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

ABSTRACT: 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 machine learning-based feature extraction models, including principal component analysis (PCA) and autoencoders (AE). Here, we significantly enhance pre-harvest yield prediction at plot-scale using Compositional Autoencoders (CAE) - a deep-learning-based feature extraction approach designed to disentangle genotype (G) and environment (E) features - on high-resolution, plot-level satellite imagery. Our approach uses a dataset of approximately 4,000 satellite images collected from replicated plots of 84

SUBMITTER: Powadi AA 

PROVIDER: S-EPMC12518292 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature