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Vietnamese traditional medicine (VTM) relies on a rich diversity of native plant species; however, urbanization and the dominance of Western medicine have made many of them increasingly difficult to locate and conserve. Accurate identification of these medicinal plants is therefore critical for preserving VTM knowledge and integrating it into digital healthcare and educational platforms. This study introduces DualBranchNet, a deep learning classifier designed to recognize Vietnamese medicinal plants in the VNPlant200 image dataset. DualBranchNet employs a dual-branch convolutional architecture that concatenates features extracted by two pretrained backbones, DenseNet-201 and Xception. To improve training stability and generalization, we employ learning rate warm-up, L2 regularization, and on-the-fly data augmentation. Model performance is tracked via Top-1 accuracy and categorical cross-entropy, and the best checkpoint is evaluated on the full validation set. Experimental results show that DualBranchNet attains high classification accuracy and represents a reliable step toward digitizing and preserving Vietnamese medicinal plant knowledge.

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Deep Learning-Based Classification of Vietnamese Traditional Medicinal Plants Using Dual CNN Backbones

  • 会议论文

  • 发表日期：

    2026年

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  • Khanh Nhan Nguyen,
  • Viet-Tuan Le,
  • Hoang Vinh Truong

摘要

Vietnamese traditional medicine (VTM) relies on a rich diversity of native plant species; however, urbanization and the dominance of Western medicine have made many of them increasingly difficult to locate and conserve. Accurate identification of these medicinal plants is therefore critical for preserving VTM knowledge and integrating it into digital healthcare and educational platforms. This study introduces DualBranchNet, a deep learning classifier designed to recognize Vietnamese medicinal plants in the VNPlant200 image dataset. DualBranchNet employs a dual-branch convolutional architecture that concatenates features extracted by two pretrained backbones, DenseNet-201 and Xception. To improve training stability and generalization, we employ learning rate warm-up, L2 regularization, and on-the-fly data augmentation. Model performance is tracked via Top-1 accuracy and categorical cross-entropy, and the best checkpoint is evaluated on the full validation set. Experimental results show that DualBranchNet attains high classification accuracy and represents a reliable step toward digitizing and preserving Vietnamese medicinal plant knowledge.