Proceedings :: International Society of Precision Agriculture

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SILVA CAVALHEIRO, G

Souza Pinto, L.S

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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

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

DE SOUZA Santos, R

HINES PORPINO SANTOS, E

FREITAS DO NASCIMENTO, J

FARIAS DO NASCIMENTO, J

GOMES MESQUITA, D

FREITAS DA SILVA, T

SILVA CAVALHEIRO, G

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Site-Specific Nutrient, Lime and Seed Management

Type

Oral

Poster

Year

2026

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Authors

Filter results3 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. Evaluation of the Performance of Computer Vision Models in the Detection and Counting of Tomato Plants Infected by Tomato Spotted Wilt Virus (TSWV) Tomato is one of the most economically important vegetable crops worldwide. However, this crop is severely affected by Tomato Spotted Wilt Virus (TSWV), whose transmission occurs mainly through thrips. Thus, identifying infected plants is an important step to reduce the dissemination and infection of healthy plants, reducing economic losses. Computer vision-based models have been widely used in the automated detection of plant diseases. In this context, this work aimed to evaluate the performance... L.S. Souza Pinto, S. . Azevedo, M. . Medeiros, A. Felipe Dos Santos, T. Costa Barboza, M.C. Arnosti, G. Valdes Fernandez , G. Lacerda Da Silveira Download paper
3. Development and Field Validation of SMART-C: A Geostatistics and PCA-Based Decision Framework for Site-Specific Cocoa Management in the Brazilian Amazon Cocoa production plays a major socioeconomic role in Pará State, Brazil’s largest producing region, with annual output exceeding 140 thousand tons. Although Brazil ranks among the world’s leading cocoa producers, most production systems are still managed using field-average approaches that disregard within-field spatial variability of soil attributes and crop performance. This limitation restricts input efficiency and long-term system sustainability in perennial tropical systems.This...

Filter results3 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. Evaluation of the Performance of Computer Vision Models in the Detection and Counting of Tomato Plants Infected by Tomato Spotted Wilt Virus (TSWV)

Tomato is one of the most economically important vegetable crops worldwide. However, this crop is severely affected by Tomato Spotted Wilt Virus (TSWV), whose transmission occurs mainly through thrips. Thus, identifying infected plants is an important step to reduce the dissemination and infection of healthy plants, reducing economic losses. Computer vision-based models have been widely used in the automated detection of plant diseases. In this context, this work aimed to evaluate the performance... L.S. Souza Pinto, S. . Azevedo, M. . Medeiros, A. Felipe Dos Santos, T. Costa Barboza, M.C. Arnosti, G. Valdes Fernandez , G. Lacerda Da Silveira

Download paper

3. Development and Field Validation of SMART-C: A Geostatistics and PCA-Based Decision Framework for Site-Specific Cocoa Management in the Brazilian Amazon

Cocoa production plays a major socioeconomic role in Pará State, Brazil’s largest producing region, with annual output exceeding 140 thousand tons. Although Brazil ranks among the world’s leading cocoa producers, most production systems are still managed using field-average approaches that disregard within-field spatial variability of soil attributes and crop performance. This limitation restricts input efficiency and long-term system sustainability in perennial tropical systems.This...