Nitrogen is a major determinant of crop growth, yield formation, and grain quality, making precise nitrogen (N) management essential for sustainable production. This study integrated field canopy spectra acquired with an ASD FieldSpec 4 spectrometer and unmanned aerial vehicle (UAV)-based RGB imagery to estimate leaf nitrogen content in broomcorn millet across four growth stages. Vegetation indices (VIs) and gray-level co-occurrence matrix texture features were derived, and six algorithms—partial least squares (PLS), support vector machine (SVM), random forest (RF), one-dimensional convolutional neural network (CNN1D), one-dimensional residual network (ResNet1D), and one-dimensional U-Net (U-Net)—were evaluated. Among the spectral preprocessing methods, the first-derivative transformation showed the strongest relationship with leaf nitrogen content (r = −0.85). Models based on multi-source feature fusion consistently outperformed those using a single data source. The U-Net model using ASD + RGB + VIs + texture features achieved the highest test-set accuracy (R² = 0.913, RMSE = 1.851, and RPD = 3.382). SHapley Additive exPlanations (SHAP) analysis identified blue-band correlation (B_Correlation) as the most influential individual predictor (mean absolute SHAP value = 0.39), while texture features accounted for 45.170% of the cumulative feature importance. These results demonstrate the potential of multi-source remote sensing and deep learning for rapid, non-destructive assessment of leaf nitrogen content and precision nitrogen management in broomcorn millet.

Heterotrimeric GTP-binding proteins, commonly known as G proteins, act as core molecular switches in plant signal transduction, governing multiple biological processes including plant growth, development, phytohormone signaling, and abiotic stress adaptation. In this study, a total of 12 G-protein-related genes were identified in foxtail millet, consisting of six Gα subunit-encoding genes, one Gβ subunit-encoding gene, and five Gγ subunit-encoding genes. Gene structure and conserved motif analyses showed that members classified into the same subunit clade possessed highly conserved exon–intron organization and motif distribution, indicating evolutionary structural conservation within each G-protein subfamily. Promoter Cis-acting element analysis revealed that all G-protein family genes harbored light-responsive elements, as well as multiple regulatory elements associated with phytohormone signaling and abiotic stress responses. Transcriptome analysis showed that different G-protein genes exhibited distinct expression patterns under salt stress. Notably, SiαXLG2 exhibited drastically induced expression upon salt stress, which serves as a pivotal candidate gene for salt-stress response in foxtail millet. Collectively, this study provides a systematic characterization of the G-protein gene family and offers candidate gene resources for further elucidating G-protein-mediated salt-stress regulatory mechanisms in foxtail millet.