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.
随着草药的日益普及,高标准的草药质量控制成为必要,其中草药识别是巨大挑战之一。由于草药的加工过程复杂,已采用了需要化学材料和专业知识的人工识别方法,如指纹识别和经验识别。自动方法可以通过基于深度学习的草药图像识别部分缓解该问题,但大多数研究需要强大且昂贵的计算硬件,这对资源有限的环境不太友好。在本文中,我们介绍了一种支持深度学习的移动应用程序,它可以完全在普通低成本智能手机上运行,以便在资源有限的情况下进行高效且稳健的草药图像识别,且识别准确率颇具竞争力。我们希望该应用程序能为全球范围内草药可及性的提高做出贡献。