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

第一作者： Bai, Xuebing

作者： Bai, Xuebing;Li, Xinxing;Fu, Zetian;Zhang, Lingxian;Lv, Xiongjie;Bai, Xuebing;Li, Xinxing;Fu, Zetian;Li, Xinxing;Fu, Zetian;Zhang, Lingxian

作者机构：

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：

年卷期：

页码：

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.

分类号： S

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