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Deep learning-enabled mobile application for efficient and robust herb image recognition

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 material...

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Detalles Bibliográficos
Autores principales: Sun, Xin, Qian, Huinan, Xiong, Yiliang, Zhu, Yingli, Huang, Zhaohan, Yang, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023495/
https://www.ncbi.nlm.nih.gov/pubmed/35449192
http://dx.doi.org/10.1038/s41598-022-10449-9
Descripción
Sumario: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.