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Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion
The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in th...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659782/ https://www.ncbi.nlm.nih.gov/pubmed/34883948 http://dx.doi.org/10.3390/s21237945 |
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author | Zhu, Yinlong Zhang, Fujie Li, Lixia Lin, Yuhao Zhang, Zhongxiong Shi, Lei Tao, Huan Qin, Tao |
author_facet | Zhu, Yinlong Zhang, Fujie Li, Lixia Lin, Yuhao Zhang, Zhongxiong Shi, Lei Tao, Huan Qin, Tao |
author_sort | Zhu, Yinlong |
collection | PubMed |
description | The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production. |
format | Online Article Text |
id | pubmed-8659782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86597822021-12-10 Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion Zhu, Yinlong Zhang, Fujie Li, Lixia Lin, Yuhao Zhang, Zhongxiong Shi, Lei Tao, Huan Qin, Tao Sensors (Basel) Article The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production. MDPI 2021-11-28 /pmc/articles/PMC8659782/ /pubmed/34883948 http://dx.doi.org/10.3390/s21237945 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhu, Yinlong Zhang, Fujie Li, Lixia Lin, Yuhao Zhang, Zhongxiong Shi, Lei Tao, Huan Qin, Tao Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion |
title | Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion |
title_full | Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion |
title_fullStr | Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion |
title_full_unstemmed | Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion |
title_short | Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion |
title_sort | research on classification model of panax notoginseng taproots based on machine vision feature fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659782/ https://www.ncbi.nlm.nih.gov/pubmed/34883948 http://dx.doi.org/10.3390/s21237945 |
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