Image-based high-throughput phenotyping enables genetic analyses of pod morphological traits in mungbean (Vigna radiata (L.) R. Wilczek)

Authors: BODDEPALLI, VENKATA NARESH - Iowa State University; JUBERY, TALKUDER ZAKI - Iowa State University; Cannon, Steven; DUTTA, SOMAK - Iowa State University; GANAPATHYSUBRAMANYAN, BASKAR - Iowa State University; SINGH, ARTI - Iowa State University

Submitted to: G3: Genes, Genomes, Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/17/2026
Publication Date: 4/28/2026
Citation: Boddepalli, V., Jubery, T., Cannon, S.B., Dutta, S., Ganapathysubramanyan, B., Singh, A. 2026. Image-based high-throughput phenotyping enables genetic analyses of pod morphological traits in mungbean (Vigna radiata (L.) R. Wilczek). G3: Genes, Genomes, Genetics. https://doi.org/10.1093/g3journal/jkag106.
DOI: https://doi.org/10.1093/g3journal/jkag106

Interpretive Summary: Key challenges in plant breeding include (1) efficiently measuring complex traits that are to be improved, and (2) identifying genetic markers that are strongly associated with the traits of interest. This study reports ways that these challenges have been addressed in mungbean, which is a nutritious, high-protein crop with potential for farming locations similar to those suited for soybean production. Mungbean fills slightly different nutritional and agronomic niches than soybean, with increasing demand for plant-based protein sources, and with the ability to produce a crop under hot, dry conditions and in a short timeframe (with harvest 60-75 days after planting). This study used machine processing of scanned images to measure and extract pod and seed characteristics, and then used a statistical approach called a genome-wide association study (GWAS) to identify genetic markers that are closely associated with the desired seed and pod traits. The results will be usable by plant breeders to efficiently select new mungbean varieties for U.S. farms and for consumers from domestic and global markets.

Technical Abstract: Mungbean (Vigna radiata (L.) R. Wilczek) is a vital source of digestible proteins and is well-suited for the plant-based protein industry. In this study, we analyzed pod morphological traits in the Iowa Mungbean Diversity (IMD) panel of 372 genotypes (2022-23) using image-analysis-based phenotyping on 2,418 pod images. Pod morphological traits were extracted using deep learning image analysis, achieving excellent agreement with manual measurements (r>0.96 for pod length and seed per pod). Four complementary GWAS models identified 65 significant SNPs (-log10(P) >/= 5.56) associated with pod curvature, length, width, and seed per pod traits. A significant SNP (5_35265704) on chromosome 4 was linked to pod dimensional traits, length, width, and curvature. A candidate gene, Virad04G0076900, located 15.6 kb from this SNP, is part of the GH3 gene family and has an Arabidopsis ortholog (AT4G27260) known for influencing organ elongation, pod, and seed development. Another SNP, 5_210437 on chromosome 6, has been found to be significantly associated with both pod length and seed per pod. A candidate gene, Virad06G0002400, is located 36.5 kb from this SNP within the significant LD block, belongs to the potassium transporter family, and shares homology with the Arabidopsis HAK5 gene AT4G13420, which influences pod and seed growth. Image-based measurements achieved genomic prediction accuracies ranging from 0.61 to 0.85 across various traits, demonstrating comparable accuracy to manual methods for linear traits and up to 22% improvement for complex shape traits. These results highlight the potential of deep learning-assisted phenomics integrated with genomic tools to accelerate selection for improved pod architecture in mungbean breeding programs across the Midwestern United States and globally.