Sun Xin, Qian Huinan, Xiong Yiliang, Zhu Yingli, Huang Zhaohan, Yang Feng
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China.
School of Traditional Chinese Classics, Beijing University of Chinese Medicine, Beijing, 100029, China.
Sci Rep. 2022 Apr 21;12(1):6579. doi: 10.1038/s41598-022-10449-9.
With the increasing popularity of herbal medicine, high standards of the high quality control of herbs becomes a necessity, with the herb recognition as one of the great challenges. Due to the complicated processing procedure of the herbs, methods of manual recognition that require chemical materials and expert knowledge, such as fingerprint and experience, have been used. Automatic methods can partially alleviate the problem by deep learning based herb image recognition, but most studies require powerful and expensive computation hardware, which is not friendly to resource-limited settings. In this paper, we introduce a deep learning-enabled mobile application which can run entirely on common low-cost smartphones for efficient and robust herb image recognition with a quite competitive recognition accuracy in resource-limited situations. We hope this application can make contributions to the increasing accessibility of herbal medicine worldwide.