Leaf Disease Detection using Clustering Optimization and Multi-Class Classifier

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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%.

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

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S. Bhuvana, Kaviya Bharati B., Kousiga P., Rakshana Selvi S., "Leaf Disease Detection using Clustering Optimization and Multi-Class Classifier," WSEAS Transactions on Computers, vol. 17, pp. 260-268, 2018, DOI:
S. Bhuvana, Kaviya Bharati B., Kousiga P., Rakshana Selvi S.. Leaf Disease Detection using Clustering Optimization and Multi-Class Classifier. WSEAS Transactions on Computers. 2018;17:260-268.
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