Yield–Quality Synergy, Adaptability, and Spectral Characteristics of High-Quality Wheat (Triticum aestivum L.) in the Northern Huang-Huai-Hai Region of China

Multi-environment trials involving 12 wheat (Triticum aestivum L.) cultivars were conducted during the 2023–2024 and 2024–2025 growing seasons at multiple locations in the northern Huang-Huai-Hai wheat region of China. Tai’an (TA), Weifang (WF), and Wuqiao (WQ) were evaluated in both growing seasons, whereas Gaocheng (GC) was included only in 2023–2024 and Xinji (XJ) only in 2024–2025. Analysis of variance (ANOVA) revealed that genotype (G), environment (E), and the genotype-by-environment (G × E) interaction significantly influenced grain yield. To elucidate yield–quality synergy, regional adaptability, and the spectral response characteristics at key phenological stages, we integrated yield-component analysis, the genotype main effect plus genotype-by-environment interaction (GGE) biplot, the additive main effects and multiplicative interactions (AMMI) model, yield-quality synergy, and unmanned aerial vehicle (UAV)–based multispectral monitoring. The mean grain yield across cultivars was 8966.0 kg ha−1. The combined GGE and AMMI analyses indicated that JM765, JMU68, and JM115 achieved favorable combinations of grain yield and environmental stability; JM379 had the highest pooled mean yield but showed greater environmental sensitivity. Based on grain yield and comprehensive quality scores, the tested wheat cultivars were classified into four distinct types: synergistic, high-yield, high-quality, and general. UAV multispectral analysis further revealed stage-dependent associations between vegetation indices and agronomic traits. At anthesis, the simple ratio red-edge index (SR-REG) and green atmospherically resistant index (GARI) showed the strongest positive associations with grain yield. At the dough stage, the nitrogen reflectance index (NRI) and simplified canopy chlorophyll content index (S-CCCI) showed the strongest positive associations with grain yield and grain protein content, respectively. These indices were therefore highlighted as candidate trait-associated spectral indicators. Overall, the study identified cultivars with favorable combinations of grain yield and environmental stability. Stage-specific spectral associations with grain yield and protein content were also identified, supporting cultivar evaluation and UAV-assisted high-throughput phenotyping in the northern Huang Huai Hai wheat region or in other similar regions.