Proceedings :: International Society of Precision Agriculture

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Amaral, E

Alves de Morais, R.M

Alves de Araújo, G

Ata-Ul-Karim, S

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Authors

Cammarano, D

Ata-Ul-Karim, S

Canicatti, M

Abalos, D

Zhou, Y

Tanaka, T.S

Butterbach-Bahl, K

Alves de Araújo, G

Costa Souza, J.B

Freire de Oliveira, M

Ortiz, B.V

Luns Hatum de Almeida, S

Felipe dos Santos, A

Pereira da Silva, R.P

Alves de Morais, R.M

, G

Faria, R.D

Silva, L.S

Oliveira, M.D

Zavala, E.H

Arnosti, M.C

Felipe dos Santos, A

Costa Barboza, T

Souza Pinto, L.S

Amaral, E

Lacerda da Silveira, G

Valdes Fernandez , G

Baker, A.P

, G

Oliveira, M.D

Zavala, E.H

Alves de Morais, R.M

Amaral, M.M

de Castro, A.�

Silva, L.S

Topics

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

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Agricultural Robotics, Automation, and Mechanization

Remote and Proximal Sensing of Soils and Crops

Type

Oral

Poster

Year

2026

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Filter results5 paper(s) found.
1. System-based Precision Agriculture for Sustainable Crop Production The major challenge addressed is the systemic mismanagement of nitrogen (N) fertilizer in agricultural fields leading to problems such as leaching of nitrates into groundwater and emission of harmful greenhouse gases. Digital technologies are commercialized in agriculture (available from the early 1990s) but have failed with N fertilization. Despite agriculture is the least digitized sector (as highlighted at the last World Economic Forum) to make a reliable recommendation, researchers need to... D. Cammarano, S. Ata-ul-karim, M. Canicatti, D. Abalos, Y. Zhou, T.S. Tanaka, K. Butterbach-bahl Download paper
2. Integrating Management Zones, Artificial Neural Networks and Remote Sensing for Smart Peanut Harvesting The integration of technologies contributes significantly to agricultural development, especially regarding the rational and more sustainable use of soil. Thus, the use of remote sensing and artificial intelligence techniques combined with precision agriculture can maximize smart harvesting for peanut crops, which face several challenges such as limited harvesting technology, indeterminate growth, and the development of pods below the soil surface. Therefore, this study aimed to develop a peanut...
3. Effect of Terrain Slope Obtained by LiDAR on Operational Performance in Semi-mechanized Coffee Transplanting with Autopilot. The application of precision agriculture techniques has become an important tool in surveying coffee plantations, allowing for the rapid assessment of slope profiles in these areas and indicating the possibility of mechanizing the plots. Therefore, there is a need to work with quality in semi-mechanized transplanting operations using autopilot to optimize future processes related to coffee cultivation. The objective of this study was to determine the efficiency of use and mechanical availability... R. , G. , R.D. Faria, L.S. Silva, M.D. Oliveira, E.H. Zavala
4. 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
5. Spectral Behavior of Coffee Fruit Ripeness Using a Hyperspectral Camera Selective harvesting is essential to ensure high beverage quality in coffee production; however, the coexistence of fruits at multiple ripeness stages on the same plant makes manual selection subjective, labor‑intensive, and time‑consuming. This preliminary study aimed to develop a non‑destructive method based on spectral information for the classification of Coffea arabica L. cv. Arara fruits at green (unripe) and yellow (ripe) stages, using images acquired on a laboratory bench with hyperspectral... A. Palma Diniz Baker, G. , M.D. Oliveira, E.H. Zavala, R. , M.M. Amaral, A.

Filter results5 paper(s) found.

1. System-based Precision Agriculture for Sustainable Crop Production

The major challenge addressed is the systemic mismanagement of nitrogen (N) fertilizer in agricultural fields leading to problems such as leaching of nitrates into groundwater and emission of harmful greenhouse gases. Digital technologies are commercialized in agriculture (available from the early 1990s) but have failed with N fertilization. Despite agriculture is the least digitized sector (as highlighted at the last World Economic Forum) to make a reliable recommendation, researchers need to... D. Cammarano, S. Ata-ul-karim, M. Canicatti, D. Abalos, Y. Zhou, T.S. Tanaka, K. Butterbach-bahl

Download paper

2. Integrating Management Zones, Artificial Neural Networks and Remote Sensing for Smart Peanut Harvesting

The integration of technologies contributes significantly to agricultural development, especially regarding the rational and more sustainable use of soil. Thus, the use of remote sensing and artificial intelligence techniques combined with precision agriculture can maximize smart harvesting for peanut crops, which face several challenges such as limited harvesting technology, indeterminate growth, and the development of pods below the soil surface. Therefore, this study aimed to develop a peanut...

3. Effect of Terrain Slope Obtained by LiDAR on Operational Performance in Semi-mechanized Coffee Transplanting with Autopilot.

The application of precision agriculture techniques has become an important tool in surveying coffee plantations, allowing for the rapid assessment of slope profiles in these areas and indicating the possibility of mechanizing the plots. Therefore, there is a need to work with quality in semi-mechanized transplanting operations using autopilot to optimize future processes related to coffee cultivation. The objective of this study was to determine the efficiency of use and mechanical availability... R. , G. , R.D. Faria, L.S. Silva, M.D. Oliveira, E.H. Zavala

4. 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

5. Spectral Behavior of Coffee Fruit Ripeness Using a Hyperspectral Camera

Selective harvesting is essential to ensure high beverage quality in coffee production; however, the coexistence of fruits at multiple ripeness stages on the same plant makes manual selection subjective, labor‑intensive, and time‑consuming. This preliminary study aimed to develop a non‑destructive method based on spectral information for the classification of Coffea arabica L. cv. Arara fruits at green (unripe) and yellow (ripe) stages, using images acquired on a laboratory bench with hyperspectral... A. Palma Diniz Baker, G. , M.D. Oliveira, E.H. Zavala, R. , M.M. Amaral, A.