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.

Maize is a staple crop supporting global food security. Comprehensive evaluation of its phenotypic variation and environmental adaptability is urgently needed. In this study, three mutually independent maize germplasm panels with a total of 1197 hybrids were characterized using 39 standardized DUS traits: Panel A (783 hybrids, single location, 2023), Panel B (129 hybrids, two locations in the same year), and Panel C (285 hybrids, single location across two consecutive years). Tassel- and anthocyanin-related traits exhibited wide phenotypic variation and strong germplasm discrimination ability, while leaf margin anthocyanin (Char. 8) displayed rare genotypes with unique identification values. Hierarchical clustering divided all materials into eight distinct groups. PCA revealed weak overall phenotypic differentiation, which is likely driven by long-term directional selection in modern maize breeding. AMMI-based stability evaluation demonstrated that grain morphological traits (Char. 33, Char. 35) maintained stable performance across locations and years, whereas anthocyanin-related descriptors (Char. 8, Char. 14) showed severe environmental plasticity. Using the classic Multi-Trait Stability Index (MTSI), we screened top stable genotypes: 77, 69 and 52 for cross-location trials, and N90, N85 and N107 for multi-year trials. BLUP five-fold cross-validation revealed that grain morphological traits achieved high cross-location predictive correlation (r² > 0.80), whereas cross-year predictability was notably lower, reflecting greater influence of annual climatic fluctuations. Collectively, this study constructs a standardized analytical pipeline to mine routine multi-environment DUS records, identifies stable elite germplasm, and provides data-driven references for optimizing maize breeding and improving official DUS testing efficiency.