Research on the classification method of different quality dry alfalfa based on scanning electron microscopy (SEM) image texture analysis
This study proposes a new method to distinguish between the different qualities of dry alfalfa. This method uses scanning electron microscopy (SEM) to obtain grayscale images, and. then, using an equalized histogram, the gray-level co-occurrence matrix (GLCM) extracts 14 texture features. The textur...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-01-01
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| Series: | Cogent Food & Agriculture |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311932.2019.1697073 |
| _version_ | 1818623877390008320 |
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| author | Gaofeng Chen Guifang Wu |
| author_facet | Gaofeng Chen Guifang Wu |
| author_sort | Gaofeng Chen |
| collection | DOAJ |
| description | This study proposes a new method to distinguish between the different qualities of dry alfalfa. This method uses scanning electron microscopy (SEM) to obtain grayscale images, and. then, using an equalized histogram, the gray-level co-occurrence matrix (GLCM) extracts 14 texture features. The texture feature vector is processed by principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce data redundancy and extract the best features. Finally, a back propagation neural network (BPNN) algorithm, an artificial neural network (ANN) and a least square support vector machine (LS-SVM) classification model are established to evaluate the classification effect. The results show that LDA is more effective in transforming the original data. In addition, LDA-based classification results are better than PCA-based classification results, and the recognition rate is 100% accurate. In contrast, the reliability and potential of extracting the main information based on LDA are shown. Based on these conclusions, it is possible to identify various types of alfalfa after different drying methods. |
| first_indexed | 2024-12-16T18:48:03Z |
| format | Article |
| id | doaj.art-64f128c319c544d6a089939014cd7b12 |
| institution | Directory Open Access Journal |
| issn | 2331-1932 |
| language | English |
| last_indexed | 2024-12-16T18:48:03Z |
| publishDate | 2019-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Food & Agriculture |
| spelling | doaj.art-64f128c319c544d6a089939014cd7b122022-12-21T22:20:47ZengTaylor & Francis GroupCogent Food & Agriculture2331-19322019-01-015110.1080/23311932.2019.16970731697073Research on the classification method of different quality dry alfalfa based on scanning electron microscopy (SEM) image texture analysisGaofeng Chen0Guifang Wu1Inner Mongolia Agricultual UniversityInner Mongolia Agricultual UniversityThis study proposes a new method to distinguish between the different qualities of dry alfalfa. This method uses scanning electron microscopy (SEM) to obtain grayscale images, and. then, using an equalized histogram, the gray-level co-occurrence matrix (GLCM) extracts 14 texture features. The texture feature vector is processed by principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce data redundancy and extract the best features. Finally, a back propagation neural network (BPNN) algorithm, an artificial neural network (ANN) and a least square support vector machine (LS-SVM) classification model are established to evaluate the classification effect. The results show that LDA is more effective in transforming the original data. In addition, LDA-based classification results are better than PCA-based classification results, and the recognition rate is 100% accurate. In contrast, the reliability and potential of extracting the main information based on LDA are shown. Based on these conclusions, it is possible to identify various types of alfalfa after different drying methods.http://dx.doi.org/10.1080/23311932.2019.1697073scanning electron microscopy image of alfalfagray-level co-occurrence matrixprincipal component analysislinear discriminant analysisclassification model |
| spellingShingle | Gaofeng Chen Guifang Wu Research on the classification method of different quality dry alfalfa based on scanning electron microscopy (SEM) image texture analysis Cogent Food & Agriculture scanning electron microscopy image of alfalfa gray-level co-occurrence matrix principal component analysis linear discriminant analysis classification model |
| title | Research on the classification method of different quality dry alfalfa based on scanning electron microscopy (SEM) image texture analysis |
| title_full | Research on the classification method of different quality dry alfalfa based on scanning electron microscopy (SEM) image texture analysis |
| title_fullStr | Research on the classification method of different quality dry alfalfa based on scanning electron microscopy (SEM) image texture analysis |
| title_full_unstemmed | Research on the classification method of different quality dry alfalfa based on scanning electron microscopy (SEM) image texture analysis |
| title_short | Research on the classification method of different quality dry alfalfa based on scanning electron microscopy (SEM) image texture analysis |
| title_sort | research on the classification method of different quality dry alfalfa based on scanning electron microscopy sem image texture analysis |
| topic | scanning electron microscopy image of alfalfa gray-level co-occurrence matrix principal component analysis linear discriminant analysis classification model |
| url | http://dx.doi.org/10.1080/23311932.2019.1697073 |
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