Ginseng Seed Variety Identification Based on Hyperspectral Imaging Technology and Transfer Learning

ABSTRACT This study proposes a rapid and non‐destructive method for ginseng seed classification at the individual seed level using hyperspectral imaging combined with transfer learning. A total of 900 seeds from three ginseng varieties were used, with 300 samples per variety. Spectral features were extracted from circular regions of interest, and classification models including PLS‐DA, LSSVM, and random forest were built using preprocessed spectral data, achieving accuracies of 92%, 92%, and 93%, respectively. Principal component images (PC1–PC3) were fused into RGB format and fed into pre‐trained deep learning models (AlexNet, GoogLeNet, Inception‐V3, ResNet‐50, SqueezeNet). Among them, the improved AlexNet model achieved the highest accuracy of 97%. The results demonstrate that transfer learning models integrating spectral and image features outperform traditional spectral models, offering an effective solution for precise classification and quality control of ginseng seeds.