Development of an RGB-GE Data Generation and XAI-Based On-Site Classification System for Differentiating Zizyphus jujuba and Zizyphus mauritiana in Herbal Medicine Applications

Abstract

Herbal medicines have significant industrial value in East Asia. Zizyphus jujuba Mill. var. spinosa, used in Korea for treating insomnia, is often confused with Zizyphus mauritiana Lam., which has unverified medicinal properties yet is sold at premium prices. This misclassification undermines consumer trust and poses health risks. This study proposes a deep learning-based classification system trained on RGB-GE data, combining grayscale and edge-detected images with RGB inputs to enhance feature extraction while reducing color-dependency. Our method achieves superior generalization while maintaining cost-effectiveness. The system incorporates Grad-CAM for model interpretation and reliability. By comparing accuracy and speed across basicCNN, DenseNet, and InceptionV3 models, we identified an optimal solution for on-site herbal medicine classification, achieving 98.36% accuracy with basicCNN, ensuring reliable quality control.