Predicting spatial and temporal variability in soybean yield using deep learning and open source data.
Resumen:
ABSTRACT.- Spatial crop yield prediction provides valuable insights for supporting sustainable and precise crop management decisions. This study assessed the capabilities of advanced Deep Learning (DL) architectures in predicting within-field soybean yields using spectral bands from Sentinel-2 (RS-Inputs), weather (W-Inputs), and topographic attributes (TA-Inputs). © 2024 The Author(s). Published by Elsevier B.V
| 2025 | |
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Soybean Sentinel-2 Deep learning Weather inputs Topographic attributes SISTEMA AGRÍCOLA-GANADERO - INIA |
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| Inglés | |
| Instituto Nacional de Investigación Agropecuaria | |
| AINFO | |
| https://ainfo.inia.uy/consulta/busca?b=pc&id=65041&biblioteca=vazio&busca=65041&qFacets=65041 | |
| Acceso abierto |
