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This study presents a comparative analysis of deep learning models used for medicinal plant identification, highlighting the limitations of single-modal approaches and proposing a multi-modal framework integrating leaves, bark, stems, and flowers. We evaluate various existing methodologies and introduce a novel hybrid CNN-Transformer model for enhanced accuracy. Our proposed model achieves superior performance in real-world scenarios and paves the way for robust medicinal plant classification.

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Multi-modal Deep Learning for Medicinal Plant Identification: A Comparative Benchmark Study

  • 会议论文

  • 发表日期：

    2026年

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  • Swaraj Jagdale,
  • Himangi Pande

摘要

This study presents a comparative analysis of deep learning models used for medicinal plant identification, highlighting the limitations of single-modal approaches and proposing a multi-modal framework integrating leaves, bark, stems, and flowers. We evaluate various existing methodologies and introduce a novel hybrid CNN-Transformer model for enhanced accuracy. Our proposed model achieves superior performance in real-world scenarios and paves the way for robust medicinal plant classification.