Interpretable deep learning with hyperspectral imaging for Hawthorn cultivar classification and quality prediction

Hawthorn (Crataegus pinnatifida) is a commonly consumed medicinal fruit. This study proposes a rapid and non-destructive technique that integrates hyperspectral imaging (HSI) with interpretable deep learning for the classification of hawthorn cultivars from different regions and the quantitative prediction of key quality indicators, including citric acid, total sugar, and vitamin C content. A total of 1227 samples were collected from 11 categories, representing different cultivars and origins. Model robustness was ensured by acquiring HSI data in three different orientations, with the stalk positioned horizontally, up, and down. Classification results showed an EfficientNet model achieved the highest accuracy (95.92 %) by fusing spectral data from all three orientations. In the regressions, the EfficientNet model outperformed both PLSR and CNN. Among the measured compounds, citric acid and total sugar yielded satisfactory results, with R² values of 0.94 and 0.92 and RPD values of 4.08 and 3.55, respectively. Furthermore, combining gradient-weighted class activation mapping (Grad-CAM) and Shapley additive explanations (SHAP) enabled a visual and quantitative interpretation of spectral feature contributions, effectively addressing black-box issues. This is the first study to integrate HSI with interpretable deep learning for simultaneous classification and quality prediction in hawthorn, with improved model performance through multi-orientation spectral fusion.