Proceedings :: International Society of Precision Agriculture

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Alemandri, V

Antônio, M

Amaral, E

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Authors

Suarez, F

Gómez Montenegro, B

Dottori, C

Alemandri, V

de Breuil, S

Bruno, C

García Seleme, F

Arnosti, M.C

Felipe dos Santos, A

Costa Barboza, T

Souza Pinto, L.S

Amaral, E

Lacerda da Silveira, G

Valdes Fernandez , G

Antônio, M

Koch, G

Moura, P.A

Silva, F

Santana, C.C

Topics

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Oral

Year

2026

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Filter results3 paper(s) found.
1. A Methodological Framework for Modeling Plant Virus Occurrence Using Biometeorological Data: Insights from Multi-crop Case Studies in Argentina Viral diseases represent a major threat to the productive stability of agricultural systems. Their spatial and temporal occurrence is influenced by environmental conditions that regulate interactions among viruses, vectors, and hosts, making disease anticipation difficult using statistical traditional approaches. This situation highlights the need to understand the dynamics of the different biological components capable of affecting agricultural systems, and design and apply tools that facilitate... F. Suarez, B. Gómez montenegro, C. Dottori, V. Alemandri, S. De breuil, C. Bruno, F. García seleme Download paper
2. 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 Download paper
3. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal Imagery Accurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluated... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana Download paper

Filter results3 paper(s) found.

1. A Methodological Framework for Modeling Plant Virus Occurrence Using Biometeorological Data: Insights from Multi-crop Case Studies in Argentina

Viral diseases represent a major threat to the productive stability of agricultural systems. Their spatial and temporal occurrence is influenced by environmental conditions that regulate interactions among viruses, vectors, and hosts, making disease anticipation difficult using statistical traditional approaches. This situation highlights the need to understand the dynamics of the different biological components capable of affecting agricultural systems, and design and apply tools that facilitate... F. Suarez, B. Gómez montenegro, C. Dottori, V. Alemandri, S. De breuil, C. Bruno, F. García seleme

Download paper

2. 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

Download paper

3. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal Imagery

Accurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluated... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana

Download paper