Origin Intelligent Identification of <i>Angelica sinensis</i> Using Machine Vision and Deep Learning
The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of <i>Angelica si...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/9/1744 |
| _version_ | 1827727677873717248 |
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| author | Zimei Zhang Jianwei Xiao Shanyu Wang Min Wu Wenjie Wang Ziliang Liu Zhian Zheng |
| author_facet | Zimei Zhang Jianwei Xiao Shanyu Wang Min Wu Wenjie Wang Ziliang Liu Zhian Zheng |
| author_sort | Zimei Zhang |
| collection | DOAJ |
| description | The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of <i>Angelica sinensis</i> (<i>A. sinensis</i>) from eight areas including 1859 samples. The effects of different datasets, learning rates, solver algorithms, training epochs and batch sizes on the performance of the deep learning model were evaluated. The optimized hyperparameters of the model were the dataset 4, learning rate of 0.001, solver algorithm of rmsprop, training epochs of 6, and batch sizes of 20, which showed the highest accuracy in the training process. Compared to support vector machine (SVM), K-nearest neighbors (KNN) and decision tree, the deep learning-based algorithm could significantly improve the prediction performance and show better robustness and generalization performance. The deep learning-based model achieved the highest accuracy, precision, recall rate and F1_Score values, which were 99.55%, 99.41%, 99.49% and 99.44%, respectively. These results showed that deep learning combined with machine vision can effectively identify the origin of <i>A. sinensis</i>. |
| first_indexed | 2024-03-10T23:09:45Z |
| format | Article |
| id | doaj.art-ae3cf166d0a044ff94d049cf94746a7e |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T23:09:45Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-ae3cf166d0a044ff94d049cf94746a7e2023-11-19T09:06:42ZengMDPI AGAgriculture2077-04722023-09-01139174410.3390/agriculture13091744Origin Intelligent Identification of <i>Angelica sinensis</i> Using Machine Vision and Deep LearningZimei Zhang0Jianwei Xiao1Shanyu Wang2Min Wu3Wenjie Wang4Ziliang Liu5Zhian Zheng6College of Engineering, China Agricultural University, Beijing 100083, ChinaBeijing Institute of Aerospace Testing Technology, Beijing 100074, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaChinese Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaThe accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of <i>Angelica sinensis</i> (<i>A. sinensis</i>) from eight areas including 1859 samples. The effects of different datasets, learning rates, solver algorithms, training epochs and batch sizes on the performance of the deep learning model were evaluated. The optimized hyperparameters of the model were the dataset 4, learning rate of 0.001, solver algorithm of rmsprop, training epochs of 6, and batch sizes of 20, which showed the highest accuracy in the training process. Compared to support vector machine (SVM), K-nearest neighbors (KNN) and decision tree, the deep learning-based algorithm could significantly improve the prediction performance and show better robustness and generalization performance. The deep learning-based model achieved the highest accuracy, precision, recall rate and F1_Score values, which were 99.55%, 99.41%, 99.49% and 99.44%, respectively. These results showed that deep learning combined with machine vision can effectively identify the origin of <i>A. sinensis</i>.https://www.mdpi.com/2077-0472/13/9/1744<i>Angelica sinensis</i>origin identificationdeep learningmachine vision |
| spellingShingle | Zimei Zhang Jianwei Xiao Shanyu Wang Min Wu Wenjie Wang Ziliang Liu Zhian Zheng Origin Intelligent Identification of <i>Angelica sinensis</i> Using Machine Vision and Deep Learning Agriculture <i>Angelica sinensis</i> origin identification deep learning machine vision |
| title | Origin Intelligent Identification of <i>Angelica sinensis</i> Using Machine Vision and Deep Learning |
| title_full | Origin Intelligent Identification of <i>Angelica sinensis</i> Using Machine Vision and Deep Learning |
| title_fullStr | Origin Intelligent Identification of <i>Angelica sinensis</i> Using Machine Vision and Deep Learning |
| title_full_unstemmed | Origin Intelligent Identification of <i>Angelica sinensis</i> Using Machine Vision and Deep Learning |
| title_short | Origin Intelligent Identification of <i>Angelica sinensis</i> Using Machine Vision and Deep Learning |
| title_sort | origin intelligent identification of i angelica sinensis i using machine vision and deep learning |
| topic | <i>Angelica sinensis</i> origin identification deep learning machine vision |
| url | https://www.mdpi.com/2077-0472/13/9/1744 |
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