Abstract

Soybean pod-related traits are important for seed development, yield formation, and cultivar evaluation. However, conventional measurements are inefficient and have limited ability to characterize complex pod features such as curvature, local enlargement, and continuous color variation. In this study, mature pod images of 187 cultivated soybean accessions collected across two successive years were analyzed using a deep learning-assisted image phenotyping approach. Eight pod-related traits related to size, morphology, and color were extracted from pod images. A genome-wide association study (GWAS) was performed using 61,541 high-quality SNP markers to dissect the genetic basis of these image-derived pod traits. A total of 16 stable loci associated with pod size, morphology, and color traits were identified across 11 chromosomes. Among these loci, eight were not reported in the previous image-based soybean pod GWAS study. Based on SoyBase gene annotation and Gene Ontology biological process information, 32 biologically relevant candidate gene records were prioritized within the corresponding candidate genomic intervals, while pod-related expression profiles and SoyBase association information were used as supporting evidence for candidate gene evaluation. These findings indicate that refined image-derived traits can provide complementary genetic information beyond conventional pod measurements and offer additional opportunities for dissecting soybean pod development, morphology, and mature pod color variation.

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