Summer foxtail millet (Setaria italica L.) is a crucial crop in the arid and semi-arid regions of the North China Plain. Therefore, adopting effective irrigation management strategies is essential for conserving water resources while sustaining millet production in these water-limited areas. A two-year field experiment was conducted in Hengshui in 2020 and 2021 to determine the optimal irrigation amount for foxtail millet and evaluate the critical role of root distribution across various soil depths in determining yield and water productivity. Grain yield, yield-related traits, water use efficiency, and root traits were measured under six irrigation regimes (I0, I1, I2, I3, I4, and I5). Grain yield significantly increased with irrigation, but no further significant yield improvement was observed between the I3 and I5 treatments. The highest water productivity was observed under I3 in 2020 and I2 in 2021. Biomass, thousand grain weight, abortive grain rate, panicle dry weight, and water use efficiency under I3 were similar to those under I4 and I5 treatments. Root traits, including total root length, surface area, volume, and dry weight, did not significantly differ between I3, I4, and I5. Grey relational analysis indicated that total water content in the shallow soil layer (0–40 cm) had the greatest impact on yield. Overall, the I3 treatment (150 mm) is recommended as the optimal irrigation amount for increasing foxtail millet production and water use efficiency.

Drought stress severely limits foxtail millet yield and quality, yet current drought-resistance indices are exclusively yield-oriented and ignore grain-filling quality. Our two-year (2024–2025) experiments with 24–48 varieties revealed that yield and blighted grain rate (BGR) are partially decoupled (e.g., Zhangzagu 18: yield 2307 kg/ha, BGR 0.444; Zhonggu 19: yield 1622 kg/ha, BGR 0.280). We therefore constructed the Yield–Quality Synergy Index (YQSI = DYI − BGR), which penalizes varieties with poor grain filling. The YQSI tied for first place with DYI in comprehensive screening performance and achieved the highest inter-annual stability (Spearman ρ = 0.823, Jaccard = 0.438, composite score = 1.261). Sensitivity analysis confirmed robustness of the equal-weight formula across a 4-fold range of quality-penalty weights. Six strongly drought-resistant germplasms with balanced yield and quality were identified. Using UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R² = 0.819 and an F1 score of 0.933 for variety screening. Feature-importance analysis highlighted NDVI, WDRVI, and red-edge texture as key predictors. This study provides a quality-constrained drought-resistance evaluation framework and demonstrates the potential of UAV-based high-throughput phenotyping for foxtail millet breeding.

Rapid phenotyping of water- and salt-soluble wheat protein fractions can support grain quality evaluation and breeding. Whole-wheat flour from water-nitrogen and water-fertilizer trials was used to predict measured albumin and globulin contents and their derived sum, termed the metabolic-protein sum (albumin + globulin). After systematically comparing various preprocessing, wavelength selection, and regression approaches, a framework combining order-optimized derivative preprocessing (ODP), a hybrid feature-selection approach based on minimum redundancy maximum relevance (MRMR) and recursive feature elimination (RFE) and a tabular prior-data fitted network (TabPFN) regressor provided consistently strong test-set performance. The optimal derivative orders were 1.0 for albumin and the metabolic-protein sum and 0.8 for globulin. The ODP-MRMR-RFE-TabPFN framework retained 147, 46, and 70 wavelengths for albumin, globulin, and the metabolic-protein sum, yielding test set coefficient of determination (R²) values of 0.847, 0.693, and 0.850; root mean square error (RMSE) values of 3.073, 0.951, and 3.368 mg g⁻¹; and residual prediction deviation (RPD) values of 2.554, 1.804, and 2.586, respectively. Leave-one-cultivar-out validation revealed substantial variation in predictive performance across held-out cultivars, with R² and RPD ranging from 0.282 to 0.736 and from 1.180 to 1.945, respectively, for albumin, and from 0.416 to 0.750 and from 1.308 to 2.001, respectively, for globulin. Thus, although albumin achieved quantitative-level performance under the random train-test split, this performance was not consistently maintained for unseen cultivars, whereas globulin was generally more suitable for screening. SHapley additive explanations (SHAP) and partial dependence plots (PDPs) revealed that visible wavelengths contributed strongly to all models, with near-infrared (NIR) wavelengths supplying complementary information for globulin and the metabolic-protein sum. Overall, this framework offers rapid, interpretable flour-based estimation of metabolically active wheat protein fractions.