• Vinothpriya M
• Meena K

In the field of agriculture, the detection of leaf disease is very important. The loss of food is mainly due to infected leaf, which reflexively reduces the production rate. The main challenge is to cut back the usage of pesticides within the agricultural field and to extend the standard amount of the assembly rate keeping track of health and identifying of illness of plant leaves in early stage is extremely crucial in agriculture. Identifying plant disease manually is extremely difficult because it desires heap of labor, solely the expertise are able to determine the disease. In this proposed methodology an enhanced k-means clustering algorithm to predict the infected area of the leaves. A color based segmentation model is defined as the infected region and placing it to its suitable classes. Leaf diseases can be detected by image processing technique. Disease detection involves steps like image pre-processing, clustering, Image and segmentation. The experimental result shows the affected part of the leaf in percentage.

Leaf Disease. K-means clustering, Image Segmentation.

"DETECTION OF LEAF DISEASES USING K-MEANS CLUSTERING ALGORITHM IN MATLAB ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.19-24, April-2019, Available :http://www.jetir.org/papers/JETIRBI06004.pdf

"DETECTION OF LEAF DISEASES USING K-MEANS CLUSTERING ALGORITHM IN MATLAB ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp19-24, April-2019, Available at : http://www.jetir.org/papers/JETIRBI06004.pdf