An enhancement in classifier support vector machine to improve plant disease detection

This proposed work is about automatic detection of diseases and diseased part present in the leaf images of plants and even in the agriculture Crop production. It is done with advancement of computer technology which helps in farming to increase the production. Mainly there is problem of detection accuracy and in neural network approach support vector machine (SVM) is latest classifier of that approach. In this paper, SVM is implemented which contains two datasets; one is training dataset and train dataset. Firstly original image is captured and then it is being used for processing. Secondly it gives us the black and background pixels of image segmented and also separate the hue part and saturation part of image. Thirdly detection of disease and diseased part of image is detected and healthy part is segmented from it. This work will also provide % of area in which diseases is present and give us the name of disease. As in the results of one image diseased area is 5.56%. This work provides accuracy which is better as in proposed algorithm results.