UAV-based phenotyping identifies net assimilation rate as a diagnostic trait for synergistic enhancement of rice yield and grain quality

Achieving rice yield-quality synergy, which is critical for breeding and agronomic practice, is hindered by dynamic regulatory gaps due to methodological constraints, while high-throughput unmanned aerial vehicle (UAV) phenotyping can enable breakthroughs by decoding dynamic traits at scale. This study conducted five experiments (EXP, 2022-2024; including nitrogen fertilization, multi-cultivar, and breeding material experiments) with UAV-based phenotyping to establish trait estimation models (EXP1-EXP3), enabling dissection of trait-specific contributions to yield-quality synergies via regression, multi-objective optimization, and path analysis (EXP4-EXP5), and identifying diagnostic traits in practice. Using UAV data, effective regression models were developed to monitor five rice traits: plant height (R 2 = 0.89), aboveground biomass (R 2 = 0.84), leaf area index (R 2 = 0.61), canopy nitrogen content (R 2 = 0.68), and leaf nitrogen content (R 2 = 0.83), thereby systematically establishing 37 critical plant traits across the growth stages. Furthermore, feature importance analysis using extreme gradient boosting (R 2 = 0.99) assessed the importance of these traits for yield and grain quality respectively, and four common traits that were crucial for both yield and grain quality were identified. Notably, the synergistic yield-quality group exhibited 26.38-51.76% higher net assimilation rate (NAR) than the low-performance group (validated by multi-objective optimization), positioning NAR as a diagnostic marker for yield-quality synergistic enhancement. Path analysis revealed that NAR exerted positive effects on yield and grain quality, while yield indirectly influenced grain quality through eating quality. Overall, this study integrated UAV-based phenotyping and trait analysis, providing a novel insight into the synergistic enhancement of yield and grain quality.