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Abstract Mungbean (Vigna radiata(L.) Wizcek) is an important pulse crop, increasingly used as a source of protein, fiber, low fat, carbohydrates, minerals, and bioactive compounds in human diets. Mungbean is a dicot plant with trifoliate leaves. The primary component of many plant functions, including photosynthesis, light interception, and canopy structure, are leaves. The objectives were to investigate leaf morphological attributes, use image analysis to extract leaf morphological traits from photos from the Iowa Mungbean Diversity (IMD) panel, create a regression model to predict leaflet area, and undertake association mapping. We collected over 5000 leaf images of the IMD panel consisting of 484 accessions over 2 years (2020 and 2021) with two replications per experiment. Leaf traits were extracted using image analysis, analyzed, and used for association mapping. Morphological diversity included leaflet type (oval or lobed), leaflet size (small, medium, large), lobed angle (shallow, deep), and vein coloration (green, purple). A regression model was developed to predict each ovate leaflet's area (adjustedR² = 0.97; residual standard errors of < = 1.10). The candidate genesVradi01g07560,Vradi05g01240,Vradi02g05730, andVradi03g00440are associated with multiple traits (length, width, perimeter, and area) across the leaflets (left, terminal, and right). These are suitable candidate genes for further investigation in their role in leaf development, growth, and function. Future studies will be needed to correlate the observed traits discussed here with yield or important agronomic traits for use as phenotypic or genotypic markers in marker‐aided selection methods for mungbean crop improvement. 

Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [ Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms.