Enhancing Estimation Performance of Winter Wheat Chlorophyll Fluorescence Parameter Fv/Fm by Fusing Spectral, Textural, and Physiological Features

The ratio of variable fluorescence to maximum fluorescence (Fv/Fm) is a critical indicator of crop photosynthetic health, yet its accurate estimation via remote sensing remains challenging. While vegetation indices (VIs) have been widely used for this purpose, integrating them with texture features (TFs), and chlorophyll content (SPAD) to enhance model accuracy remains unexplored. To address this gap, a field experiment was conducted across the 2022-2023 winter wheat growing season. Five feature fusion strategies—combining VIs derived from UAV multispectral images, TFs extracted from UAV RGB images, and leaf chlorophyll content— were evaluated using Gaussian process (GP) and other machine learning algorithms. Results indicated significant correlations between all three VI categories and Fv/Fm. The highest correlations within each category were observed for ExGR (visible spectrum only; r = 0.80), SIPI (near-infrared without red-edge; r = 0.79), and both MCARI and NDVI705 (red-edge spectrum; r = 0.69). In contrast, TFs exhibited weaker correlations with Fv/Fm, peaking at r = -0.53 for B_M and B_VAR. However, feature selection based solely on maximal within-category correlations may yield suboptimal results due to inter-feature collinearity. Among the five fusion strategies, integrating VIs, TFs, and physiological features (SPAD) achieved optimal performance, increasing validation R² from 0.70 to 0.85 and reducing RMSE from 0.08 to 0.05 compared to single-feature models (GP regression). This approach demonstrates the viability of multi-feature fusion for accurate Fv/Fm monitoring, providing a scalable tool for precision agriculture.