Proceedings :: International Society of Precision Agriculture

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Antônio, M

Amaral, E

Albuquerque, M

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Authors

Arnosti, M.C

Felipe dos Santos, A

Costa Barboza, T

Souza Pinto, L.S

Amaral, E

Lacerda da Silveira, G

Valdes Fernandez , G

Hermes, M

Albuquerque, M

Andreoli, A

, C

Chaves, C

Quicaña, A.

Chimello, L

Hermes, M

Andreoli, A

Albuquerque, M

Figueiredo, G

Antônio, M

Koch, G

Moura, P.A

Silva, F

Santana, C.C

Albuquerque, M

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Wireless Sensor Networks, Edge Computing, and Farm Connectivity

Digital Solutions for Soil Health, Water Quality, and Conservation Practices

Remote and Proximal Sensing of Soils and Crops

Invited Presentations

Type

Oral

Poster

Year

2026

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Authors

Filter results5 paper(s) found.
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 Download paper
2. Development of a LoRaWAN Network for Remote Sensing in Precision Agriculture The modernization of agriculture through Agriculture 4.0 requires the use of advanced sensors and wireless communication networks for the precise monitoring of environmental variables and process optimization. However, the implementation of these Internet of Things (IoT) technologies in rural areas frequently faces the challenge of limited infrastructure over large territorial expanses. In this scenario, Low-Power Wide-Area Networks (LPWAN), specifically the LoRaWAN protocol, stand out for their... M. Hermes, M. Albuquerque, A. Andreoli, C. , C. Chaves
3. Use of Digital Permeameter for the Functional Characterization of Geoenvironments Characterizing agricultural geoenvironments with precision is inherently a complex task. Historically, this process has relied on quasi-static edaphic attributes, such as soil texture and apparent electrical conductivity. However, a critical problem exists, as these parameters exhibit low sensitivity to ephemeral structural changes resulting from soil management systems. Texture conditions the productive potential, yet it fails to reflect modifications in pore geometry induced by mechanical pressures... C. Chaves, A. . Quicaña, L. Chimello, M. Hermes, A. Andreoli, M. Albuquerque, G. Figueiredo, M. Hermes
4. 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
5. Opening Ceremony: Special Guest, ISPA & AsBraAP Presidents, and Brazilian Agriculture ... M. Albuquerque

Filter results5 paper(s) found.

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

Download paper

2. Development of a LoRaWAN Network for Remote Sensing in Precision Agriculture

The modernization of agriculture through Agriculture 4.0 requires the use of advanced sensors and wireless communication networks for the precise monitoring of environmental variables and process optimization. However, the implementation of these Internet of Things (IoT) technologies in rural areas frequently faces the challenge of limited infrastructure over large territorial expanses. In this scenario, Low-Power Wide-Area Networks (LPWAN), specifically the LoRaWAN protocol, stand out for their... M. Hermes, M. Albuquerque, A. Andreoli, C. , C. Chaves

3. Use of Digital Permeameter for the Functional Characterization of Geoenvironments

Characterizing agricultural geoenvironments with precision is inherently a complex task. Historically, this process has relied on quasi-static edaphic attributes, such as soil texture and apparent electrical conductivity. However, a critical problem exists, as these parameters exhibit low sensitivity to ephemeral structural changes resulting from soil management systems. Texture conditions the productive potential, yet it fails to reflect modifications in pore geometry induced by mechanical pressures... C. Chaves, A. . Quicaña, L. Chimello, M. Hermes, A. Andreoli, M. Albuquerque, G. Figueiredo, M. Hermes

4. 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

5. Opening Ceremony: Special Guest, ISPA & AsBraAP Presidents, and Brazilian Agriculture

... M. Albuquerque