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...

Täydet tiedot

Bibliografiset tiedot

Päätekijä:	GASO, D. (author)
Muut tekijät:	LA ROSA, L. E. C. (author), PUNTEL, L. A. (author), RATTALINO EDREIRA, J. I. (author), DE WIT, A. (author), KOOISTRA, L. (author)
Aineistotyyppi:	article
Kieli:	englanti
Julkaistu:	2025
Aiheet:	Soybean Sentinel-2 Deep learning Weather inputs Topographic attributes SISTEMA AGRÍCOLA-GANADERO - INIA
Linkit:	https://ainfo.inia.uy/consulta/busca?b=pc&id=65041&biblioteca=vazio&busca=65041&qFacets=65041
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