Enhancing yield prediction from plot-level satellite imagery through genotype and environment feature disentanglement

Accurately predicting yield during the growing season enables improved crop management and better resource allocation for both breeders and growers. Existing yield prediction models for an entire field or individual plots are based on satellite-derived vegetation indices (VIs) and widely used machin...

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Vydáno v:	Frontiers in plant science Ročník 16; s. 1617831
Hlavní autoři:	Powadi, Anirudha A., Jubery, Talukder Z., Tross, Michael, Shrestha, Nikee, Coffey, Lisa, Schnable, James C., Schnable, Patrick S., Ganapathysubramanian, Baskar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Media SA 2025 Frontiers Media S.A
Témata:	Accuracy Agricultural production Clustering Corn Corn belt Crop management crop yield prediction Decision making Deep learning Environmental factors Feature extraction Genotype & phenotype genotype × environment interactions Genotypes Growing season Growth models Image resolution latent feature extraction Machine learning Plant breeding Precision agriculture Prediction models Principal components analysis representation learning Resource allocation satellite data Satellite imagery Unmanned aerial vehicles Vegetation Vegetation index United States > US Missouri genotype × environment interactions crop yield prediction satellite data representation learning latent feature extraction
ISSN:	1664-462X, 1664-462X
On-line přístup:	Získat plný text
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