A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images

•Improving the extraction of cucumber leaf spot disease under complex backgrounds.•Redefining the feature distance between pixel xj and clustering center vi.•Calculating the two-dimensional neighborhood mean gray value as a sample point.•Proposing a new weighting method for gray value and neighborho...

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Vydané v:	Computers and electronics in agriculture Ročník 136; s. 157 - 165
Hlavní autori:	Bai, Xuebing, Li, Xinxing, Fu, Zetian, Lv, Xiongjie, Zhang, Lingxian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 15.04.2017 Elsevier BV
Predmet:	Algorithms apples Clustering Cucumbers disease diagnosis Evaluation FCM Grading Image processing Image segmentation leaf spot leaves Neighborhood grayscale Pixels Plant diseases Studies Target leaf Test procedures Weighted method FCM Weighted method Target leaf Image processing Neighborhood grayscale
ISSN:	0168-1699, 1872-7107
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Abstract	•Improving the extraction of cucumber leaf spot disease under complex backgrounds.•Redefining the feature distance between pixel xj and clustering center vi.•Calculating the two-dimensional neighborhood mean gray value as a sample point.•Proposing a new weighting method for gray value and neighborhood gray value. Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as ‖xj2-vi2‖. Third, the pixel's neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications.
AbstractList	Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as ‖xj2-vi2‖. Third, the pixel's neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications. Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as ... Third, the pixel's neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications. •Improving the extraction of cucumber leaf spot disease under complex backgrounds.•Redefining the feature distance between pixel xj and clustering center vi.•Calculating the two-dimensional neighborhood mean gray value as a sample point.•Proposing a new weighting method for gray value and neighborhood gray value. Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as ‖xj2-vi2‖. Third, the pixel's neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications.
Author	Fu, Zetian Li, Xinxing Zhang, Lingxian Bai, Xuebing Lv, Xiongjie
Author_xml	– sequence: 1 givenname: Xuebing surname: Bai fullname: Bai, Xuebing email: 464161695@qq.com organization: China Agricultural University, Beijing 100083, PR China – sequence: 2 givenname: Xinxing surname: Li fullname: Li, Xinxing organization: China Agricultural University, Beijing 100083, PR China – sequence: 3 givenname: Zetian surname: Fu fullname: Fu, Zetian organization: China Agricultural University, Beijing 100083, PR China – sequence: 4 givenname: Xiongjie surname: Lv fullname: Lv, Xiongjie organization: Information Institute, Tianjin Academy of Agricultural Sciences, Tianjin 300192, PR China – sequence: 5 givenname: Lingxian surname: Zhang fullname: Zhang, Lingxian email: zhanglx@cau.edu.cn organization: China Agricultural University, Beijing 100083, PR China
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Snippet	•Improving the extraction of cucumber leaf spot disease under complex backgrounds.•Redefining the feature distance between pixel xj and clustering center... Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM)...
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SubjectTerms	Algorithms apples Clustering Cucumbers disease diagnosis Evaluation FCM Grading Image processing Image segmentation leaf spot leaves Neighborhood grayscale Pixels Plant diseases Studies Target leaf Test procedures Weighted method
Title	A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images
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