There is an escalating need to find alternative sources of energy. The focus on sustainability encourages the search for renewable energy sources like biofuels. One such biofuel production can be generated from the Jatropha Curcas plant. In recent times, Computer Vision and Artificial Intelligence approaches have been adopted by researchers to overcome the task of disease detection helping in increasing production. Therefore, in the proposed method techniques such as the Mean Shift algorithm, Filtering (Gaussian Blur), Edge detection (Canny), and Contour analysis have been utilized for the disease detection from the images of Jatropha plant leaf. The model using the jatropha leaf dataset achieved an accuracy of 96.93%. Along with this our proposed method also evaluated the perimeter of the leaf with the total and infected area as well as the percentage of infected region and also notified whether the leaf is healthy or diseased.