Monitoring phosphorus content in winter wheat using feature fusion and feature selection from UAV remote sensing...

The rapid and accurate quantification of plant phosphorus (P) content is essential for the real-time assessment of crop P status and improvement of P fertilizer use efficiency. However, non-destructive and rapid approaches for P monitoring are limited. In this study, the feasibility of monitoring plant phosphorus content (PPC) in winter wheat was evaluated through multi-source feature fusion of unmanned aerial vehicle (UAV) imagery based on a long-term field experiment with five P treatments. Multiple spectral features, including color indices (CIs), fractional vegetation cover (FVC), vegetation indices (VIs), texture features (TFs) and texture indices (TIs), were extracted from UAV RGB and multispectral images. Sensitive spectral features were systematically screened using Pearson correlation analysis, random forest (RF) importance ranking, and the Relief algorithm. Selected features were then fed into three machine learning models, RF, support vector machine (SVM), and k-nearest neighbor (KNN) to predict PPC. The results showed that GRI, VARI, MGRVI, TGI, NDRE, and CIred edge were highly correlated with PPC at the maturity stage (r = 0.96). Both TFs and TIs demonstrated stronger correlations with PPC at the 750 and 840 nm bands, with most TIs outperforming TFs, confirming the feasibility of spectral-based PPC estimation. Based on the selected input variables including DTI (450-Ent, 750-Mea), 840-Mea, and RVI, the SVM model achieved the best performance (R²c=0.94, RMSEc=0.29, RPDc=4.03; R²v=0.92, RMSEv=0.36, RPDv=3.48). These results highlight the potential of combining VIs, TFs, and TIs features for training machine learning models for PPC prediction, while the organ-level physiological explanations warrantee further investigations under controlled P gradients. This study provides data-driven insights for UAV-based monitoring of plant P nutritional status under local experimental conditions.