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Silva, D.O
Santos, A.L
Silveira de Farias, M
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Authors
Silveira Pavão, L
Müllich, A
Rolim Farias da Silva, E
Cavalcanti, R
Silveira de Farias, M
Maldaner, I
Sgarbossa, J
, L
Kern, L.G
Kaefer Seganfredo, G
da Silva, G.B
Pegoraro, V.C
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
Digital Solutions for Soil Health, Water Quality, and Conservation Practices
Remote and Proximal Sensing of Soils and Crops
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Type
Poster
Oral
Year
2026
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1. Topographic Modeling Using Remotely Piloted Aircraft to Identify Areas with Water Erosion Potential and to Plan Sowing Lines

Water erosion constitutes one of the main factors of agricultural soil degradation. In this context, knowledge of the topography of agricultural fields and the planning of sowing lines guided by geotechnologies emerges as a strategy to mitigate surface runoff and soil loss. This study aimed to perform the topographic modeling of an agricultural field and to analyze the effect of using different sowing line designs on the longitudinal slope of these lines. The study was conducted in an agricultural... L. Silveira Pavão, A. Müllich, E. Rolim Farias Da Silva, R. Cavalcanti, M. Silveira De Farias, I. , J. Sgarbossa, L.

2. 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

3. 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