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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, C
Chang, F
Chang, J
Chang, Y
Chen, C
Chen, H
Chen, J
Chen, N
Chen, S
Chen, W
Chen, X
Chen-Chang, L
Cheng, C
Cherng, A
Chien, L
Chikowo, R
Cho, J
Cho, W
Choton, J
Chou, C
Chou, J
Choudhury, S.D
Chu, C
Chu, W
Chu, Y
Chuang, C
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
Dai, Z
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
Ding, C
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
GONZALES, B
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
Haung, C
Hawkins, E
Hazzoumi, Z
Hefley, T
Hegedűs, G
Hehar, G
Heil, K
Henrie, A
Henties, T
Hernandez, C
Herrmann, I
Hessel, R
Hidaka, K
Hillyer, C.C
Hintz, G.D
Hinze, J
Ho, Y
Hodeghatta, U.R
Hoffmann Silva Karp, F
Holland, K.H
Holmes, A
Holthaus, D
Hong, C
Hoogenboom, G
Hopkins, B
Horbe, T
Horváth, B
Hostert, P
Hovio, H
Hsieh, S
Hu, J
Huang, C
Huang, Y
Huang, Z
Huender, L
Hufnagel, E
Hunhoff, L
Hunt, L
Igwe, K.E
Imaoka, K
Ingram, B
Inácio, F.D
Islam, M
JANBAZIALAMDARI, S
Jagadish, K
Jakhar, A
Jakimow, B
Jamaludin, M
Jamei, M
Jang, Y
Janjua, U.U
Janz, A
Javed, B
Jha, G
Jha, S
Jhala, A
Ji, X
Jiang, J
Jiménez Castaño, V
Joalland, S
Johnson, D.M
Johnson, J
Jones, N
Joo Kim, H
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
Kanjanaphachoat, 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, D
Kim, H
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
Kung, Y
Kunwar, S
Kuo, Y
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, K
Li, L
Li, M
Li, X
Li, Y
Liao, P
Lichtenberg,, S
Liew, C
Lin, H
Lin, T
Lin, W
Lindsey, L
Lingua, L.N
Linker, R
Liu, A
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
Matese, A
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
Nakanishi, T
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
Noh, H
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
Okayasu, T
Oldoni, H
Oliveira, L
Oliveira, M.F
Oliveira, R
Oliveira, V
Oliveira, W.K
Ome Narvaez, J.D
Ono, S
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
Park, J
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
Ryu, C
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
Santosa, A
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
Sean, W
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
Shibusawa, S
Shih, 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, M
Singh, R
Skovsen, S
Sleichter, R
Smith, B.K
Smith, T
Snider, J
Sobjak, R
Soderstrom, M
Sogbedji, J.M
Song, S
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
Sutrisna Wijaya, I
Swenson, A
Swinton, S.M
Syed, H.H
Sysskind, M
Sysskind, M.N
Sánchez Tomás, J
Sánchez Virosta,
Sørensen, C.G
T.Meyer, S
Tabbassi, A
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
Toscano, P
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
Werner, A
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
Wu, C
Wu-Yang, S
Wölbert, E
Xu, J
Xu, S
Xu, X
Xu, Z
Yadav, P.K
Yang, C
Yang, M
Yang, X
Yang, Z
Yasutake, D
Yen, P
Yoder, J
Yore, A
You, Z
Yu, K
Yu, Z
Yun, C
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
Zude-Sasse, M
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
2025
Home » Year » Results

Year

Filter results527 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 3D Camera-based Fertilizer Residue Monitoring System with Isobus for Precision Agriculture

Accurate monitoring of fertilizer usage is critical for effective variable-rate fertilization (VRF), contributing to optimized nutrient management and environmental sustainability. VRF systems typically use open-loop control based on prescription maps and calibration data. However, this approach can introduce discrepancies between targeted and actual fertilizer application rates due to variations in fertilizer characteristics and environmental conditions. This study proposes a fertilizer hopp... C. Yun

5. 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

6. 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

7. 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

8. 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

9. 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

10. A Dilution-free Capacitive Sensing Platform for Rapid Detection of Honey Adulteration

Honey adulteration has become increasingly prevalent, and consumers cannot easily verify authenticity without relying on specialized testing laboratories. Such approaches are time consuming and labor intensive, creating barriers to routine quality assurance. To streamline authenticity assessment, this study introduces a capacitive sensor as an alternative to conventional electrochemical impedance spectroscopy. The sensor directly interrogates undiluted honey and adulterated samples, eliminati... Y. Kung

11. A Field Machine for Automated Quantification of Sweet Potato Phenotypic Traits

Sweet potato is a globally important food crop, and its breeding is essential for enhancing nutritional value, ensuring food security, and promoting sustainable agriculture. However, the current process of parental selection largely depends on manual visual assessment, which is time-consuming and subject to human bias, thereby limiting both the efficiency and accuracy of breeding programs. In this work, a field machine for automated quantification of sweet potato phenotypic traits was propose... S. Hsieh

12. 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

13. 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

14. 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

15. 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

16. A Low-cost Multi-view Image to 3d Reconstruction for Plant Phenotyping

Current 3D plant phenotyping approaches often rely on LiDAR or multi-camera systems, which are costly, require complex calibration, and lack scalability. This study introduces a simple and cost-effective pipeline for 3D plant reconstruction using Hunyuan3D-2.5, a multi-view generative model. Plant samples were photographed directly using a mobile phone, and raw images were processed with a custom Python background-removal pipeline that enhanced plant contours and removed environmental noise. ... C. Huang

17. 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

18. 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

19. A New Paradigm of Datadriven Agrifood Systems

“Data-driven agrifood systems” is issued as a new standard terminology of smart farming from the international organization for standardization (ISO), and it has also focused on the needs of small/medium enterprises of farming. Data management scheme has changed the context of decision making on received style of good agricultural practices. Farmers and stakeholders should re-watch the system changes with emerging technologies. Farm management sustainable and community-based shoul... S. Shibusawa

20. A Physics-informed Neural Network Approach for Simulating Laminar Flow

Efficient and accurate modeling in agricultural fields is critical for advancing precision agriculture. These simulations, often involving the prediction of airflow, temperature, and humidity distributions, directly support decisions related to crop management, greenhouse climate control, and irrigation strategies. Computational Fluid Dynamics (CFD) has been a primary tool for decades, offering reliable and high-fidelity simulations through established numerical methods such as the finite-dif... C. Huang

21. A Rapid Non-invasive Capacitive Platform for in Vitro Assessment of Insecticide- Induced Skin Corrosion

This study introduces a rapid, non-invasive, and highly sensitive method for evaluating skin corrosion. The platform combines a capacitive sensor with a screen-printed electrode coated in a skin-mimetic layer, allowing real-time monitoring of capacitance changes in surrogate skin before and after exposure to corrosive agents such as agricultural insecticides. The biomimetic coating, formulated from hexane, ethanol, and lanolin, reproduces the lipid composition of the human stratum corneum. Ac... Y. Kung

22. A Review on Structural Enhancements and Domain-Specific Adaptation of YOLO for Crop-Weed Recognition

This review systematically summarizes YOLO-based weed detection models, focusing on two key directions: attention mechanisms that improve discrimination between visually similar vegetation and lightweight techniques that ensure real-time performance on limited hardware. A comparative analysis of improved YOLO variants highlights how structural optimizations improve detection, offering insights into efficient model design. ... J. Park

23. A Simulation-based Matching System for Utilizing Clean Energy from Agri-livestock Waste

In order to mitigate greenhouse gas emissions and air pollution derived from agricultural and livestock waste and to enhance the resilience of the clean energy supply chain, a simulation-based matching system for utilizing clean energy from agri-livestock waste as developed. Building upon a prior research entitled " An Inventory of Greenhouse Gases and a Map of Biomass Energy Utilization in Agriculture and Animal Husbandry Biomass Waste," the system is designed to evaluate the effic... J. Jiang

24. A Vision-guided Gantry Robot for Efficient Orchid Basket Reorganization in Greenhouses

Proper alignment of orchid baskets in greenhouses is important to maintain visual uniformity, maximize space usage, and ensure consistent light exposure during flowering. Manual arrangement is time-consuming and labor-intensive, underscoring the need for automation. To address this challenge, we propose an integrated robotic system for automated basket organization. The system combines a cartesian gantry robot, a rotation-aware clamping gripper, dual Intel RealSense D435i cameras, and a light... W. Lin

25. 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

26. 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

27. Adoption of Precision Agriculture in Japan

Japan is a country facing global challenges in terms of a declining and aging agricultural population, making the establishment of a sustainable production system a matter of urgency from the perspective of food security. While respecting Japan's traditional knowledge, the author believes that precision agriculture is an effective solution to resolve this situation. We argue that data-driven agriculture presents a higher degree of affinity with Japanese farmers, providing a more viable pa... E. Morimoto

28. 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

29. 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

30. 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

31. 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

32. 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

33. Africa Regional Meeting

... K.A. Frimpong

34. African Association for Precision Agriculture Community Meeting

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

35. 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

36. 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

37. 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

38. 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

39. 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

40. AI for Genomic Agriculture — from Sequence to Field Impact

Genomics offers powerful opportunities to enhance crop yield, resilience, and nutritional value, yet the complexity and scale of genomic, transcriptomic, and epigenomic data pose significant challenges for interpretation and application. Artificial intelligence (AI), particularly machine learning and deep learning, provides powerful approaches to decode this complexity and accelerate precision agriculture. I will present AI-based methods developed in my laboratory for annotating pla... C. Chen

41. AI for Genomic Agriculture — from Sequence to Field Impact

Genomics offers powerful opportunities to enhance crop yield, resilience, and nutritional value, yet the complexity and scale of genomic, transcriptomic, and epigenomic data pose significant challenges for interpretation and application. Artificial intelligence (AI), particularly machine learning and deep learning, provides powerful approaches to decode this complexity and accelerate precision agriculture. I will present AI-based methods developed in my laboratory for annotating pla... C. Chen

42. 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

43. 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

44. 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

45. 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

46. AI-driven Evapotranspiration Prediction Using Plant Wearable Sensor for Smart Irrigation

The global expansion of greenhouse cultivation has created a need for reliable crop evapotranspiration (ET) estimation to enable precise irrigation, thereby improving yields, enhancing crop quality, and addressing challenges related to water scarcity and environmental sustainability. This study proposes the development of a plant signal-based artificial intelligence (AI) model for ET prediction, tailored to the unique environmental conditions of greenhouse systems. Unlike empirical models tha... W. Cho

47. 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

48. 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

49. 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

50. 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

51. 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

52. 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

53. An Intelligent Blade Balancing Control System for Steep-terrain Tea Cutting Applications

Tea is a famous and valuable beverage in Taiwan. Tea is mainly grown in steep or mountainous areas. The terrain is a challenge for harvesting automation. Manual labor in harvesting tea in complex terrain is time-consuming and affects the economic efficiency of the product. This study proposes a tea-cutting blade balancing control system integrating image processing and fuzzy logic control. A unique mechanism is developed to adapt to the slope of the terrain. The limiting angle is 12 degrees r... W. Lin

54. An Intelligent Poultry Health Monitoring System Based on Multimodal Sensing Technologies

Traditional poultry farming primarily relies on manual observation to assess the chicken flock health status. However, this approach is not only time-consuming and labor-intensive but also highly dependent on individual experience, making real-time monitoring difficult to achieve. With the advanced Internet of Things (IoT) and Artificial Intelligence (AI) technologies, the animal industry is gradually transitioning toward intelligent farming. At present, most sensors focus primarily on enviro... Y. Shih

55. 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

56. Analysis Of Internal Abnormalities Of Tilapia Flesh Using Hyperspectral Imaging And Machine Learning Method

Tilapia, the most produced aquaculture species in Taiwan, has experienced significant production loss due to internal abnormalities, notably streptococcosis, which remains undetectable until fillets are cut. The absence of visible external symptoms frequently leads to quality reduction and economic loss. To address this, hyperspectral imaging, capable of capturing subtle spatial and spectral differences, was employed. The objective of this study was divided into two phases: firstly, identific... S. Chen

57. 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

58. 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

59. 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

60. Apple Weight Prediction Based on Lifecycle Growth Information

It is essential for determining optimal harvest timing, the grade of quality, and fresh maintenance, all of which directly impact growers’ economic returns, to accurately predict individual apple fruit weight. This study aims to predict the fruit weight of Fuji apples at the main branch level (n = 126) using growth data collected throughout the growing season. Fuji apples were monitored at 23 orchards in 2022 and 2023, and at 24 orchards in 2024. Growth data were col... C. Ryu

61. 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

62. 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

63. Application of Automation Technology in Mushroom Stem-cutting: a Case Study from Xinshe, Taiwan

The Xinshe region is one of Taiwan’s major mushroom production hubs, renowned for its high annual yield and exceptional quality. However, despite the growing demand in both domestic and international markets, the region faces a lack of comprehensive automated processing equipment, limiting productivity and exacerbating labor shortages. To address these challenges, this study applies advanced automation technology to develop a mushroom stem-cutting machine aimed at enhancing production e... K. Li

64. Application of Deep Learning for Symptom Detection and Localization in Phalaenopsis Plantlets

Phalaenopsis plantlets in dense greenhouses are vulnerable to diseases like soft rot, which spreads rapidly. This study compares YOLOv11 with enhanced architectures (FasterNet, MambaVision) for symptom detection and localization. Single- and multi-model strategies were evaluated for disease recognition, plant segmentation, and keypoint localization, enabling robotic removal and efficient automated disease management. ... Y. Huang

65. Application of Image Processing and Artificial Inteligence (AI) for Cabbage Cultivation Monitoring

Cabbage requires precise monitoring for during cultivation, e.g., transplanting performance, water stress, growth status, and yield estimation. This study presents image processing and artificial intelligence (AI) techniques to enhance automation for cabbage production operations. High-resolution multispectral and thermal images were acquired using UAVs and ground-based platforms. Seedling detection during transplanting operation was implemented using a YOLOv8 model with a CSPDarknet53 backbo... S. Chung

66. Applying Retrieval-augmented-generation to Support Farmers in Pest and Disease Diagnosis

According to the Ministry of Agriculture, crop production in Taiwan reached a value of $275 billion NTD in 2023, highlighting the economic importance of agriculture. However, the industry is now facing serious challenges, particularly in pest and disease identification and crop protection. Due to global ecological challenges, the manifestations of local pests and diseases have changed, making it difficult for farmers to rely on past experiences to identify and manage them effectively. Farmers... Y. Kuo

67. 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

68. Asia and Oceania Regional Meeting

... S.K. Balasundram

69. 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

70. 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

71. 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

72. 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

73. 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

74. 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

75. 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

76. 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

77. 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

78. Assessment of Light Interception Considering Plant Architecture is Important for Yield Prediction in Strawberry

Crop yield depends on whole‒plant photosynthesis, which is limited by the light interception by each leaf and its individual photosynthetic capacity. To date, there are some researches on assessments of yield considering their plant architecture and photosynthetic capacities in tomato and cucumber. However, there are few in strawberry although its cultivars exhibit considerable variation in their plant architecture and photosynthetic capacity. This research gap could significantly hinder ac... D. Yasutake

79. 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

80. Automated Analysis of Dairy Cow Bedding Behavior Patterns Based on Deep Learning Technology

Stalls are among the most frequently used facilities for dairy cows. Their design and use directly affect the cows’ health, comfort, and willingness to lie down. These factors are closely associated with both animal welfare and milk production. Therefore, monitoring stall use provides a practical basis for evaluating stall design and barn environment quality, in line with the Five Freedoms of Animal Welfare. This study proposes an automated system based on deep learning to analyze stall... C. Chang

81. 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

82. Automated Identification of Tomato Diseases, Pests, and Disorders Using Ai Models and Smartphone Applications

Tomato is one of the most important economic crops in many countries, with a substantial global production volume. However, tomato growth is often affected by diseases, pests, and physiological disorders (DPD), which typically manifest as symptoms on leaves, such as specks, yellowing, necrosis, or leaf deformation. These issues significantly reduce tomato yield and quality. Therefore, accurately identifying these symptoms and implementing corresponding management strategies have become crucia... Y. Kuo

83. 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

84. 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

85. Automated Quality Determination of Broccoli and Cauliflower Using Deep Learning

Broccoli and cauliflower have a narrow harvesting window, making accurate quality assessment essential for determining optimal harvest timing. This study developed specific grading models to automatically determine the quality of broccoli and cauliflower by three phenotypic indicators: color, shape, and maturity, using deep learning methods. About 600 top-view field images of broccoli and cauliflower were collected under natural conditions, and all annotations were cross-checked and verified ... S. Chen

86. Automated Selection of Taiwan Native Breeding Chickens Using Machine Vision and Deep Learning

Chicken is a primary global source of protein. In Taiwan, the poultry sector is a cornerstone of the domestic food supply. A significant part of this sector is the Taiwan Native Chicken (TNC), a collection of indigenous breeds prized for their unique flavor and cultural value, generating over 26 billion New Taiwan Dollars in 2023. Maintaining the quality of TNC relies on the effective selection of superior breeders. Conventionally, this selection is performed through manual inspection of phen... Y. Kuo

87. 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

88. 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

89. Automatic Air-exchange Whole-plant Carbon Sequestration Measurement System in Potted Native Taiwanese Plant Applications

As the climate crisis accelerates, reliable data on the carbon‐sequestration capacity of native plants are needed to guide nature-based mitigation strategies. Accurate, whole-plant assessments of carbon sequestration are crucial for identifying native species that can efficiently reduce atmospheric carbon dioxide, yet most plant physiology studies rely on costly instruments limited to leaf-level measurements. We therefore designed a low-cost, closed-chamber system that automatically refresh... L. Chien

90. 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

91. Automatic Calibration of Crop Growth Models for Predicting Corn Economic Optimum Nitrogen Rates

The objectives of this study were to 1) evaluate an automatic model calibration strategy; and 2) compare the performance of DSSAT and APEX models for simulation of maize (Zea mays L.) growth, plant nitrogen (N) uptake, yield in response to different N application rates and the estimation of the economic optimum N rate (EONR). Detailed data collected from eight site- years of N experiments conducted from 2014 to 2016 in Minnesota and Wisconsin, USA were used in this research. The results indic... Y. Miao

92. Automatic Counting of Chickens Around Feeders Using Convolutional Neural Networks

In 2023, Taiwan’s chicken industry generated about NTD 93.6 billion, or 43.5% of the livestock production value, underscoring its central role in the sector. Nonetheless, monitoring flock health and housing remains labor-intensive, and adjustments to feeding regimes frequently depend on subjective judgment, limiting standardization and scalability. Because feeding behavior is a key indicator of health and welfare, we present a vision-based system that continuously detects feeders and co... Y. Kuo

93. 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

94. 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

95. 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

96. 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

97. 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

98. Biosynthesis of Silver Nanoparticles from Platostoma Palustre for Agricultural Applications

Nanoparticle synthesis using natural resources offers a cost-effective and eco-friendly strategy. In this study, silver nanoparticles (AgNPs) were synthesized using Platostoma palustre extract (PPE), rich in polysaccharides and bioactive compounds. Characterization by XRD, SEM, and TEM confirmed successful synthesis. TEM revealed oval-shaped nanoparticles (7-80 nm) with PPE forming a stabilizing layer to prevent agglomeration, while XRD indicated a crystallite size of approximately ... W. Lin

99. 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

100. 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

101. Cabbage Yield Estimation Using Multispectral UAV Imagery and Deep Neural Segmentation

Accurate and efficient yield estimation is essential of optimizing crop management, resource allocation, and harvest planning in precision agriculture. Traditional manual methods are time-consuming, labor-intensive, and often lack spatial accuracy. Recent advances in remote sensing and deep learning offer scalable, non-destructive alternatives for yield monitoring. This study proposed a cabbage yield estimation based on an enhanced unity networking (U-Net) segmentation model utilizing multisp... S. Chung

102. Can AI and Automation Transform Specialty Crop Production?

... Y. Ampatzidis

103. 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

104. 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

105. Cfd Evaluation of Uvc Air-cleaning Integration in Greenhouse Hvac Systems

Greenhouse crops in Taiwan are highly vulnerable to airborne pathogens due to the humid climate and poor ventilation. This study evaluated the integration of UVC air- cleaning devices with the greenhouse HVAC system to reduce pathogen concentrations. A SolidWorks model of the NTU smart greenhouse was constructed, and CFD simulations were conducted to compare three configurations in which four UVC units were placed at the upper, middle, and lower regions of the wet pad. Results showed that the... C. Huang

106. 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

107. 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

108. 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

109. Close-range Remote Sensing Data for Optimizing Horticultural Production Processes

Plant sensors have been explored over the last three decades, resulting in various non-destructive sensor systems, feasible for usage along the entire horticultural supply chain. This review will show examples of sensor applications, pointing out benefits and challenges of different measuring principles. Particular emphasis is given on recent developments on analyzing plants directly in the field, aiming precise, data-driven production measures. ... M. Zude-sasse

110. Coarse-to-fine Navigation for Robotic Feed Delivery in Precision Livestock Farming

This paper introduces an autonomous mobile robot designed for automated feed replenishment at multiple designated stations. The robot employs a hierarchical navigation strategy, enabling both wide-area coarse positioning and precise localization at target feeding stations. For global navigation, the robot fuses data from a pre-defined map grid, odometry, and an electronic compass. This multi-sensor data is integrated using a least squares method (LSM) to ensure robust coarse position estimati... C. Chang

111. Color Identification and Texture Features of Phalaenopsis Using Deep Learning

As one of the most economically important and widely traded ornamental plants worldwide, Phalaenopsis hold a significant position in the global floriculture industry. The breeding process is traditionally labor-intensive, requiring careful visual assessment of numerous floral traits to select desirable varieties, which underscores the need for scalable, automated solutions. To enhance the efficiency of Phalaenopsis breeding and accelerate phenotypic comparison across varie... Y. Kuo

112. 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

113. 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

114. 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

115. 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

116. 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

117. 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

118. 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

119. 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

120. 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

121. 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

122. 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

123. 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

124. 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

125. 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

126. 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

127. 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

128. 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

129. Current Status and Potential of Digital Agriculture in India

Indian agriculture is facing multiple challenges, including climate change, resource depletion, low productivity growth, high post-harvest losses etc. To address these, a strong push toward digital and smart farming is underway. The Government of India has launched major initiatives such as the Digital Agriculture Mission, Digital India Programme, and targeted funding for AI, Machine Learning, and cyber security to support agricultural innovation. The focus is on building Digital Pu... M. Singh

130. 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

131. 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

132. 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

133. 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

134. 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

135. Deep Learning Based Image Recognition for the Detection of Natural Behaviors in Laying Hens

Eggs are an important source of protein, widely favored and needed by the public. The production and quality of eggs are closely related to the rearing environment of laying hens. Common rearing methods include conventional cages, enriched cages, floor systems, and free-range systems. Different housing environments may influence the production efficiency of hens. In recent years, increasing attention has been given to balancing animal welfare with production efficiency. Animal welfare is clos... C. Huang

136. 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

137. Deep Learning Prediction of Methane Production in Mesophilic and Thermophilic Anaerobic Digestion

Anaerobic digestion (AD) converts organic waste into methane-rich biogas but forecasting methane yield is difficult due to nonlinear dynamics. This study compares Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), and Temporal Fusion Transformer (TFT) models for predicting methane production rate (MPR, L/L/d) under mesophilic (37°C) and thermophilic (55°C) conditions. Lab-scale reactor data with features including hydraulic r... C. Chou, A. Liu

138. 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

139. Deep Learning-based Detection and Quantity of Livestock Carcasses

In recent years, livestock farming in Taiwan has gradually expanded. However, due to geographical constraints and regulatory restrictions, local farms are not permitted to bury or burn culled livestock carcasses on their own. As a result, farms must commission rendering trucks to collect and transport the carcasses to centralized rendering plants for processing. At the rendering plants, the manager are required to recount the number of carcasses. However, the large quantity and irregular arra... C. Chuang

140. Deep Learning-based Insect Detection on Sticky Traps Captured Via Mobile Phones Under Field Lighting Conditions

Insect pests pose a major threat to agricultural production, requiring effective integrated pest management (IPM) strategies that depend on accurate identification and counting of pests captured on sticky traps. However, mobile phone images taken under natural field lighting often suffer from inconsistent illumination, shadow interference, and low visibility of small insect targets, which significantly reduce the reliability of automated monitoring systems. To address these challenges, this s... T. Lin

141. Deep Learning-Based Vocal Signature Analysis for Rumination Belching Identification

Animal vocalizations provide important information about various behaviors, including courtship, parental care, foraging, and vigilance, as well as physiological states such as estrus, parturition, stress, and disease. In ruminants, eructation-related sounds are primarily produced when methane, generated by microbial fermentation of plant fibers in the rumen, is released orally. The acoustic characteristics of these sounds—such as frequency, intensity, and duration—are closely ass... C. Wu

142. Deep Reinforcement Learning Based Robotic Arm Control for Autonomous Harvesting

Inverse Kinematics (IK) is a traditional method used for robotic arm manipulation, relying heavily on precise calibration and huge computational demands for arms with higher Degrees of Freedom (DoF). In contrast, Deep Reinforcement Learning (DRL) is an innovative approach to manipulation that exhibits greater tolerance for calibration inaccuracies. It trains using noise added to joint angles, allowing it to learn how to compute accurate trajectories even with inaccuracies in the joint angles.... C. Huang

143. 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

144. 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

145. 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

146. 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

147. 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

148. 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

149. Design and Development of UECS-based Environmental Monitoring and Control Platform Without Coding

Data-driven agriculture has been increasingly adopted to achieve labor-saving, energy efficiency, and resource optimization in agricultural operations. Among small- and medium-scale horticul- tures, the Ubiquitous Environment Control System (UECS) proposed in 2004 is attracting again due to low cost of introduction. The UECS is an autonomous and distributed open-source en- vironmental monitoring and control platform for greenhouse horticulture. A computer called a node is used in each environ... T. Okayasu

150. Design and Performance Analysis of a Flexible Lig Thin-film Heater

Gas sensors play vital roles in environmental, agricultural, and industrial monitoring, yet metal-oxide sensors require high operating temperatures. Conventional ceramic or metallic heaters consume high power and lack flexibility, limiting portable applications. Here, flexible thin-film heaters were fabricated using laser-induced graphene (LIG) on polyimide substrates. Two patterns with varied laser powers and scanning speeds were tested, and electrical and thermal performances evaluated. Hig... X. Ji

151. Design of a Collision Avoidance Algorithm for Autonomous Tractors with Implements

Over the past decade, autonomous tractors have emerged as a key technology in agricultural automation. Global Navigation Satellite System (GNSS)-based navigation is widely used in autonomous tractors. However, since the GNSS cannot perceive the surroundings, an additional perception system is required to ensure the safety of the operation. Paddy ridges, one of the major obstacles in paddy fields, are typically higher than farmland to facilitate water storage. These height differences can lead... H. Kim

152. Design of a Garlic Seeding Monitoring and Mapping System Using GNSS and Vision Sensors

Seeding monitoring serves as the first step in precision agriculture, playing a crucial role in collecting and managing data across the entire agricultural process. While several international companies have recently developed precision agriculture solutions that monitor seeding rate, missing rate, and more, the agricultural environment in Korea presents unique challenges. For instance, in the case of Korean garlic planters, an average missing rate of approximately 10% is observed. When these... H. Kim

153. 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

154. 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

155. 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

156. Detecting and Removing Defective Carcasses of Taiwanese Native Chickens Using Convolutional Neural Networks

Poultry is one of the most important sources of meat worldwide. In 2023, the production value of poultry in Taiwan reached 59.8 billion NTD, accounting for 27.8% of the economic value of the animal husbandry industry. Among various chicken breeds, Taiwanese native chickens (TNC) are highly favored by consumers for their meat quality and flavor. As the demand for chicken increases, providing high quality meat to the market has become crucial. Unlike broilers, Taiwanese native chickens have div... Y. Kuo

157. 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

158. 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

159. 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

160. 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

161. 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

162. 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

163. 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

164. 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

165. 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

166. 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

167. Development of 3D Phenotypic Analysis Technology for Precision Monitoring of Strawberries

Strawberries exhibit high overall production volume but low productivity per unit area, primarily due to diseases that occur during cultivation. These yield losses can be mitigated through precision monitoring technologies based on phenotypic analysis. To enhance monitoring accuracy, 3D phenotyping techniques are essential. This study aims to automate such 3D phenotyping by constructing a 3D segmentation model capable of identifying plant organs. Strawberry plants were imaged from all angles ... M. Yang

168. Development of a Drone-mounted Device for Aerial Application of Mating Disruption Agents in Agriculture

In recent years, drones have been increasingly adopted to reduce workforce and improve the efficiency of pest control in agriculture. However, most drones are optimized for spraying low-viscosity liquid pesticides and thus have limitations in stably applying high-viscosity liquid or solid formulations. In particular, the mating disruption agent (MDA) used in this study, which contains pheromones, must be attached to the crown to maximize pheromone diffusion. It is necessary to develop a techn... S. Song

169. 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

170. Development of a Lorawan Wireless Node for Monitoring Smart Greenhouses

The adoption of Internet of Things (IoT) technologies in the smart greenhouse domain is rapidly advancing. Greenhouse planting improves quality and yield by controlling factors affecting crop production. Temperature, humidity, and light intensity in greenhouses are important factors affecting crops. Monitoring and regulating these parameters is conducive to improving the quality and yield of crops. Traditional greenhouse monitoring systems that use wired connections often have problems with c... S. Chung

171. Development of a Low-power Wireless Communication System Using Lora for Structural Monitoring in Greenhouse Foundations

Plastic greenhouses dominate protected cultivation in South Korea but are vulnerable to extreme weather and foundation instability. To address this issue, a low-power, low-cost monitoring system was developed to estimate foundation attitude and detect anomalies such as uplift. The system integrates an IMU (Inertial Measurement Unit)-based sensor node, LoRa (Long Range) communication, and a gateway in a star topology. Field tests, including pipe uplift and natural conditions, confirmed compara... J. Park

172. Development of a Measurement and Analysis System for Tillage Operations in Paddy Fields

This study developed a foundational technology for real-time tillage depth measurement using Inertial Measurement Units (IMUs). The ultimate goal is to enable variable-rate tillage operations tailored to spatial variations in topsoil depth. The system consisted of an RTK-GNSS module and two IMUs to measure the respective pitch angles of the tractor and implement. Tillage depth was estimated using a model derived from the geometric relationship between the implement’s pitch angle and its... E. Morimoto

173. Development of a Mobile Inspection Robot for Stacked-cage Layers Houses in Taiwan

In stacked-cage layers houses, it is essential to know the eggs produced in each cage per day and their distributions for evaluating egg-laying performance and the health status of the layers. A two-wheel-drive mobile inspection robot for egg-counting was thus designed, assembled, and on-site experiments were performed and evaluated in this paper. The path of the mobile robot was pre-designated according to the site floor layout, so the robot can move autonomously aisle by aisle. Multiple cam... A. Cherng

174. 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

175. Development of a Small-scale Weeding Robot for Inter-plant Areas Using Vision and Rake Mechanism

In low-herbicide or herbicide-free farming systems such as those used for medicinal and herbal crops, weed management remains one of the most labor-intensive tasks. Intra-row weeds, which grow between closely spaced crop plants, are particularly difficult to remove using traditional mechanical methods. Manual weeding, although effective, still poses a significant labor burden and limits the scalability despite the high market value of the crops. To address this challenge, we have de... K. Imaoka

176. Development of a Small-Scale Weeding Robot for Inter-Plant Areas Using Vision and Rake Mechanism

In low-herbicide or herbicide-free farming systems such as those used for medicinal and herbal crops, weed management remains one of the most labor-intensive tasks. Intra-row weeds, which grow between closely spaced crop plants, are particularly difficult to remove using traditional mechanical methods. Manual weeding, although effective, still poses a significant labor burden and limits the scalability despite the high market value of the crops. To address this challenge, we have de... K. Imaoka

177. Development of a Smart Agriculture Platform for Modern Management of Longan Orchards

Smart agriculture has emerged as a critical approach in modern agricultural systems. This study aimed to develop a smart agriculture platform for longan orchards by integrating Internet of Things (IoT) technologies and digital systems for precision farming. The study population comprised 100 large-scale agricultural producers located in the provinces of Chiang Mai and Lamphun, Thailand. The developed platform incorporated six core technologies: IoT-based smart irrigation, weather monitoring, ... C. Kanjanaphachoat

178. Development of a Smart Low-Carbon Greenhouse Integrated Plasma- Activated Water and Second-Life EV Batteries

Global warming and the excessive use of nitrogen fertilizers pose significant challenges to sustainable agriculture. This study presents a smart low-carbon greenhouse system powered by a solar photovoltaic microgrid, integrated with second-life electric vehicle batteries and a real-time energy management system. Plasma-activated water (PAW) technology is employed to reduce dependence on chemical nitrogen fertilizers while enhancing crop productivity and reducing nitrous oxide emissions. The s... W. Sean

179. Development of Ai-based Energy Management Strategy in Seawater Desalination Plant Based on Physical Modeling

Global water scarcity is becoming increasingly severe, and seawater reverse osmosis (SWRO) has become a major technology for freshwater production due to its high efficiency. However, membrane fouling during long-term operation increases transmembrane pressure, reduces flux, and raises energy demand, ultimately lowering efficiency and shortening membrane lifetime. Traditional control and prediction methods struggle with the nonlinear and dynamic nature of these processes. To address this, we ... W. Sean

180. Development of an Autonomous Navigation and Obstacle Avoidance Robot for Poultry Sheds

Traditional poultry sheds in Taiwan are mostly open-structured, resulting in low efficiency for manual inspection and egg collection, and increased risks of labor fatigue and injuries. This study develops an autonomous obstacle avoidance system for unmanned agricultural vehicles tailored to poultry shed environments, focusing on dynamic path planning and obstacle evasion for safe, efficient navigation.A tracked vehicle chassis enhances stability and adaptability on soft litter floors and narr... J. Chang

181. Development of an Electric-assisted Handling System for Pig Farm

In swine farming, manure management is a critical yet labor-intensive task. With increasing agricultural labor shortages, optimizing farm infrastructure to reduce manual workload has become essential. Many pig farms in Taiwan utilize elevated slatted floors (concrete or cast iron) to separate pigs from their waste, allowing excrement to fall through gaps for later disposal. While this design improves hygiene by reducing direct contact, the heavy and bulky slatted panels pose significant chall... W. Chen

182. Development of an In-line Sc-ise Sensor System for Closed Hydroponic Nutrient Monitoring

Nutrient monitoring is crucial in closed hydroponic systems, where accurate control over individual ion concentrations directly influences crop yield and fertilizer efficiency. While electrical conductivity (EC) sensors are commonly used, they only measure total ionic strength and cannot distinguish between specific ions. Liquid-contact ion-selective electrodes (LC-ISEs) have been studied as an alternative but tend to suffer from durability issues and signal instability under the high-flow co... Y. Jang, W. Cho

183. Development of an Integrated Harvesting Machine for Taro Fields

Taiwan cultivates a diverse range of agricultural products, among which taro (Colocasia esculenta) is an important root vegetable. Although several harvesters exist for root crops, their applicability remains limited due to crop-specific requirements, and no dedicated integrated harvesting machine is currently available for taro in Taiwan. Farmers still rely heavily on manual labor, using knives or spades to loosen the soil around taro plants before uprooting them individually—a time-co... W. Chen

184. Development of Automated Rose Monitoring System with Deep Learning-based Growth Stage Classification

In cut-flower cultivation, effective production planning is essential to accommodate seasonal fluctuations in market demand. Precise rose growth stage monitoring is critical for cultivation schedule, environmental control, and harvest timing, yet current practices rely on manual observations, which are time-consuming and prone to subjectivity, limiting consistency and scalability. This study presents an automated monitoring system integrating computer vision and deep learning for ob... S. Chen

185. Development of Cultivar-optimized Nir Spectroscopy Model for Cherry Tomato Maturity and Sweetness Assessment

"Yunu" cherry tomato cultivars hold substantial commercial value in Taiwan’s premium markets, where sweetness serves as a key quality attribute. To enhance cultivar-specific quality assessment, this study evaluates tomato quality in both pre-harvest and post-harvest stages.In the pre-harvest stage, image data were used to establish a Red Ripeness Index (RRI) for evaluating tomato maturity. Color calibration techniques were applied to improve consistency, and the stability and ... S. Chen

186. Development of Efficient Co2 Enrichment Technique Based on a Simple Photosynthesis Model of Strawberries

In Japanese strawberry production, environmental control in greenhouses is carried out to increase yields and improve fruit quality. CO2 enrichment technique, which promotes leaf photosynthesis by supplying CO2 gas generated by burning kerosene inside greenhouses, has become an indispensable technique in strawberry cultivation. However, conventional CO2 enrichment involves continuous supplementation over a long period of time regardless of the photosynthetic response of strawbe... K. Hidaka

187. Development of Light-Normalized Crop Monitoring Framework Using RGB-D Imaging and Spatial Light Regression

To achieve high-quality, high-yield crop production, non-destructive precision monitoring technologies combined with image-based artificial intelligence are being studied to establish finely controlled cultivation environments tailored to crop growth stages. However, variations in lighting-one of the most critical cultivation factors-can cause significant fluctuations in crop image data, limiting the accuracy of phenotype extraction. This study aims to develop a light-normalized crop monitori... M. Yang

188. Development of Methane Monitoring System for Dairy Cow Eructation

Methane emissions from dairy cow eructation constituted a significant greenhouse gas source in livestock production and were closely linked to rumination activity. Accurate, continuous, and non-invasive monitoring of eructation events was proven essential for assessing animal health, optimizing feed strategies, and reducing environmental impact. Conventional approaches— including manual observation, jaw-movement sensors, and respiration chambers—remained costly, labor-intensive, a... C. Wu

189. Development of Rgb and Lidar Fusion Based Pear Fruit Quantification and Mapping System

This study presents a system for accurate fruit quantification using LiDAR-RGB sensor fusion. The system projects 2D fruit detections from a YOLO model onto a 3D map generated via SLAM, assigning a unique coordinate to each fruit to prevent double-counting. This approach achieved an aggregate accuracy of 98.5%, with a predicted total of 535 fruits compared to the 527 observed. The resulting data revealed significant fruit density variations (3.2 to 12.6 fruits/m²), establishing the syste... E. Morimoto

190. 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

191. Development of Temperature and Humidity Sensor Calibration Procedure for Multifunctional Orchid Greenhouse Monitoring System

Bacterial soft rot and bacterial brown spot are primary diseases that threaten orchid cultivation, often resulting in substantial economic losses. To address labor shortages and environmental challenges in recent years, the orchid industry is increasingly adopting intelligent disease management systems that combine sensing technologies and data analytics as part of its transformation strategy. The multifunctional monitoring system was developed as an economical, integrating sensors for temper... C. Haung

192. Development of Vision-guided Autonomous Robot for Phenotypic Monitoring in Tomato Breeding

Phenotypic monitoring in crop breeding requires continuous data collection throughout growth cycles, yet traditional manual methods are both labor-intensive and time-consuming. Individual plant tracking over extended periods poses particular challenges due to field scale and measurement frequency requirements across diverse agricultural environments. This study presents an autonomous robotic platform integrating computer vision and precision positioning technologies for automated phenotypic d... S. Chen

193. Development of Weeding Robots Using Ai Image Recognition Technology

In this study, a laser weeding robot was developed to remove weeds, one of the factors that interfere with the growth of crops in agricultural fields. The driving unit controlled the speed of the DC motor using an Arduino-Mega (MCU). The weed recognition unit calculated the location information of the weeds using the cabbage and weed image recognition model (YOLO-v8) mounted on the control device (Jetson-Orin). The calculated location information was transmitted to the MCU through serial comm... H. Kim

194. Development, Design, and Integration of an Egg Tray System with Unmanned Ground Vehicle for Robotic Poultry Automation

Eggs are among the most extensively consumed foods, valued for their nutritional and health benefits. While generally studied, the exact fine representation of a raspberry’s egg shape remains complex. Egg forms are usually classified as globular, ellipsoidal, elliptical, and pyriform- the last of which still lacks a definitive equation. This study presents a new system for modeling egg figures and calculating volume, based on crucial parameters, including the major axis, the m... B. Gonzales

195. Develpoment of Bagged Guava Quality Grading System Using Image Recognition and Generative Adversarial Networks(GANS)

Taiwan’s warm climate and abundant sunlight make it highly suitable for guava cultivation, making guava an important economic crop. However, current quality grading still relies on manual inspection, which is labor-intensive, inconsistent, and affected by bagging practices. To address this, we propose an automatic grading system using deep learning and generative adversarial networks. The framework collects images of bagged and bare guavas, applies YOLOv9 for fruit detection and backgro... C. Chang

196. 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

197. 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

198. Disease Symptom Recognition and Severity Assessment for Phalaenopsis Orchids

Traditional disease assessment relies on manual visual inspection, which is subjective and often leads to inconsistent results due to variations in human judgment. To address these challenges, this study proposes an automated approach for disease classification and severity grading in Phalaenopsis orchids using the YOLOv8-seg deep learning model. The system integrates instance segmentation with Lab color space analysis, which was found to outperform HSV in distinguishing healthy and diseased ... C. Huang

199. 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

200. 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

201. 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

202. Dual-channel Imaging and Two-stage Deep Learning for Fertility Detection of Duck Eggs

In Taiwan, the waterfowl industry generates a production value of NT$11.2 billion, of which meat ducks contribute about 80% (≈NT$8.9 billion). As the upstream segment of the duck meat industry, the hatching process of duck eggs plays a critical role in duck production. Fertilized eggs require a clean incubation environment to develop properly. To protect this environment, unfertilized eggs need to be removed at an early stage, which makes fertility detection essential. However, conventi... Y. Kuo

203. 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

204. Early Warning System Based on Deep Learning for Multi-type Abnormal Chicken Comb Detection

With the rapid development of smart agriculture, environmental sensing technologies have been widely applied to enhance production efficiency and management in poultry farming. However, existing poultry house systems mainly focus on monitoring environmental parameters such as temperature, humidity, and gas concentrations, offering limited capabilities for real-time assessment of individual chicken health. Currently, flock health largely relies on manual inspections by farm personnel, which ar... Y. Chu

205. 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

206. Edge-AI-based Dairy Calf Behavior Monitoring System Using Computer Vision and Iot Technologies

We present an edge-AI, IoT system for real-time monitoring of dairy calf behavior that runs on embedded system and streams only compact results to the cloud. A lightweight, quantized MoViNet-A2 model deployed on a Raspberry Pi 4 classifies seven behaviors (non-active/active lying, non-active/active standing, feeding, drinking, ruminating) from 4-s clips captured once per minute, and publishes JSON outputs to AWS for dashboards. Field trials on three Holstein calves at the National Taiwan Univ... T. Lin

207. 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

208. 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

209. 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

210. 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

211. 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

212. Embodied Agentic Artificial Intelligence for Precision Agriculture: Cross-domain Experience from Multimodal Generative AI

My team develops inclusive, responsible, and multimodal AI technology across education, healthcare, and digital services grounded in our research in embodied agentic intelligence and large language models. I will share deployed examples from these domains and draw parallels to agriculture, where similar technical challenges persist, ranging from multimodal fusion for contextual reasoning, explainable AI for actionable insights, and data-efficient learning for adaptation and localization. Whil... N. Chen

213. 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

214. 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

215. Energy Harvesting and Application of a Tubular Triboelectric Nanogenerator Driven by Water Flow

In recent years, growing awareness of sustainable and environmentally friendly practices has driven the development of innovative renewable energy technologies. To mitigate the environmental impact of energy production, this study proposed a tubular Triboelectric Nanogenerator (TENG) capable of continuously and stably harvesting energy from flowing water. The designed tubular TENG consisted of two structural components: the inner part served as the primary power generation unit, comprising a ... P. Liao

216. 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

217. 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

218. 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

219. 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

220. 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

221. 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

222. Enhancing Rice Disease Management: Estimating Pathogen Damage Through Multispectral Imaging Analysis

This study investigates the application of multispectral imaging (MSI) in conjunction with machine learning algorithms for the early detection and estimation of pathogen damage in rice crops, with a specific focus on Bacterial Leaf Blight (BLB) and Blast diseases. Rice plays a crucial role in global food security, yet these diseases significantly compromise its production. Traditional diagnostic methods are often labor-intensive and time-consuming, necessitating the adoption of innovative tec... I. Sutrisna wijaya

223. 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

224. Enhancing Sustainable Farming of Nh: Mechanization of Planting and Post Harvest Cleaning

Nymphoides hydrophylla (NH), commonly known as white water snowflake, is a culturally and nutritionally important aquatic vegetable, particularly valued in Taiwan's Hakka communities. However, its commercial scalability remains limited due to labor-intensive practices in both planting and post-harvest cleaning. This study introduces an integrated mechanized system that combines a seedling planting tool and a cleaning machine, designed to enhance overall production efficiency, reduce ... W. Lin

225. 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

226. Establishment of Spatial Information for Soybean Cultivation Complex Through Drone Image Analysis

This study demonstrates that time-series drone imagery can effectively monitor crop growth in large-scale soybean paddy complexes. Additionally, spatial data were constructed for each field, including geographic coordinates, parcel numbers, area, crop type, sowing date, and growth information. ... J. Park

227. 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

228. Estimating Rice Canopy Height Using a Ground-based Slam Lidar System

This study evaluates the application of a ground-based LiDAR system, integrated with a Simultaneous Localization and Mapping (SLAM) algorithm, to estimate rice crop canopy height (CH). Using the Velodyne VLP-16 LiDAR sensor, point cloud data were collected and processed to map the rice field. The experimental area covered approximately 600 m² during the crop’s vegetative stage. LiDAR-derived canopy height (LCH) was extracted using percentile-based metrics and compared with manual m... E. Morimoto

229. 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

230. 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

231. Estimation of Crop Coefficient in Malaysian Durian Using Satellite Data and Machine Learning

Durian (Durio zibethinus) is a popular fruit and key crop in Southeast Asia, known as the “King of Fruits” for its thorny exterior and distinctive aroma. The crop coefficient (Kc), based on crop evapotranspiration (ETc) and reference evapotranspiration (ETo), is crucial for water efficiency. Currently, there is no Kc value for Malaysian durian. This study introduces a machine learning method utilizing remote sensing data from Sentinel-1, Sentinel-3, and MODIS ET, combined wit... S.K. Balasundram

232. Europe Regional Meeting

... E. Gil

233. 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

234. 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

235. Evaluating Flight Path Strategy for Uav-based Phenotyping of Individual Muskmelon Plant in Greenhouse Environments

Unmanned Aerial Vehicle (UAV)-based phenotyping is an emerging non-invasive method for high-throughput trait measurement in controlled environments. This study examines how UAV flight trajectory affects reconstruction fidelity and trait accuracy for muskmelon and grape plants in a GPS-denied greenhouse. Two strategies - circular loop and vertical hop - were flown using a UAV with RGB-D SLAM navigation, capturing data with a RunCam Thumb Pro. Data were processed through a GLOMAP structure-from... T. Lin

236. 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

237. 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

238. 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

239. 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

240. 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

241. 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

242. Evaluation of High-throughput 3d Reconstruction Method for Plants and Its Application to Traits Feature Extraction

2D images are widely utilized to monitor and evaluate plant growth, capturing the dynamic and multi-directional nature of plant canopies remains difficult, emphasizing the need for 3D monitoring integrated with plant phenotyping systems.This study aims to introduce a high-throughput plant phenotyping system using 3D plant shape model reconstructed from a dataset of 2D plant images from multiple camera poses. A robot autonomously gathered data by recording video footage of plants from various ... T. Okayasu

243. 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

244. Evaluation of Planting Accuracy and Early Growth Uniformity of Spring Cabbage in Greenhouses

Mechanized transplanting reduces labor and time in greenhouse cabbage production, yet misplacement, over burial, and missing seedlings still compromise uniform stands This study evaluated transplant quality and early growth uniformity with two stages during transplanting and harvesting image and machine learning workflow at plot scale. Two transplanters, automatic and semi-automatic, were tested under ridge widths of 60, 70 and 80 cm and seedling ages of 30 and 35 days. In February after tran... S. Chung

245. 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

246. 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

247. 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

248. 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

249. 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

250. 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

251. Fabrication of Laser-induced Graphene Heater Integrated into Flexible Printed Circuit Boards

Flexible Printed Circuit Board (FPCB) are widely used in portable devices, wearable systems, and biomedical sensing applications because their flexibility, thinness, and high integration. With the development of the Internet of Things and smart agriculture, sensors are evolving toward lightweight and multifunctional designs, including applications in temperature and humidity monitoring, pressure sensing, physiological signal acquisition, and gas detection. Most gas sensors require high-temper... C. Wang

252. 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

253. 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

254. Field Crop Robots - Adoption and Farm Level Economics

... M. Gandorfer

255. 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

256. Field Testing of a Laboratory-made Portable Hydroponic Nutrient Analyzer with Ion-selective Electrodes

As a strategy to address climate change and declining agricultural productivity, hydroponic systems have gained increasing attention. In particular, precise control of nutrient ion composition in nutrient solutions is essential for ensuring stable crop growth and improving product quality. However, most hydroponic farms currently rely on pH and electrical conductivity (EC) sensors for nutrient solution management. While EC reflects the overall ionic strength, it does not provide quantitative ... H. Kim

257. 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

258. 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

259. 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

260. 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

261. 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

262. 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

263. Fusing Deep Learning and Control Theory for Optimized Sugar Beet Yield Prediction

Accurate yield prediction is a vital field of research in precision agriculture, enabling optimal resource allocation and enhanced food security under growing climatic uncertainty. Traditional models struggle to capture complex, non-linear interactions between environmental drivers and crop growth. To address this, we present our approach, a multi-stage method for sugar beet yield prediction and management that integrates deep learning with control-theoretic techniques and mathematical langua... A. Tabbassi

264. 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

265. 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

266. 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

267. 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

268. 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

269. 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

270. 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

271. 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

272. 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

273. High-reliability Navigation for Multi-functional Robots Using Rfid Triggers and 3d Slam in a Protected Horticulture

Protected horticulture in Japan is facing a serious labor shortage, yet existing robots have not achieved sufficient return on investment, and their adoption remains limited. To support the deployment of multi-functional robots, we developed a high-reliability autonomous navigation system that integrates RFID-based event-triggered state transitions with LiDAR-based simultaneous localization and mapping (SLAM).The developed mobile platform was built on an omnidirectional robot equipped with fo... T. Okayasu

274. HMI-integrated Environmental Sensing for Poultry Water-intake Forecast

Environmental monitoring is crucial in poultry farming, yet traditional reliance on manual inspection is often labor-intensive and inefficient. This study addresses these challenges by developing a comprehensive poultry production management system. The research utilized environmental data—specifically temperature, humidity, carbon dioxide (CO₂), and ammonia (NH₃)—from a guinea fowl population at the Biaoyu Husbandry Farm in Miaoli County. The core of the system is a Human-Mac... H. Chen

275. 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

276. How Do Different Data Intervals in Exponential Sine Model Affect Prediction of Strawberry Flowering Dynamics?

In strawberry cultivation, where harvests occur periodically, predicting flowering dynamics is crucial for optimizing yield. This study aimed to organize fundamental information on the exponential sine model, which could play a central role in developing prediction processes for strawberry flowering dynamics. To achieve this, the model was applied to flowering data, and the impacts of different data intervals on predictive performance (trends and accuracy) were evaluate. Over three cultivatin... S. Ono

277. How Does an Autonomous Tractor See the World

... G. Bansal

278. 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

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

... Y. Salzer

280. Identification of Citrus Diseases, Pests, and Disorders Using Deep Learning

Taiwan’s warm climate offers favorable conditions for citrus production, making it the most economically valuable fruit crop in the country. Citrus trees are perennial and mainly propagated asexually. Long-term exposure and limited genetic diversity make them more susceptible to infection by various pathogens. In practice, diagnosis often relies on farmers’ experience, which can be subjective despite their familiarity with local conditions. Microscopic examination by plant patholo... Y. Kuo

281. Identification of Cucumber Pests, Diseases, and Disorders Using Deep Learning

Cucumber is an essential economic crop worldwide, which is typically cultivated in summer. The hot and humid conditions make them highly susceptible to various pests, diseases, and physiological disorders, which hinder their growth and lead to significant yield losses. Early and accurate detection is vital to limiting the spread of diseases or pests. However, traditional diagnostic approaches rely heavily on visual inspection by experienced farmers or microscopic examination by specialists, w... Y. Kuo

282. 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

283. Improving Depth Accuracy by Using a Real-time Monitoring System for Traditional Tillage Machinery

Tillage depth has a great influence on soil quality, fuel consumption, and equipment durability in mechanized farming. However, traditional methods often maintain a fixed depth, lacking the ability to adjust in real time. This study proposes a real-time monitoring system that significantly improves the depth measurement accuracy of traditional tillage machinery. The system is equipped with a soil contact wheel combined with an angle sensor, which converts the rotation angle into a depth value... W. Lin

284. 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

285. 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

286. 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

287. 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

288. 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

289. 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

290. 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

291. 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

292. 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

293. 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

294. 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

295. Innovating Irrigation: Affordable Smart Solutions for Water Sustainability

Agriculture accounts for 70–80% of global freshwater use, a level increasingly unsustainable under climate change. This study reports the development and field validation of a low-cost smart irrigation system for tomato and melon in Tuscany (2021–2023). The system integrates evapotranspiration-based models, wireless sensor networks, and adaptive control algorithms. In 2023 it achieved up to 50% water savings compared to traditional practices, without yield reduction, at a total co... A. Matese

296. 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

297. 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

298. Integration of a Real-time Dairy Cow Eye Temperature Monitoring System Based on Deep Learning and Thermal Imaging

Early detection of heat stress and illness in dairy cows is critical for maintaining herd health and optimizing milk production. Among various physiological signals, body temperature is a key indicator of health status. In this study, we present a real-time, non-contact monitoring system that integrates dual-channel thermal imaging and deep learning for precise and automated surveillance. The system processes RGB and thermal video streams in parallel: in the RGB channel, YOLO detects faces, B... T. Lin

299. 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

300. 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

301. 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

302. Intra-row Mechanical Cabbage Weeding Based on Machine Vision

Cabbage production is strongly influenced by environmental factors such as weather, soil, weeds, and pests, which can reduce both yield and quality. Chemical weeding is efficient and inexpensive but restricted due to environmental and food safety concerns, while manual weeding is safe yet labor-intensive. To address these issues, this study proposes a machine vision–based in-row weeding system that integrates a belt-driven sliding module with an embedded computing platform. Using the YO... H. Lin

303. Investigating the Behavior and Responses of Cage-free Laying Hens Using a Laser Disturbance System

Growing attention to animal welfare is accelerating the shift to cage-free housing, but floor eggs remain a persistent problem. Eggs laid on the ground are easily soiled, broken, and can transmit disease; they also raise labor and time costs because they must be collected quickly. Once floor laying becomes habitual, correction is difficult. We propose a laser-based disturbance system that uses non-invasive light cues to guide hens toward raised platforms and nest boxes. Deployed on an embedde... F. Chang

304. Investigation of Seed Monitoring Potential Using Light Dependent Resistor (Ldr) for Cell Type Precision Seeders

Precision seeding is an important operation in modern agriculture, ensuring accurate seed placement at defined rates and intervals to optimize crop performance. Despite their critical importance, conventional seed metering devices often require frequent manual calibration, making them labor-intensive, inefficient, and impractical for both smallholder and large-scale farming operations. Existing seed monitoring technologies are often costly and lack real-time adaptability to varying field cond... S. Chung

305. 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

306. Lameness Detection in Side-View Videos of Dairy Cows Based on Pose Estimation and Deep Learning

Lameness is a critical factor affecting milk production and remains a major concern in dairy farming. Conventional lameness detection relies on visual observation and veterinary judgment, which are subjective and labor-intensive. This study proposed a non-contact lameness detection system integrating pose estimation and machine learning. A YOLOv11-pose model was trained to detect cow keypoints, and features such as back curvature, head swing, and Back Posture Measurement (BPM) were extracted.... C. Chu

307. 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

308. Laser- Induced Enhancement of Seed Germination and Early Growth in Legumes

Laser technologies are emerging as promising tools in precision agriculture for enhancing plant development and productivity. This study investigates the effects of low- power laser irradiation (532 nm, 1W) on the seed germination and early growth of mung beans (Vigna radiata). Seeds were exposed to laser light prior to planting, and their germination performance, leaf expansion, chlorophyll content, and shoot length were measured and compared to untreated control seeds. The laser-treated see... C. Ding

309. Latin America and the Caribbean Regional Meeting

... R.A. Ortega

310. Lauraceae Timber Identification Using Vision Transformer

The forest coverage in Taiwan exceeds 60%, yet over 99% of annual timber consumption relies on imports. This significant dependence, coupled with frequent incidents of wood misidentification and fraud, highlights the need for accurate and efficient wood species identification systems. Conventional approaches, such as microscopic analysis and sensory- based macroscopic inspection, are labor-intensive, subjective, and require domain expertise, making them unsuitable for large-scale or real-time... Y. Kuo

311. 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

312. 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

313. 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

314. Low-code Development Environment and Middleware for Ubiquitous Environment Control Systems

This work presents a low-code development environment that enables non-engineers to construct a customized software for UECS devices automating horticultural facilities as well as a middleware that provides a uniform application executing environment on different platforms for the UECS software. ... T. Nakanishi

315. 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

316. 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

317. 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

318. Machine Learning Prediction Models for Dual-Horizon Egg Production Forecasting

Egg production forecasting presents significant challenges in agricultural supply chain management due to complex seasonal patterns, disease outbreaks, and market volatility. Although various forecasting models have been developed for agricultural production, limited research has systematically compared model performance across different temporal horizons or developed integrated frameworks optimized for diverse planning needs. This study develops a comparative dual-horizon machine learning fr... S. Chen

319. 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

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

... M. Karkee

321. 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

322. Measure, Model, Manage: the Unfinished Revolution in Agriculture

Over the last 40 years, the paradigm of Measure, Model, Manage has promised an agricultural revolution through data-informed precision management. This shift remains largely incomplete, lagging concurrent innovations in genetics and pesticides. Significant barriers persist in achieving breakthrough innovations for crop data collection and the development of data analysis/decision-making systems. These hurdles include a decades-old "Sensor Crisis" (a lack of appropriate too... A. Werner, A. Holmes

323. 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

324. Mobile-based Automated Phenotyping System for Accessible Tomato Breeding

Tomato breeding programs require extensive phenotypic data collection including fruit development stages and critical timing parameters, yet manual monitoring is labor- intensive and limits breeding program scalability, particularly in resource-limited environments. This study presents a cost-effective automated phenotyping system that requires only smartphone video recording combined with pre-assigned plot numbers, eliminating the need for expensive mobile platforms and making advanced breed... S. Chen

325. Modeling and Characterization of Unimodal and Bimodal Diurnal Pollen Foraging Patterns in Honeybee Colonies

Pollen foraging patterns in honeybee colonies provide essential information on their ecological adaptation strategies. This study proposes a statistical modeling framework to characterize diurnal pollen foraging patterns in honeybee colonies. To support this, data were collected from healthy honeybee colonies during controlled experimental period. The raw pollen harvest data were then segmented into daily time series and converted into hourly histograms to capture foraging rhythms more effect... T. Lin

326. Modeling the Effects of Greenhouse Environmental Factors on Soft Rot Incidence in Phalaenopsis

Phalaenopsis spp. is one of Taiwan’s most important ornamental crops for export. However, during greenhouse cultivation, Phalaenopsis is frequently threatened by bacterial soft rot (Erwinia spp.), particularly under high-temperature and high-humidity conditions that accelerate pathogen spread and cause severe losses in seedlings. This study was conducted in a Phalaenopsis greenhouse located in Houbi District, Tainan, Taiwan. The greenhouse contained 21 planting beds, which wer... C. Huang

327. 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

328. Monitoring Chicken Houses with AI Surveillance System

In Taiwan, the need of chicken meat accounts for approximately 30% of total livestockvproduction. In order to maintaining animal welfare, floor-rearing chicken farming approaches are widely used in Taiwan. However, traditional poultry management is often labor-intensive which increases the risk of disease transmission. To improve monitoring efficiency, we proposed a smart rail surveillance system to automatically monitor chickens for real-time chicken health assessment. The system comprised a... Y. Kuo

329. 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

330. 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

331. Multi-system Enhancement of Autonomous Field Vehicles for Crop Monitoring Applications

Autonomous field vehicles face operational challenges in agricultural environments, including terrain-induced instability, image quality degradation during motion, and limited operational endurance that compromise the reliability of data collection for precision agriculture applications. This study presents systematic improvements in three critical subsystems of autonomous vehicles for field-based crop monitoring: mobility optimization, visual stabilization, and power management. The study ad... S. Chen

332. Multivariate Linear Regression Modeling for Predicting Chicken Body Weight Using Age, Uniformity, and Growth Rate

Accurate estimation of chicken body weight is critical for optimizing feed management, harvesting schedules, and animal welfare in commercial poultry systems. This study proposes a robust predictive framework using multivariate linear regression to estimate the average weight of native broiler chickens based on three explanatory variables: age, uniformity, and daily growth rate. After rigorous data cleaning and outlier removal, the model was trained and validated on 43 field observations coll... H. Lin

333. National Agricultural Producers Data Cooperative - Sponsor Presentation

... B.E. Craker

334. Nighttime Piglet Detection Using Deep Learning

In 2023, Taiwan’s pig industry was valued at over NT$85.1 billion, representing nearly 40% of total livestock production. However, effective piglet management remains a challenge due to environmental variability, frequent aggressive behaviors, and labor shortages—especially during nighttime. Traditional monitoring methods rely on manual observation, which is time-consuming, subjective, and impractical for continuous surveillance. To address this, we propose an automated nighttime ... Y. Kuo

335. Non-destructive Tilapia Quality Determination Using Near-infrared Spectroscopy

Tilapia represents a significant economic asset in the aquaculture industry due to its high nutritional value and commercial importance. However, internal abnormalities are frequently detected during processing operations, particularly those caused by Streptococcosis, which is among the most prevalent diseases affecting tilapia quality. These quality defects often lead to commercial disputes between aquaculture farmers and fillet processors, highlighting the critical need for non-destructive ... S. Chen

336. 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

337. North Dakota State University - Sponsor Presentation

... L. Schumacher, P. Flores, R. Sun, A. Reinholz

338. Null Dataset-Based Detection Enhances Robotic Vision in Greenhouse Cherry Tomato Harvesting

Cluttered cherry tomato greenhouse environments with visually similar distractors often trigger False Positives (FPs) in robotic vision, misguiding the robot’s motion and reducing harvesting success. We introduce a null-dataset strategy that integrates unannotated distractor images into YOLOv8l training, with their proportion tuned through loop refinement to suppress FPs while preserving precision. Optimal null proportions were identified as 12.3% for tomato detection and 8.3% for pedic... P. Yen

339. 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,

340. 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

341. 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

342. 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

343. Oklahoma State University Department of Plant and Soil Sciences - Sponsor Presentation

... R. Sharry

344. 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

345. 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

346. 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

347. 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

348. 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

349. 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

350. 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

351. 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

352. 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

353. 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

354. 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

355. 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

356. Optimizing Frost Prediction with a Multi-Window CNN–XGBoost Soft-Voting Ensemble

Recent global climate change has increased the frequency of late-spring frost events, causing more severe and widespread damage to orchard growers. Frost formation occurs due to rapid temperature drops over short periods combined with overnight air stagnation; thus, effective prediction requires analyzing patterns across multiple time scales. We introduce a hybrid frost-forecasting framework that combines a multi-window 1-D convolutional neural network (CNN), utilizing 6-, 12-, and ... D. Kim

357. 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

358. Optimizing Power Delivery in Electric Farm Machinery Using a Hybrid Battery and Ultracapacitor System

Agriculture plays a significant role in global greenhouse gas emissions, contributing notably to climate change. Integrating renewable energy into agricultural operations has become increasingly vital in addressing this challenge. This study investigates the potential of electrifying agricultural machinery using a hybrid energy storage system that combines batteries and ultracapacitors. While batteries offer high energy density, they face limitations such as slow charging and reduced lifespan... S. Wu-yang

359. 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

360. 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

361. 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

362. 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

363. 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

364. 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

365. Performance Evaluation of Agricultural Spray Nozzle Under Different Pressure Conditions by Image Analysis

Spray nozzles are critical components in agricultural equipment used for pest control, pollination, and so on. The liquid ejected from the nozzle is broken down into droplets due to friction with the air and pressure changes. Consequently, the nozzle performance is often defined by alternative parameters to estimate the actual operating conditions. This study aims to determine the operating parameters of spray injection by photographing the movement of droplets ejected from a nozzle under dif... T. Okayasu

366. Performance Study of Triboelectric Nanogenerator with Laser-induced Graphene Electrodes

As wearable electronics increasingly demand a continuous power supply, conventional batteries—requiring frequent recharging or replacement—pose both user inconvenience and environmental risks. This study develops a wristwatch‐ shaped triboelectric nanogenerator that employs solid‐ state semiconductor laser‐ induced graphene electrodes patterned directly onto a polyimide (PI) film and utilizes an independent sliding interface to harvest 1 to 3 Hz low‐frequenc... C. Wu

367. Pest and Disease Image-text Identification System of Leafy Vegetables in Urban Community Farming

Urban community farming has been integrated into education for sustainable food and agriculture. However, the participants are primarily students and novice farmers with limited background knowledge. Managing pests and diseases becomes challenging for these growers as diverse vegetable crops attract various pest and disease species, requiring accurate identification and treatment expertise. There is a need to develop timely identification services and guidance on control measures. In the... S. Chen

368. 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

369. Phalaenopsis Seedling Assessment Using Leaf Contour Detection with YOLO

In this study, we propose a vision-based approach for automatically measuring the morphological traits of Phalaenopsis seedlings. By utilizing top-view and side-view images, our method automatically extracts leaf contours to replace traditional manual measurements. A YOLOv8n-seg model was employed to segment the seedlings, and further correction strategies were introduced to improve accuracy. Experimental results demonstrate the potential of our approach to support large-scale seedling classi... Y. Kuo

370. Plantsaga: Integrating Segment Anything Model with Gaussian Splatting for Plant Organ-level 3d Segmentation

Organ-level 3D phenotyping is essential for crop breeding but remains limited by the high cost of manual annotations. To address this challenge, PlantSAGA (Plant Segment Anything Gaussian Splatting) is introduced as a reference-based framework that enables accurate organ segmentation with minimal annotation. Multi-view muskmelon plants were reconstructed using COLMAP for camera pose estimation and Gaussian Splatting for 3D modeling, while 1~10 reference masks guided organ-level discrimination... T. Lin

371. Portable DNA Detection Tool for Halal Monitoring Using Spectral Sensing

Pork and its derivatives are non-halal in Islam, raising concerns about cross- contamination in food. With the growing number of Muslim tourists and Taiwan’s efforts to expand its halal F&B exports, strict halal compliance and reliable detection methods are essential. Conventional techniques like PCR offer high accuracy but are limited by long processing times and the need for advanced laboratories. Recombinase Polymerase Amplification (RPA) presents a faster alternative, operating ... J. Chen

372. 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

373. 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

374. 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

375. 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

376. Potential of Plant Phenotyping for Data-driven Greenhouse Horticulture

We are trying to investigate the use of various features extracted from plant images for the purpose of environmental control in greenhouses according to the growth conditions of plants. A measurement robot was utilized in order to collect plant images. Plant growth features (apical buds, flowers, fruits, etc.) were extracted by using a deep learning-based detector. In addition, we also introduced a 3D reconstruction technology to obtain the plant shape features such as plant height, internod... T. Okayasu

377. Power Consumption Signal Characterization of Bldc-based Agricultural Fans for Malfunction Detection for Smart Greenhouses

Effective management of environmental parameters, notably temperature and humidity, is critical for ensuring optimal plant growth and productivity in smart greenhouses. Brushless (BLDC) fans are commonly utilized for controlling greenhouse ventilation and humidity levels. The primary aim of this study was to characterize the power consumption of BLDC agricultural fans to identify operational anomalies and facilitate predictive maintenance strategies. An experimental setup was devised, involvi... S. Chung

378. Precise Strawberry Stem Localization Via Two-stage 3d Deep Learning

Harvesting delicate fruits, such as strawberries, at their optimal ripening stage inherently presents significant challenges, given its labor-intensive, time-consuming nature and high susceptibility to mechanical damage. The solution lies in developing intelligent robotic harvesting systems that can accurately segment fruits, determine optimal picking locations, and perform delicate maneuvers without compromising fruit integrity. This study presents a novel 3D vision-based methodology for aut... C. Chang

379. 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

380. Precision Agriculture in Latin America Community Meeting

...

381. 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

382. 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

383. 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

384. Precision Nutrient Management in the USA: Current Trends and Future Opportunities

Precision nutrient management (PNM) has become integral to modern U.S. agriculture, particularly in optimizing fertilizer use efficiency, reducing environmental impacts, and sustaining profitability. As detailed in recent analyses, the adoption of precision technologies for nutrient management in the U.S. is advanced, especially among large- scale operations in the Midwest Corn Belt. Key technologies facilitating PNM include variable rate technology (VRT), remote and proximal sensing, soil an... S. Phillips

385. 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

386. 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

387. 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

388. 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

389. 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

390. 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

391. 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

392. 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

393. 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

394. Prediction of Lettuce Spad Value During Growth by a Multi-Spectral Image Sensor Using Machine Learning Model

In this study, we aimed to improve previous LR (Linear regression) model for prediction of lettuce SPAD value, and used several machine learning (ML) models such as SVR (Support vector regression), KNN (K-nearest neighbors regression), KRR (Kernel ridge regression), DTR (Decision tree regression), RFR (Random forest regression), and ANN (Artificial neural network). K-means clustering algorithm was used to separate lettuce sample from background, and the reflectance from multi-spectral images ... H. Noh

395. Preliminary Tests for Potato Yield Monitoring Using a Controlled Test Bench

Accurate yield estimation is a critical aspect of precision agriculture, particularly for root crops such as potatoes, where direct measurement during harvest can be challenging and labor-intensive. Developing precise and automated methods to enhance the efficiency and accuracy of yield assessments is thus imperative. This study explores the potential of integrating vision-based imaging and non-contact sensing technologies to achieve accurate potato mass estimation under controlled laboratory... S. Chung

396. Premier Strategy Consulting - Sponsor Presentation

... C. Zhu

397. 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

398. 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

399. 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

400. 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

401. 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

402. 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

403. Quantitative Assessment of Discharge Depth Effects on Lithium-Based Batteries: LTO, LFP, and NCM

This study explores the impact of depth of discharge (DoD) on the performance degradation of three lithium-based battery chemistries: lithium titanate (LTO), lithium iron phosphate (LFP), and nickel cobalt manganese oxide (NCM). The objective is to establish a standardized methodology for evaluating battery health under partial cycling and to quantify the degradation behavior across three DoD ranges: 0–33%, 34–66%, and 67–100%. LFP and NCM cells were cycled at 1C under room ... C. Huang

404. 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

405. 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

406. 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

407. 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

408. 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

409. Reducing Ground Losses Using a Leaf Segmentation-based Autonomous Sprayer for Papaya Greenhouses

Papaya plants have irregular canopy structures, making traditional spraying methods highly labour-intensive and prone to chemical waste due to non-selective application. In precision agriculture, delivering pesticides accurately to target areas is crucial for reducing labour requirements, costs, and environmental impact. Therefore, the integration of smart agricultural machinery and machine vision is necessary to optimise pesticide application. In this study, a low-cost autonomous spraying sy... W. Lin

410. Regression Model for Estimating Branch Number of Soybean Using Uav-based Multispectral Images

Soybean (Glycine max (L.) Merr.) is a protein-rich crop, and the number of branches is a significant trait associated with yield. This study aims to estimate the branch number of soybeans using vegetation indices (VIs) extracted from multispectral images mounted on a UAV. The study was conducted on the soybean cultivar Seonpung, sown on June 20, 2022, and June 24, 2023. Vegetation growth was investigated on 20 control and 30 treatment samples on August 20 and September 20, 2022, August 21 and... C. Ryu

411. 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

412. 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

413. 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 ...

414. 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

415. 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

416. 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

417. 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

418. Revolutionizing Poultry Health: AI-Powered Real-Time Disease Detection Using YOLO v7 and IQR for Enhanced Farm Productivity

Prompt and accurate detection of poultry diseases is crucial to prevent outbreaks and reduce economic losses. Conventional monitoring systems based on manual inspections are inefficient and prone to error, delaying timely interventions. This study proposes an AI-driven early warning system that integrates YOLO v7 for real-time image detection with Hampel Filters for anomaly recognition. The model specifically targets two critical health indicators: rooster combs and eyes. Over a period of 53 ... A. Santosa

419. Rgb-based Soil Water Content Prediction Enhanced by Hyperspectral Calibration

While hyperspectral imaging (HSI) cameras demonstrate high accuracy for detecting soil water content (SWC)-related spectral variations, their field deployment remains constrained by prohibitive costs and operational complexity. This study investigates utilizing low-cost RGB cameras through HSI-guided calibration for SWC estimation. 210 paired HSI-RGB measurements were acquired across five soil texture classes (0-40% fine particles), fourteen moisture levels (0-39% SWC), and three illumination... J. Park

420. 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

421. Robotic Arm Tomato Harvesting System and Next Best View Algorithm Development

Replacing human labor with robots is a trend for future agriculture due to its efficiency and consistency. However, in automatic fruit harvesting tasks, leaf occlusion and the dynamic orientation of fruit make it difficult for robots to directly observe the picking point. To address this problem, this research focuses on tomato harvesting, and proposes a next-best-view (NBV) algorithm based on two main structures: “tomato pose prediction” and a “target-hit-gain function&rdqu... P. Yen

422. 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

423. 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

424. 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

425. 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

426. 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

427. 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

428. 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

429. 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

430. Signal Characterization for Actuator Operation Status Monitoring in Smart Vertical Farms

Vertical farming presents a sustainable solution for high-yield crop production in space- constrained environments by enabling precise control over environmental parameters. However, effective implementation depends not only on environmental monitoring but also on the reliable operation of actuators that regulate system condition. The objective of this study was to characterize power consumption signals from actuators within smart vertical farms to facilitate precise monitoring, assessment of... S. Chung

431. Signal Characterization of Environmental Sensors for Abnormality Detection in Hot Temperature Greenhouses

Maintaining optimal microclimatic conditions is critical for crop productivity in greenhouse cultivation. High-temperature environments can induce subtle but critical deviations in environmental parameters, often resulting in reduced crop growth, quality, and yield. This study aimed to characterize the raw signal behavior of environmental sensors to enable early detection of abnormal conditions in hot-temperature greenhouses. An internet of things (IoT)-based sensor network comprising tempera... S. Chung

432. Signal Characterization of Ict Components for Malfunction Detection for Open-field Irrigation Systems

Agricultural practices in open fields increasingly rely on automated irrigation technologies and ICT components, whose operational status impacts their reliability and efficiency. This study aimed to develop a malfunction detection pattern for sensors and actuators through signal characterization in an open-field irrigation setup. The experiment included environmental sensors and actuators, interfaced with a programmed microcontroller, operating in cycles (On/Off) or alternatively. Signals we... S. Chung

433. Signal Characterization of Sensors for Operational Status Monitoring in Smart Vertical Farms

Vertical farming represents an advanced agricultural practice capable of efficiently producing high-quality crops through precise environmental management, optimal spatial utilization, and consistent production outcomes. Ensuring reliable and accurate performance of environmental sensors is essential for sustaining ideal growth conditions within these advanced agricultural systems. This study aimed to characterize signals from environmental sensors to enhance real-time operational status moni... S. Chung

434. 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

435. Simulation and Control of Brushless DC Motors Based on Fuzzy PID for Unmanned Vehicles in Poultry Houses

The poultry industry holds a significant position in the development of Taiwan's agricultural economy, with commercial broiler and layer chicken farming constituting its primary sectors. The rampant spread of avian influenza has resulted in the mass mortality of broiler and layer chickens, leading to substantial economic losses.To mitigate the risk of avian influenza infection and reduce labor costs, this study investigates the application of a FUZZY PID controller for brushless DC motors... Z. You

436. 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

437. 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

438. 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

439. 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

440. Six-Axis Robotic Arm and Object 3D Detection Technique for Supporting Mobility-Limited People on Grasping Objects

This paper presents the integration of a six-axis semi-industrial robotic arm with a real-time 3D object detection system to enable intuitive, contactless human-robot interaction, with a particular focus on healthcare applications. The robotic arm, powered by high-precision stepper motors, delivers enhanced accuracy and reliability compared to traditional servo-based systems, making it ideal for tasks that demand precision and consistency. The system is designed for extensibility, suppor... J. Chou

441. Smartflow: Ai Optimization of Desalination for Sustainable Agricultural Water Management

Limited access to reliable freshwater sources is a persistent barrier to agricultural productivity, particularly in coastal and arid regions where rivers, lakes, and groundwater reserves are rapidly declining. Farmers in these areas often struggle to meet irrigation demands, resulting in reduced yields and heightened vulnerability to climate variability. Although seawater desalination provides a potential alternative, conventional reverse osmosis (RO) systems are typically too energy-intensiv... M. Jamaludin

442. Smartphone Application for Real-time Environment Monitoring of Smart Greenhouses

Smart greenhouse technologies significantly enhance agricultural productivity, sustainability, and resource efficiency, yet existing solutions often face limitations regarding affordability, real-time responsiveness, and scalability, especially for small- and medium-sized farms. This research introduces a cost-effective, scalable smartphone- based application designed for real-time monitoring and precise control of essential greenhouse environmental parameters, including temperature, relative... S. Chung

443. 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

444. 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

445. 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

446. 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

447. 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

448. 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

449. 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

450. 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

451. 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

452. 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

453. 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

454. 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

455. Stakeholder Inclusion for Responsible Robotics: Who, How, and Why?

... D. Rose

456. 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

457. 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

458. Study of Non-contact Respiration and Temperature-humidity Index for Dairy Cow Heat Stress Monitoring

This thesis develops a non-contact system to automatically monitor the respiratory rate of dairy cows, aiming to improve real-time health assessment and management in livestock farming. Respiratory rate is a key indicator of cow health, helping detect heat stress, respiratory illness, and other conditions early. The system uses visible light and thermal cameras to capture synchronized videos, while image recognition algorithms detect and track the nasal region to perform respiratory measureme... W. Chu

459. Study on Contect Sensor-based Ridge Tracking Technology for Precision Garlic Seeding

Ridges are an important part of field operations in agriculture. From soil tillage and sowing to harvesting, ridges serve as the foundation throughout the entire crop production cycle. However, in practical application, ridges are often irregular and poorly maintained. Irregular ridge can disrupt consistent seeding which can result in uneven crop growth and a decline in overall productivity. In the case of garlic, seeding uniformity is directly related to yield. Therefore, addressing the unev... H. Kim

460. 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

461. 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

462. SurePoint Ag Systems - Sponsor Presentation

... B. Downing

463. Swarm Farming is the Future

... C. Rupp

464. Sweet Potato Skin Color Analysis Algorithm Based on Image Preprocessing and Color Models

Sweet potato breeding typically requires eight to ten years, during which phenotypic trait measurement and cultivar selection demand substantial labor and time. The Chiayi Agricultural Experiment Station in Taiwan, which hosts the nation’s largest sweet potato germplasm collection, relies heavily on manual phenotypic trait evaluations throughout the breeding process. In particular, the assessment of skin color is highly susceptible to variations in lighting conditions and subjective jud... T. Lin

465. Symposium Welcome and Introductions

... J. Lowenberg-deboer

466. Synthetic Data-driven Validation of Multi-stage Fruit Detection Systems in Controlled Virtual Environments

Accurate fruit counting across development stage is critical for tomato breeding decisions. Yet, the ground truth validation in real field remains challenging where partially occluded fruits cannot be reliably counted manually due to complex environmental factors. To address this need, this study presents a photorealistic simulation approach that complements real field data collection. A virtual environment enables controlled evaluation across three distinct fruit growth stages: green stage f... S. Chen

467. 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

468. TEG Automation Solutions - Sponsor Presentation

... V. Oliveira

469. The Development of a Real-time Monitoring System Using IoT Sensor Technology

This study developed an IoT-based monitoring system for cold storage of agricultural products. Using temperature-humidity, CO₂, and ethylene sensors with Raspberry Pi, real-time data were collected and analyzed. Field tests showed stable monitoring of temperature (3~8 °C), humidity (77~92%), and CO₂ (450~1400 ppm), while no ethylene was detected. The system demonstrated reliable performance and potential to improve quality control and efficiency in post-harvest storage. ... H. Joo kim

470. The Effects of Thermal Aging and Ultraviolet Radiation Aging on the Performance of Greenhouse Plastic Films with Different Thicknesses

Due to Taiwan’s hot and humid climate, it is necessary to consider the haze, tensile strength, and aging resistance of greenhouse plastic films of different thicknesses to evaluate whether replacement is required. This study focuses on commonly used commercial plastic films in Taiwan’s agricultural facilities, mainly composed of linear low-density polyethylene (LLDPE), with thicknesses of 0.15 mm, 0.18 mm, and 0.20 mm. These greenhouse films were subjected to artificial accelerate... L. Chen-chang

471. 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

472. 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

473. 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

474. The Ohio State University - Sponsor Presentation

... J.P. Fulton

475. 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

476. 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

477. Theoretical Analysis of Deflection in Deformed Silicone Components for Dried Longan Peeling

In traditional manual processing of dried longan, the fruit is typically peeled by cutting from the stem end with a knife and tearing along the seed axis to separate the flesh. However, to enhance operational efficiency and realize production automation, the development of dried longan processing machinery with automatic peeling capabilities has become an inevitable trend in the industry. The most critical component of such machines is the peeling module, whose geometry and dimensions directl... C. Cheng

478. Theoretical Analysis of Deflection in Deformed Silicone Components for Dried Longan Peeling

In traditional manual processing of dried longan, the fruit is typically peeled by cutting from the stem end with a knife and tearing along the seed axis to separate the flesh. However, to enhance operational efficiency and realize production automation, the development of dried longan processing machinery with automatic peeling capabilities has become an inevitable trend in the industry. The most critical component of such machines is the peeling module, whose geometry and dimensions directl... C. Cheng

479. Theoretical Power Analysis of a Driving Unit for a Sweet Potato Harvester Under Development

Mechanized harvesting has become increasingly essential in modern agriculture to enhance productivity and reduce labor dependency, particularly for root crops like sweet potatoes, which traditionally involve intensive manual labor. This study presented a theoretical analysis of a driving mechanism for a sweet potato harvester under development. A theoretical analysis was conducted to evaluate the power requirements, torque distribution, and transmission efficiency of the mechanism. This analy... S. Chung

480. Thermoelectric Infrared Sensor Integrated with SHA Absorber

This paper details the design of a high-performance thermoelectric infrared (IR) sensor using the UMC 0.18 μm CMOS-MEMS process, targeting the 8–14 μm wavelength for applications like IoT. To enhance performance, the sensor integrates two key innovations: a Sub-Wavelength Hole Array (SHA) absorber and a novel double-layer thermopile structure with 64 pairs of thermocouples. Finite-Difference Time-Domain (FDTD) simulations show the SHA structure achieves an average IR absorptivity ... Z. Dai

481. 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

482. Towards in Situ Monitoring of Root Growth Traits: Combining Spectral Imaging with Transparent Bed Hydroponics

We developed a novel method that enables non-laboratory monitoring of the growth characteristics of crop root systems by combining spectral imaging with a transparent bed hydroponics. Root systems of spinach grown were observed through the transparent bottom plate using a hyperspectral camera daily. An optimal index for the classification of root ages (days after emergence) was determined as the ratio of reflectance at 498 and 601 nm. Additionally, the distribution of root age was visualized ... D. Yasutake

483. 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

484. Transforming Row Crop Agriculture: Harnessing Computer Vision and AI for Automation and Autonomy

... A. Sharda

485. 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

486. 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

487. 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

488. 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

489. University of Georgia's Institute for Integrative Precision Agriculture - Sponsor Presentation

... R.P. Ramasamy

490. University of Nebraska-Lincoln - Sponsor Presentation

... J.D. Luck

491. Unlocking Canopy Dynamics: Uav-lidar-based Biomass Estimation in Ocimum Basilicum

UAV-LiDAR offers a high-throughput route to phenotyping and biomass estimation in basil (Ocimum basilicum L.). Over three crops seasons (2021–2023), we evaluated three commercial varieties across 96 plots under different irrigation regimes and sowing densities. Multi-temporal LiDAR acquisitions quantified canopy height, LAI and volume and were validated against ground truth. Canopy volume strongly predicted fresh biomass (R² = 0.93; mean error < 8%). Across years, fresh bio... P. Toscano

492. Unsupervised Anomaly Detection of Tipburn in Leafy Vegetables Using Denoising Autoencoder

Tipburn, a common physiological disorder in leafy vegetables, presents as marginal necrosis but its fuzzy boundaries make annotation costly and inconsistent. We present a label-free pipeline that combines CIE Lab–based preprocessing with a chroma-only denoising autoencoder (DAE) trained solely on healthy samples for real-time, pixel-level anomaly mapping. Lettuce images were acquired under controlled lighting, segmented in CIE Lab space, and reduced to the a channel and a/b chromatic ra... M. Yang

493. Unsupervised Hyperspectral Image Segmentation Using Deep Global Clustering

Hyperspectral imaging (HSI) combines rich spectral and spatial information, supporting field monitoring and crop assessment in precision agriculture. HSI scenes from one dataset usually share the same background and foreground classes, yet spectra from one region differ from those in another. Pixels that describe the same object therefore cluster together in spectral space; mapping these clusters back onto the image yields pseudo-segmentations that can stand in for class labels. However, proc... S. Chen

494. 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

495. 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

496. Using AI to Estimate Vineyards and Vegetables Vigour and Yield

... S. Fountas

497. 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

498. Using Floral Bract Withering to Identify Green-ripe Pineapples with Deep Learning

Green-ripe pineapples are ideal for extended transportation and storage during summer but are challenging to identify during on-site harvesting. This study introduces a deep learning-based approach using the YOLO-NAS algorithm to detect green-ripe pineapples by analyzing the withering rate of floral bracts at the fruit's base. A high- mounted tracked vehicle, equipped with an Intel D405 depth camera, captures images at a distance of 300–400 mm as it navigates pineapple ridges. The s... S. Chen

499. 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

500. 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

501. 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

502. 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

503. 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

504. 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

505. 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

506. 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

507. 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

508. 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

509. 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

510. 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

511. UWB-IMU System Application and Analysis in Cucumber Greenhouses

Accurate and stable positioning is essential for autonomous navigation and environmental monitoring in greenhouse environments. Simultaneous Localization and Mapping (SLAM) is one of the methods to determine the location. This method generally requires a computer and measurement devices such as LiDAR and cameras, making it relatively costly and demanding in terms of deployment conditions. In contrast, ultra- wideband (UWB) is attracting attention as a high-resolution, short-range localization... W. Lin

512. 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

513. 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

514. Veris Technologies - Sponsor Presentation

Veris Technologies, Inc. designs, builds, and markets sensors and software for precision agriculture. ... T. Lund

515. Visual Attention and Clinical Scales in Patients with Dementia

Attention is a critical indicator in dementia assessment, and its cognitive fluctuation serves as a key metric for evaluating treatment effectiveness. With technological advancements, eye-tracking has emerged as a reliable and non-invasive tool for measuring attentional performance. This study employed the Gazepoint eye-tracker to assess visual attention in 16 patients diagnosed with mild dementia (Clinical Dementia Rating, CDR = 0.5). Two visual response tasks, digit discrimination, and lett... W. Chu

516. 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

517. 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

518. 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

519. 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

520. 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

521. 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

522. 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

523. 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

524. 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

525. 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

526. Yolo Strawberry Maturity Classification and Harvest Priority with 3d Camera

Accurate harvesting timing is essential to improve crop quality and productivity, and recent advances in agricultural automation have led to the emergence of fruit maturity classification and harvest optimization algorithms for agricultural robots as major technical challenges. This study proposes a pipeline for strawberry object detection, maturity classification, distance estimation, and harvest priority. We train a YOLOv8 detector on an open RGB dataset, and estimate the camera-fruit dista... M. Yang

527. Yolox-based Monitoring for Humane Poultry Slaughter

Using deep-learning and image-recognition techniques, we built a smart, safe, and humane poultry-slaughter system that raises production efficiency while safeguarding animal welfare. The system centres on a YOLOX object-detection network that classifies each Red-Feather chicken on the processing line as either stunning or unstunning in real time. A total of 1 683 manually labelled images were collected. Of these, 1 268 were reserved for model development and 419 for final testing. The develop... Y. Ho