The accurate estimation of leaf chlorophyll content (LCC) is a significant foundation in assessing litchi photosynthetic activity and possible nutrient status. Hyperspectral remote sensing data have been widely used in agricultural quantitative monitoring research for the non-destructive assessment of LCC. Variable selection approaches are crucial for analyzing high-dimensional datasets due to the high danger of overfitting, time-intensiveness, or substantial computational requirements. In this study, the performance of five machine learning regression algorithms (MLRAs) was investigated based on the hyperspectral fractional order derivative (FOD) reflection of 298 leaves together with the variable combination population analysis (VCPA)-genetic algorithm (GA) hybrid strategy in estimating the LCC of Litchi. The results showed that the correlation coefficient (r) between the 0.8-order derivative spectrum and LCC had the highest correlation coefficients (r = 0.9179, p < 0.01). The VCPA-GA hybrid strategy fully utilizes VCPA and GA while compensating for their limitations based on a large number of variables. Moreover, the model was developed using the selected 14 sensitive bands from 0.8-order hyperspectral reflectance data with the lowest root mean square error in prediction (RMSEP = 5.04 $\mathsf{\mu }\mathrm{g}·{\mathrm{cm}}^{-2}$). Compared with the five MLRAs, validation results confirmed that the ridge regression (RR) algorithm derived from the 0.2 order was the most effective for estimating the LCC with the coefficient of determination (R² = 0.88), mean absolute error (MAE = 3.40$\mathsf{\mu }\mathrm{g}·{\mathrm{cm}}^{-2}$), root mean square error (RMSE = 4.23$\mathsf{\mu }\mathrm{g}·{\mathrm{cm}}^{-2}$), and ratio of performance to inter-quartile distance (RPIQ = 3.59). This study indicates that a hybrid variable selection strategy (VCPA-GA) and MLRAs are very effective in retrieving the LCC through hyperspectral reflectance at the leaf scale. The proposed methods could further provide some scientific basis for the hyperspectral remote sensing band setting of different platforms, such as an unmanned aerial vehicle (UAV) and satellite.

Canopy SPAD index is a practical indicator for evaluating the photosynthetic status and health of Malus sieversii, an endangered wild apple resource in Xinjiang. To develop a rapid and non-destructive monitoring approach, 255 canopy samples were collected across the flower fading stage, fruit stage, and fruit mature stage using synchronized UAV hyperspectral imaging and ground SPAD measurements. Spectral preprocessing, feature-band selection, regression modeling, and SHAP interpretation were evaluated using training-set optimization and independent test-set validation. SG-FD produced the strongest preprocessing response, with a maximum absolute correlation coefficient of 0.70. SiPLS reduced 220 effective bands to 84 wavelengths; subsequent CARS, GA, and SPA screening retained 28, 8, and 12 wavelengths, respectively. The SiPLS-CARS-based Transformer-LSTM model achieved the best performance, with R² = 0.91 and RMSE = 2.12 in training and R² = 0.86 and RMSE = 2.47 in testing. SHAP results indicated that red-edge wavelengths and visible sensitive bands contributed most to prediction. The proposed UAV hyperspectral and Transformer-LSTM framework provides an interpretable proof-of-concept method for canopy SPAD index estimation in Malus sieversii and supports non-destructive monitoring of wild fruit forest health.