Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 136, Issue -, Pages 157-165Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2017.03.004
Keywords
Image processing; FCM; Target leaf; Neighborhood grayscale; Weighted method
Funding
- Beijing Innovation Consortium of Agriculture Research System [BAIC07-2017]
- Tianjin Science and Technology Support Program [15ZCZDNC00120]
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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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