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Predicting spatial and temporal variability in soybean yield using deep learning and open source data.

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-I...

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Glavni avtor: GASO, D. (author)
Drugi avtorji: LA ROSA, L. E. C. (author), PUNTEL, L. A. (author), RATTALINO EDREIRA, J. I. (author), DE WIT, A. (author), KOOISTRA, L. (author)
Format: article
Jezik:angleščina
Izdano: 2025
Teme:
Soybean
Sentinel-2
Deep learning
Weather inputs
Topographic attributes
SISTEMA AGRÍCOLA-GANADERO - INIA
Online dostop:https://ainfo.inia.uy/consulta/busca?b=pc&id=65041&biblioteca=vazio&busca=65041&qFacets=65041
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Izvleček: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

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