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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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 |
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author | Sun, Xin Qian, Huinan Xiong, Yiliang Zhu, Yingli Huang, Zhaohan Yang, Feng |
author_facet | Sun, Xin Qian, Huinan Xiong, Yiliang Zhu, Yingli Huang, Zhaohan Yang, Feng |
author_sort | Sun, Xin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9023495 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90234952022-04-25 Deep learning-enabled mobile application for efficient and robust herb image recognition Sun, Xin Qian, Huinan Xiong, Yiliang Zhu, Yingli Huang, Zhaohan Yang, Feng Sci Rep Article 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. Nature Publishing Group UK 2022-04-21 /pmc/articles/PMC9023495/ /pubmed/35449192 http://dx.doi.org/10.1038/s41598-022-10449-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Sun, Xin Qian, Huinan Xiong, Yiliang Zhu, Yingli Huang, Zhaohan Yang, Feng Deep learning-enabled mobile application for efficient and robust herb image recognition |
title | Deep learning-enabled mobile application for efficient and robust herb image recognition |
title_full | Deep learning-enabled mobile application for efficient and robust herb image recognition |
title_fullStr | Deep learning-enabled mobile application for efficient and robust herb image recognition |
title_full_unstemmed | Deep learning-enabled mobile application for efficient and robust herb image recognition |
title_short | Deep learning-enabled mobile application for efficient and robust herb image recognition |
title_sort | deep learning-enabled mobile application for efficient and robust herb image recognition |
topic | Article |
url | 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 |
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