A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images
文献类型： 外文期刊
作者： Bai, Xuebing 1 ; Li, Xinxing 1 ; Fu, Zetian 1 ; Lv, Xiongjie 2 ; Zhang, Lingxian 1 ;
作者机构： 1.China Agr Univ, POB 209,Qinghuadonglu 17, Beijing 100083, Peoples R China
2.Tianjin Acad Agr Sci, Informat Inst, Tianjin 300192, Peoples R China
3.Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China
4.Minist Agr, Key Lab Agr Informationizat Standardizat Beijing, Beijing 100083, Peoples R China
关键词： Image processing;FCM;Target leaf;Neighborhood grayscale;Weighted method
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）
ISSN：
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收录情况： SCI
摘要： 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 xi and the cluster center v(i) is defined as vertical bar x(j)(2) - v(i)(2)vertical bar vertical bar. 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. (C) 2017 Elsevier B.V. All rights reserved.
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