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Arnosti, M.C
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Authors
Felipe dos Santos, A
Alvez, R.Q
Barboza, T.O
Arnosti, M.C
Valdez , G.F
Silveira, G.L
Valdes Fernandez , G
Lacerda da Silveira, G
Fernandes Queiroz Alves , R
Costa Barboza, T
Arnosti, M.C
Felipe dos Santos, A
da Silva, W.B
Pereira da Costa, O
Arnosti, M.C
Felipe dos Santos, A
Costa Barboza, T
Souza Pinto, L.S
Amaral, E
Lacerda da Silveira, G
Valdes Fernandez , G
Felipe dos Santos, A
Borges, R.D
Arnosti, M.C
Marcassa Lonzi de Oliveira, C
da Silva, W.B
Santos, A
Costa Barboza, T
Costa, O.P
Valdes Fernandez , G
Arnosti, M.C
Silveira, G.
Filho, R.
Souza Pinto, L.S
AZEVEDO, S.
Medeiros, M.
Felipe dos Santos, A
Costa Barboza, T
Arnosti, M.C
Valdes Fernandez , G
Lacerda da Silveira, G
Lacerda da Silveira, G
Arnosti, M.C
Valdes Fernandez , G
Costa Barboza, T
Felipe dos Santos, A
Amaral, E
Topics
Precision Crop Protection, Pest, and Plant Health
UAV-Based Scouting, Imaging, and Targeted Applications
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Agricultural Robotics, Automation, and Mechanization
Precision Agriculture for Sustainability and Environmental Protection
Type
Poster
Oral
Year
2026
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1. Comparative Analysis of YOLOv3–YOLOv12 Architectures for Automatic Oil Palm Detection in Agricultural Monitoring

Oil palm (Elaeis guineensis) is considered the most productive oilseed crop worldwide, and Brazil holds one of the greatest global potentials for palm oil production. Efficient monitoring of cultivated areas is therefore essential for proper crop management, enabling the detection of planting gaps, yield estimation, and decision-making support. In this context, computer vision techniques based on deep learning models, particularly those from the YOLO (You Only Look Once) family, have... M.C. Arnosti, A. Felipe dos santos, T. Costa barboza, L.S. Souza pinto, E. Amaral, G. Lacerda da silveira, G. Valdes fernandez