, J.A

Witt, T

Won, K

Worosz, M

Worthington, M

Wölbert, E

Xu, J

Xu, S

Xu, X

Xu, Z

Yadav, P.K

Yang, C

Yang, X

Yang, Z

Yoder, J

Yore, A

Yu, K

Yu, Z

Zajdband, A

Zaman, Q.U

Zeddies, H

Zhang, D

Zhang, J

Zhang, N

Zhang, X

Zhang, Y

Zhao, H

Zhao, L

Zhen, X

Zheng, J

Zhoa, L

Zhou, C

Zhou, J

Zhu, C

Zhu, H

Zhu, Y

Ziadi, N

Zingore, S

Zsebő, S

Zuñiga, J.P

chang, Q

da Cunha, I.A

de Boer, W.F

de Oliveira Costa Neto, A

de Oliveira, M.F

de knegt, H

dos Santos, C.L

li, F

liu, X

tao, H

van Evert, F

van Steenbergen, S

van Versendaal, E

Šusliková, B

Topics

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

Oral

Poster

Year

2024

Home » Year » Results

Year

Filter results363 paper(s) found.
1. 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
2. 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
3. 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
4. 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
5. 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 Download paper
6. 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 Download paper
7. 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
8. 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 Download paper
9. 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 Download paper
10. 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 Download paper
11. 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 Download paper
12. 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
13. 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 Download paper
14. 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 Download paper
15. 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 Download paper
16. 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 Download paper
17. 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 Download paper
18. 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 Download paper
19. 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
20. 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 Download paper
21. 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 Download paper
22. Africa Regional Meeting ... K.A. Frimpong
23. African Association for Precision Agriculture Community Meeting ... K.A. Frimpong, V. Aduramigba-modupe, N. Fassinou hotegni
24. 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 Download paper
25. 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 Download paper
26. 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 Download paper
27. 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 Download paper
28. 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
29. 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 Download paper
30. 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 Download paper
31. 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 Download paper
32. 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 Download paper
33. 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 Download paper
34. 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 Download paper
35. 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 Download paper
36. 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
37. 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
38. 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 Download paper
39. 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 Download paper
40. 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
41. 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 Download paper
42. 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 Download paper
43. 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 Download paper
44. 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
45. 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 Download paper
46. Asia and Oceania Regional Meeting ... S.K. Balasundram
47. 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 Download paper
48. 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 Download paper
49. 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
50. 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
51. 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 Download paper
52. 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 Download paper
53. 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 Download paper
54. 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 Download paper
55. 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 Download paper
56. 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 Download paper
57. 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
58. 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
59. 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
60. 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 Download paper
61. 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
62. 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 Download paper
63. 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 Download paper
64. 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
65. 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 Download paper
66. 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
67. 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
68. 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
69. 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 Download paper
70. Can AI and Automation Transform Specialty Crop Production? ... Y. Ampatzidis
71. 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
72. 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
73. 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 Download paper
74. 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 Download paper
75. 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 Download paper
76. 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 Download paper
77. 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
78. 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 Download paper
79. 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 Download paper
80. 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 Download paper
81. 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 Download paper
82. 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 Download paper
83. 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 Download paper
84. 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 Download paper
85. 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 Download paper
86. 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
87. 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 Download paper
88. 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 Download paper
89. 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
90. 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 Download paper
91. 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 Download paper
92. 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 Download paper
93. 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 Download paper
94. 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 Download paper
95. 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 Download paper
96. 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 Download paper
97. 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 Download paper
98. 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
99. 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
100. 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
101. 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 Download paper
102. 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 Download paper
103. 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 Download paper
104. 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
105. 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
106. 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 Download paper
107. 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 Download paper
108. 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 Download paper
109. 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 Download paper
110. 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
111. 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 Download paper
112. 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 Download paper
113. 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 Download paper
114. 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
115. 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 Download paper
116. 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 Download paper
117. 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 Download paper
118. 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
119. 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 Download paper
120. 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
121. 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 Download paper
122. 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 Download paper
123. 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 Download paper
124. 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
125. 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 Download paper
126. 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
127. 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 Download paper
128. 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 Download paper
129. 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 Download paper
130. 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 Download paper
131. 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
132. 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
133. 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 Download paper
134. 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
135. 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 Download paper
136. 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
137. 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 Download paper
138. 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
139. 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
140. 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 Download paper
141. 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 Download paper
142. 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
143. 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 Download paper
144. 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 Download paper
145. 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
146. 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
147. Europe Regional Meeting ... E. Gil
148. 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 Download paper
149. 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
150. 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 Download paper
151. 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
152. 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 Download paper
153. 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
154. 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 Download paper
155. 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 Download paper
156. 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
157. 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 Download paper
158. 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
159. 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 Download paper
160. 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 Download paper
161. 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 Download paper
162. 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 Download paper
163. 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 Download paper
164. 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 Download paper
165. Field Crop Robots - Adoption and Farm Level Economics ... M. Gandorfer
166. 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 Download paper
167. 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 Download paper
168. 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
169. 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 Download paper
170. 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 Download paper
171. 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 Download paper
172. 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 Download paper
173. 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
174. 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 Download paper
175. 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 Download paper
176. 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
177. 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
178. 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 Download paper
179. 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 Download paper
180. 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 Download paper
181. 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
182. 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 Download paper
183. How Does an Autonomous Tractor See the World ... G. Bansal
184. 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 Download paper
185. I Call Shotgun: Uncovering Human-System/Robot Gaps in Emerging Technologies ... Y. Salzer
186. 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
187. 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 Download paper
188. 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 Download paper
189. 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 Download paper
190. 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 Download paper
191. 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 Download paper
192. 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 Download paper
193. 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 Download paper
194. 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 Download paper
195. 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 Download paper
196. 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 Download paper
197. 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
198. 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 Download paper
199. 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 Download paper
200. 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
201. 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 Download paper
202. 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 Download paper
203. 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 Download paper
204. 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 Download paper
205. Latin America and the Caribbean Regional Meeting ... R.A. Ortega
206. 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 Download paper
207. 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 Download paper
208. 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 Download paper
209. 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
210. 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
211. 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 Download paper
212. 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 Download paper
213. Machine Vision, AI, and Robotics in Specialty Crop Production ... M. Karkee
214. 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
215. 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 Download paper
216. 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 Download paper
217. 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
218. 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 Download paper
219. National Agricultural Producers Data Cooperative - Sponsor Presentation ... B.E. Craker
220. 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
221. North Dakota State University - Sponsor Presentation ... L. Schumacher, P. Flores, R. Sun, A. Reinholz
222. 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, Download paper
223. 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 Download paper
224. 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
225. 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
226. Oklahoma State University Department of Plant and Soil Sciences - Sponsor Presentation ... R. Sharry
227. 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
228. 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 Download paper
229. 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
230. 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 Download paper
231. 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
232. 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
233. 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 Download paper
234. 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 Download paper
235. 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
236. 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 Download paper
237. 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
238. 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 Download paper
239. 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 Download paper
240. 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 Download paper
241. 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 Download paper
242. 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 Download paper
243. 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 Download paper
244. 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
245. 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
246. 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 Download paper
247. 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 Download paper
248. 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 Download paper
249. 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
250. 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 Download paper
251. 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
252. Precision Agriculture in Latin America Community Meeting ...
253. 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 Download paper
254. 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
255. 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
256. 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 Download paper
257. 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 Download paper
258. 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 Download paper
259. 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 Download paper
260. 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 Download paper
261. 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 Download paper
262. 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
263. 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 Download paper
264. 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
265. Premier Strategy Consulting - Sponsor Presentation ... C. Zhu
266. 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 Download paper
267. 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 Download paper
268. 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 Download paper
269. 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 Download paper
270. 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
271. 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
272. 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 Download paper
273. 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
274. 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
275. 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
276. 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 Download paper
277. 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 Download paper
278. 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 Download paper
279. 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 ... Download paper
280. 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
281. 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 Download paper
282. 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
283. 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
284. 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 Download paper
285. 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
286. 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 Download paper
287. 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 Download paper
288. 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 Download paper
289. 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 Download paper
290. 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
291. 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
292. 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
293. 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
294. 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 Download paper
295. 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
296. 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 Download paper
297. 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 Download paper
298. 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
299. 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 Download paper
300. 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 Download paper
301. 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 Download paper
302. 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 Download paper
303. 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 Download paper
304. 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 Download paper
305. 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 Download paper
306. 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
307. 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
308. 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 Download paper
309. 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 Download paper
310. Stakeholder Inclusion for Responsible Robotics: Who, How, and Why? ... D. Rose
311. 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 Download paper
312. 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 Download paper
313. 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 Download paper
314. 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 Download paper
315. SurePoint Ag Systems - Sponsor Presentation ... B. Downing
316. Swarm Farming is the Future ... C. Rupp
317. Symposium Welcome and Introductions ... J. Lowenberg-deboer
318. 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
319. TEG Automation Solutions - Sponsor Presentation ... V. Oliveira
320. 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 Download paper
321. 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 Download paper
322. 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
323. The Ohio State University - Sponsor Presentation ... J.P. Fulton
324. 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 Download paper
325. 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
326. 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 Download paper
327. 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
328. Transforming Row Crop Agriculture: Harnessing Computer Vision and AI for Automation and Autonomy ... A. Sharda
329. 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 Download paper
330. 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 Download paper
331. 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 Download paper
332. 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 Download paper
333. University of Georgia's Institute for Integrative Precision Agriculture - Sponsor Presentation ... R.P. Ramasamy
334. University of Nebraska-Lincoln - Sponsor Presentation ... J.D. Luck
335. 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 Download paper
336. 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 Download paper
337. Using AI to Estimate Vineyards and Vegetables Vigour and Yield ... S. Fountas
338. 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
339. 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 Download paper
340. 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 Download paper
341. 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 Download paper
342. 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
343. 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 Download paper
344. 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
345. 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 Download paper
346. 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 Download paper
347. 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 Download paper
348. 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 Download paper
349. 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 Download paper
350. 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 Download paper
351. 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 Download paper
352. 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 Download paper
353. Veris Technologies - Sponsor Presentation Veris Technologies, Inc. designs, builds, and markets sensors and software for precision agriculture. ... T. Lund
354. 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 Download paper
355. 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 Download paper
356. 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
357. 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 Download paper
358. 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
359. 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 Download paper
360. 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
361. 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 Download paper
362. 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 Download paper
363. 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 Download paper

Filter results363 paper(s) found.

1. 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

2. 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

3. 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

4. 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

5. 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