• Nayana K S
• Dr. M C Padma

Plant leaves are affected by infected microorganism such as bacteria, viruses, fungi. It can be affected by noninfectious factors, causing problems that can collectively be termed “abiotic diseases” or “abiotic disorders” . In a justifiable manner the home gardener concerned when the leaves of plants of their yard trees become diseased, especially when these disease cause the separation of ripened leaves from a branch, limb and bush death, and tree has been death by defoliated several years in a row perhaps. The concern home gardeners have an absolute requirement information of leaves disease and how to prevent the plant leaves diseases. In this paper the enhanced method for leaf disease detection using an suitable approach. The algorithm presented is used to preprocess, segment and extract information from the preprocessed image. The various clusters are obtained using k-means algorithm to done segmentation. The affected region are extracted by shape feature and the color texture features using GLCM(gray-level co-occurrence matrix) and send to the SVM(support vector machine) classifier. Severity assessment is done to check the percentage of leaf region that has been affected by the disease and also Accuracy of detection can be increased when using SVM classifier with a lot of variety of options enclosed there too.

GLCM(Gray Level Co-occurance Matrice), SVM (Support Vector Machine), Diseases : Cercospora leaf spot, Alterneria alternate, Bacterial blight, Anthracnose.

"Detection of Affected Region and Classification of Plant Leaves Diseases using Image Processing Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.633-637, May-2019, Available :http://www.jetir.org/papers/JETIR1905M97.pdf

"Detection of Affected Region and Classification of Plant Leaves Diseases using Image Processing Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp633-637, May-2019, Available at : http://www.jetir.org/papers/JETIR1905M97.pdf