# Comprehensive Evaluation of Mung Bean Germplasm Resources Based on DUS Test Characteristics

## TL;DR

This study evaluates mung bean varieties using standardized traits to identify key characteristics linked to high yield and quality, aiding future breeding efforts.

## Contribution

The study systematically validates DUS characteristics for mung bean breeding and identifies key traits influencing yield.

## Key findings

- PCA and correlation analysis revealed key variation dimensions in plant morphology, pod, and seed characteristics.
- Cluster analysis identified four distinct groups, with Cluster 2 being ideal for high-yield breeding.
- Leaf greenness, pod number per plant, and hundred-grain weight significantly affect yield (p < 0.05).

## Abstract

The Distinctness, Uniformity, and Stability (DUS) testing guidelines for mung beans (Vigna radiata L.) offer a standardized framework for new variety assessment. Although these guidelines are essential for variety management, the actual efficiency and breeding value of the 31 specified DUS characteristics in improving yield potential remain largely underexplored and lack systematic validation. To address this critical research gap, 180 genetically diverse mung bean accessions were analyzed using principal component analysis (PCA) and correlation analysis. The results revealed intrinsic relationships among characteristics and identified key variation dimensions centered on “plant morphology”, “pod characteristics”, and “seed characteristics”. Cluster analysis classified the 180 accessions into four distinct clusters. Cluster 2, in particular, offers a clear selection reference for breeding materials targeting high-yield and quality. The DTOPSIS (Dynamic Technique for Order Preference by Similarity to Ideal Solution) multi-criteria decision-making model was applied, with index weights assigned using an objective weighting method. Following systematic evaluation, Yingge 2 was identified as an outstanding phenotype. Breeders may refer to its quantitative characteristics in subsequent breeding cycles. Linear regression analysis was employed to construct a yield prediction model, identifying leaf greenness, pod number per plant, and hundred-grain weight as three core DUS characteristics with statistically significant effects on final yield (p < 0.05). This study performed a systematic, multi-dimensional analysis and comprehensive evaluation of mung bean germplasm resources based on DUS characteristics, with the aim of identifying key yield-related DUS traits, screen elite germplasm for high-yield breeding, and providing a theoretical basis and practical reference for the efficient improvement and selective breeding of new mung bean varieties.