In a developing country like India agriculture plays a noteworthy role. Agricultural intervention in the livelihood of rural India indulges by about 58%. Thus, preventing significant loss in quantity and yield of these plants is important and majorly dependent on recognition and classification of diseases those plants might possess. Advanced and developing technologies like Image processing are used to classify such issues using different types of algorithms and techniques. Initially, the leaf of a plant gets affected, when plant develops a particular type of disease. In this project, four consecutive stages are used to discover the type of disease. The four stages consist of pre-processing, segmentation, extraction of features and their classification. To remove the noise we are doing the pre-processing and to part the affected or damages area of the leaf, image segmentation is used. The k-nearest neighbors (KNN) algorithm, which is a guided, supervised and advance machine learning algorithm, is implemented to find solutions for both the problems related to classification and regression. During the terminal stage, user is recommended treatment that might help. Mostly live plants are adversely affected by the diseases. This paper conveys representation of leaf disease detection by using image processing that can identify drawbacks in the said plant by inputting images, based on color, bound and texture to give the brisk and reliable results to the farmer.