Application development for traditional chinese medicine herb

Loke, Zhu Mun (2025) Application development for traditional chinese medicine herb. Final Year Project, UTAR.

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Abstract

Traditional Chinese Medicine (TCM) has been practiced for thousands of years, relying heavily on the accurate identification and combination of herbs for effective treatment. However, the vast array of herbs and their subtle differences pose significant challenges in ensuring accurate identification and proper use, especially for non-experts. To address these challenges, this project proposes the development of a mobile application that utilizes advanced image recognition technology, specifically Convolutional Neural Networks (CNNs), to accurately identify TCM herbs and provide users with personalized herb combination recommendations. The application is designed to enhance the accessibility and safety of TCM practices by offering a user-friendly interface that supports herb identification, detailed information on each herb, and symptom-based herb recommendations. The project leverages the capabilities of a pre-trained CNN model, fine-tuned with a comprehensive herb dataset, to deliver high accuracy in herb recognition. Additionally, the application includes features such as user authentication, camera integration for herb scanning, and a history tracking module to support personalized user experiences. Through this project, the application aims to modernize TCM practices by integrating cutting-edge technology, providing a valuable tool for practitioners, students, and patients to enhance their understanding and effective use of TCM herbs.

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