Soybean variety development using high-resolution satellite imagery and machine learning enabled phenomic-assisted selection

<jats:p>This study evaluated high-resolution multispectral (MS) imagery from the Pléiades Neo satellite constellation for seed yield (SY) prediction and phenomic-assisted selection (PAS) in a soybean cultivar development program. Data were collected during the 2022–2024 growing seasons in Iowa from more than 54,000 progeny-row (PR) and yield-trial (YT) plots at three time points per season. Random forest models were trained using spectral plot features derived from raw bands (RBs), RGB band-based vegetation indices (RGB VIs), and multispectral vegetation indices (MS VIs). MS VIs provided the most consistent predictive performance, with average R² values of 0.53 for PR and 0.65 for YT. While RBs performed comparably, RGB VIs were weaker. Later-season imagery had the greatest predictive value, and two well-timed acquisitions may capture most of the useful information. A leave-one-trial-out method showed that at a 30% selection threshold, MS VI models achieved mean sensitivity, accuracy, and specificity of 0.54, 0.72, and 0.80, respectively, in both PR and YT datasets. In independent 2024 YT-2 trials, satellite-based PAS showed moderate-to-strong agreement (50-70%) with breeder selections across maturity-group ranges. These results demonstrate that high-resolution satellite imagery can predict SY at the breeding-plot scale and support scalable advancement and culling decisions in soybean cultivar development.</jats:p>