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 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.