Abstract
Detecting diseases from the leaf images of a plant is an important and challenging task. Various image processing techniques like pre-processing, segmentation, classification, etc., are performed to detect plant diseases from its leaf images. Image segmentation is one of the important steps in the process of disease detection in leaf images of plants. A well segmented image increases the accuracy of prediction. In this paper, we have implemented the K-means algorithm to segment leaf images of tea infected with red rust disease caused by algae. The value of K in K-means needs to be set manually. Determining the optimum value of K is crucial to obtain a well segmented image. So, the elbow method and silhouette coefficient determination are employed for this purpose.
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Acknowledgements
This research work is being carried out with the help of fund received from AICTE NER-RPS for the project titled “Detection of Tea Leaf Pest Attack in Assam Tea Gardens” via Sanction Letter File No. 8-13/FDC/RPS (NER)/POLICY-1/2020-21 dated March 10, 2021.
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Das, A.K., Ahmed, S.S. (2024). Determination of Optimum K Value for K-means Segmentation of Diseased Tea Leaf Images. In: Deka, J.K., Robi, P.S., Sharma, B. (eds) Emerging Technology for Sustainable Development. EGTET 2022. Lecture Notes in Electrical Engineering, vol 1061. Springer, Singapore. https://doi.org/10.1007/978-981-99-4362-3_19
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DOI: https://doi.org/10.1007/978-981-99-4362-3_19
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