In Farming and Gardening, leaf diseases have grown to be a challenge as it can cause considerable reduction in both quality and measure of agricultural yields. Thus, automated recognition of diseases on leaves plays a vital role in Farming and Gardening sector. This Thesis imparts a simple and computationally dexterous method used for leaf disease identification and grading using digital image processing and machine learning method. The proposed system is divided into two phases, in the first phase the plant is recognized on the basis of the features of leaf, it includes pre-processing of leaf images, and feature extraction followed by Support vector machine and decision tree based training. In the second phase the disease present in the leaf is classified, this process includes K-Means based segmentation of defected area, feature extraction of the defected portion and the Support vector machine and decision tree based classification of disease. Then the disease grading is done on the basis of the amount of disease present in the leaf.