Development of an RGB-GE Data Generation and XAI-Based On-Site Classification System for Differentiating <i>Zizyphus jujuba</i> and <i>Zizyphus mauritiana</i> in Herbal Medicine Applications
Herbal medicines have significant industrial value in East Asia. <i>Zizyphus jujuba</i> Mill. var. spinosa, used in Korea for treating insomnia, is often confused with <i>Zizyphus mauritiana</i> Lam., which has unverified medicinal properties yet is sold at premium prices. Th...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/10/1022 |
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| Summary: | Herbal medicines have significant industrial value in East Asia. <i>Zizyphus jujuba</i> Mill. var. spinosa, used in Korea for treating insomnia, is often confused with <i>Zizyphus mauritiana</i> 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. |
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| ISSN: | 2077-0472 |