ArticleThe plant genome2026
Integrative genomics and zero-shot deep learning-based phenotyping reveal the genetic architecture of seed traits in mungbean (Vigna radiata L.).
Article in The plant genome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
7 authors.
Funding
Abstract
Improving seed size and weight is a major breeding goal in mungbean (Vigna radiata (L.) R. Wilczek). Improved genomic resources and precision phenotyping may enable more efficient selection for seed trait improvement. In this study, we integrated a deep learning-based segment anything model phenotyping pipeline with genome-wide association studies (GWAS), comparative mapping, and genomic prediction (GP) to dissect the genetic architecture of seed size, shape, and weight traits in the Iowa mungbean diversity panel. The zero-shot segmentation approach reliably captured seed size traits, which exhibited high heritability (H
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