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.

Accurate monitoring of the leaf area index (LAI) throughout the entire growth cycle of walnut trees using UAV multispectral imagery is essential for digital orchard management. In this study, focusing on the ‘Wen 185’ walnut variety in Xinjiang, we simultaneously acquired UAV multispectral images and ground-measured LAI data during four critical growth stages: expansion, hard shell, oil conversion, and maturity. A total of 25 vegetation indices and 48 texture features derived from the gray-level co-occurrence matrix were extracted. Hybrid feature selection combining linear (Pearson correlation), nonlinear (maximum information coefficient and random forest importance), and multiple consensus strategies was employed to reduce redundancy. LAI prediction models were constructed using four algorithms: Random Forest (RF), Support Vector Machine (SVM), LASSO, and Ridge Regression (RR), with model interpretability enhanced by SHAP analysis. Results showed that the multiple consensus screening reduced feature redundancy by an average of 69.6%. SHAP identified five core features: Redge_750_Mean, NDVI, B_Mean, RENDVI, and G_Homogeneity. Importantly, predictor importance shifted significantly with phenology: texture features dominated during the expansion stage, while red-edge indices (RENDVI and Redge_750_Mean) became predominant during the hard shell and oil conversion stages, effectively mitigating the saturation problem commonly observed in traditional indices such as NDVI within the LAI range of 1.5–5.8 in this study. The hybrid feature subset combining “red-edge spectrum + spatial texture” with the Random Forest algorithm achieved superior performance across all stages, with the RPD value exceeding 2.0 during the oil conversion stage, indicating excellent estimation capability. This study demonstrates that a “quality over quantity” feature selection strategy not only reduces model complexity but also enables high-precision, dynamic LAI monitoring throughout the entire walnut growth cycle, providing a scientific basis for intelligent management of large-scale orchards in arid regions.