Adaptive K-Means Clustering with Multi Color Space Fusion for Robust Leaf Disease Segmentation and Severity Quantification
Journal of Smart and Sustainable Farming
Received On : 10 November 2025
Revised On : 18 December 2025
Accepted On : 30 December 2025
Published On : 06 January 2026
Volume 02, 2026
Pages : 001-011
Abstract
The identification and quantification of diseases in leaves must be accurate to increase crop productivity and precision agriculture. However, the classical segmentation approaches normally adopt the depiction in single color space and standard clustering algorithms and generally are feeble in the context of varying lighting and compound leaf textures. To address them, this paper will recommend an adaptive K-means clustering system with multi-color space fusion which will be viable in terms of leaf disease segmentation and the severity of the disease. The specified strategy combines the strengths of RGB, HSV, and CIELab colour spaces into the form of a hybrid strategy that enhances the level of discrimination. The optimal number of clusters is estimated by a dynamic adaptive K-means algorithm to facilitate the centroid start-up and reliable separation of different samples. The other component that is incorporated in the framework is the severity quantification module which entails a pixel level analysis to ascertain the disease progression with greater precision. They are tested on the images of potato and tomato leaves obtained as a part of the PlantVillage Dataset and other samples to verify the hypothesis. The segmentation accuracy of the proposed is 97.2, Dice coefficient is 0.94 and Intersection-over-Union (IoU) is 89.6, which is more than 10 percent higher than the conventional clustering methods. Moreover, the error in the severity estimation is kept to a minimum of less than 3 percent which is very reliable. The findings prove that the proposed framework offers a computationally effective, precise, and scalable solution to the automated plant disease analysis in real-world agricultural systems.
Keywords
Adaptive K-Means Clustering, Multi-Color Space Fusion, Leaf Disease Segmentation, Severity Quantification, Precision Agriculture, Plant Village Dataset.
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The authors confirm contribution to the paper as follows:
Conceptualization: Anandakumar Haldorai; Methodology: Anandakumar Haldorai; Software: Bui Hong Quang; Data Curation: Bui Hong Quang; Writing- Original Draft Preparation: Anandakumar Haldorai and Bui Hong Quang; Investigation: Anandakumar Haldorai and Bui Hong Quang; Supervision: Anandakumar Haldorai and Bui Hong Quang; Validation: Anandakumar Haldorai and Bui Hong Quang; Writing- Reviewing and Editing: Anandakumar Haldorai and Bui Hong Quang. All authors reviewed the results and approved the final version of the manuscript.
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Cite this Article
Anandakumar Haldorai and Bui Hong Quang, “Adaptive K-Means Clustering with Multi Color Space Fusion for Robust Leaf Disease Segmentation and Severity Quantification”, Journal of Smart and Sustainable Farming, pp. 001-011, 2026, doi: 10.64026/JSSF/2026001.