中藥的準確鑑定對於保證患者安全和有效治療至關重要。 然而，現有的中草藥識別研究在識別的草藥類別和手動數據收集過程方面受到限制，阻礙了全面、高效的識別系統的發展。 本研究旨在通過提出一種自動化中草藥識別系統來解決這些局限性。 我們開發了一種自動化中草藥識別系統，使用戶無論其計算機專業知識如何，都可以輕鬆識別不同類型的草藥，添加新的草藥類別，並訓練系統進行準確識別。 通過利用人工智能技術，系統對數據進行預處理，包括裁剪圖像、去除陰影和增強數據集。 我們採用 DC-ShadowNet 模型進行陰影去除，並利用 RandAugment 進行數據增強。 使用 Swin Transformer 模型進行圖像分類。 實驗結果證明了我們系統的有效性，Swin Transformer 模型和 RandAugment 的結合實現了 99.1% 的最高準確率。 令人驚訝的是，加入陰影去除並沒有帶來更好的結果，這表明陰影可能有助於中草藥識別所必需的特徵。 我們的研究結果強調了在準確的草藥分類中考慮表面特徵和陰影存在的重要性。 通過這項研究，我們開發了一種自動化中草藥識別系統，克服了以前方法的局限性。 通過集成人工智能和自動化，我們的系統簡化了中藥材鑑定流程，使無需豐富 IT 背景的用戶也能輕鬆操作。 識別技術的進步有助於中藥在中醫藥領域更廣泛的應用和理解。

Accurate identification of Chinese herbs is crucial in Traditional Chinese Medicine (TCM) to ensure patient safety and effective treatment. However, existing research in Chinese herb recognition has been limited in terms of recognized herbal categories and the manual data collection process, hindering the development of a comprehensive and efficient recognition system. This study aims to address these limitations by proposing an automated Chinese herb recognition system. We have developed an automated Chinese herb recognition system that empowers users, regardless of their computer expertise, to easily recognize different types of herbs, add new herb categories, and train the system for accurate recognition. By leveraging AI techniques, the system preprocesses data, including cropping images, removing shadows, and augmenting datasets. We employed the DC-ShadowNet model for shadow removal and utilized RandAugment for data augmentation. Image classification was performed using the Swin Transformer model. Experimental results demonstrated the effectiveness of our system, with a combination of the Swin Transformer model and RandAugment achieving the highest accuracy of 99.1%. Surprisingly, the inclusion of shadow removal did not lead to improved results, indicating that shadows may contribute to the features essential for Chinese herb recognition. Our findings highlight the importance of considering surface characteristics and shadow presence in accurate herb classification. Through this research, we have developed an automated Chinese herb recognition system that overcomes the limitations of previous approaches. By integrating AI and automation, our system streamlines the Chinese herb identification process, making it accessible to users without extensive IT backgrounds. This advancement in recognition technology contributes to the broader application and understanding of Chinese herbs in the field of Traditional Chinese Medicine.

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