UAV Hyperspectral Estimation of Malus sieversii Canopy SPAD Index Using Transformer-LSTM

 

Abstract:

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.

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