WSEAS Transactions on Computers

Print ISSN: 1109-2750
E-ISSN: 2224-2872

Volume 17, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Leaf Disease Detection using Clustering Optimization and Multi-Class Classifier

AUTHORS: S. Bhuvana, Kaviya Bharati B., Kousiga P., Rakshana Selvi S.

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ABSTRACT: Agriculture is the only passion to cultivate foods, raising a human’s life and animals by producing desired plant products. India ranked in the world's five largest producers of over 80% of agricultural produce items, including many cash crops such s rice, guava, tobacco, etc.Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing time. Consequently, image processing is used for the detection of plant diseases. The proposed system consist of following phases like: image preprocessing, image segmentation using otsu segmentation, clustering of an image using k-means, extract the feature using GLCM feature extraction, classify the image by Multi class SVM classifier. In compared to existing system, the proposed system significantly identify the plant leaf disease at an early disease and improve the accuracy to 98%.

KEYWORDS: image pre-processing, OTSU segmentation, K-means, GLCM, Multi Class SVM


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WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 17, 2018, Art. #32, pp. 260-268

Copyright © 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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