Proceedings :: International Society of Precision Agriculture

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Silva, D.O

Santos, A.L

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Authors

Santos, A.L

Linhares, A.A

Barbosa , P.C

Lopes, W.C

Reis, M.D

Rocha, K.D

de Oliveira, D.G

Rodrigues, M.S

Costa, D.D

Silva, V.S

Silva, E.S

Silva, D.O

Oliveira, M.F

Tavares, A.C

Negrini, R.P

Mendes, L.A

Topics

Remote and Proximal Sensing of Soils and Crops

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Oral

Year

2026

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Filter results2 paper(s) found.
1. Development of Predictive Models for Determining Organic Carbon and Clay Content in Soils under Irrigated Fruit Production in the Brazilian Semi-Arid Region The agricultural sector plays a pivotal role in both greenhouse gas emissions and climate change mitigation through soil carbon sequestration. Total organic carbon (TOC) and soil texture - particularly clay content - are key indicators of this dynamic, as they influence organic matter stabilization, water retention, and soil structural quality. In semi-arid regions, where edaphoclimatic conditions and water scarcity constrain agricultural production, the integrated assessment of these attributes... A.L. Santos, A.A. Linhares, P.C. Barbosa , W.C. Lopes, M.D. Reis, K.D. Rocha, D.G. De Oliveira, M.S. Rodrigues, D.D. Costa Download paper
2. Satellite Embedding-Based Corn Yield Prediction Using AutoML and Explainable AI Accurate, spatially explicit yield mapping underpins many precision agriculture decisions (e.g., variable-rate inputs and zone management), yet reliable yield monitor data are not always available and can be difficult to standardize across operations. Satellite-based yield models are often built from hand-crafted vegetation indices or phenology metrics, which may limit transferability across fields and years. Here, we evaluated a pixel-level corn yield prediction workflow that uses Satellite Embedding... V.S. Silva, E.S. Silva, D.O. Silva, M.F. Oliveira, A.C. Tavares, R.P. Negrini, L.A. Mendes Download paper

Filter results2 paper(s) found.

1. Development of Predictive Models for Determining Organic Carbon and Clay Content in Soils under Irrigated Fruit Production in the Brazilian Semi-Arid Region

The agricultural sector plays a pivotal role in both greenhouse gas emissions and climate change mitigation through soil carbon sequestration. Total organic carbon (TOC) and soil texture - particularly clay content - are key indicators of this dynamic, as they influence organic matter stabilization, water retention, and soil structural quality. In semi-arid regions, where edaphoclimatic conditions and water scarcity constrain agricultural production, the integrated assessment of these attributes... A.L. Santos, A.A. Linhares, P.C. Barbosa , W.C. Lopes, M.D. Reis, K.D. Rocha, D.G. De Oliveira, M.S. Rodrigues, D.D. Costa

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2. Satellite Embedding-Based Corn Yield Prediction Using AutoML and Explainable AI

Accurate, spatially explicit yield mapping underpins many precision agriculture decisions (e.g., variable-rate inputs and zone management), yet reliable yield monitor data are not always available and can be difficult to standardize across operations. Satellite-based yield models are often built from hand-crafted vegetation indices or phenology metrics, which may limit transferability across fields and years. Here, we evaluated a pixel-level corn yield prediction workflow that uses Satellite Embedding... V.S. Silva, E.S. Silva, D.O. Silva, M.F. Oliveira, A.C. Tavares, R.P. Negrini, L.A. Mendes

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