Lemon Leaf Disease Detection and Classification using SVM and CNN

India is a developing country and agriculture has always played a major role in bolstering the country’s economic growth. Due to various factors like industrialization, mechanization and globalization, the green fields are facing complications. So, identifying the plant disease incorrectly will lead to a huge loss of both quantity and quality of the product and it will also incur loss in time and money. Hence, identifying the condition of the plant plays a major role for successful cultivation. Now a day’s image processing technique is being employed as a focal technique for diagnosing the various features of the crop. The image processing techniques can be used for identification of the plant disease and hence classify the plant disease. Generally, the symptoms of the disease are observed on leaves, stems, flowers etc. Here, the leaves of the affected plant are used for the identification and classification of the disease. Leaf image is captured using a smart phone as the first step and then they are processed to determine the condition of the plant. Identification of plant disease follows the steps like loading the image of the plant leaf, histogram equalization for enhancing contrast of the image, segmentation process by using Lab color space model, extracting features of the segmented image using GLCM (Grey Level Co- occurrence Matrix) and finally classification of leaf disease by using MCSVM (Multi Class Support Vector Machine).This procedure obtained an accuracy percentage of 83.6%.Also, it takes long training time for large datasets. To improve the accuracy of the detection and the classification of the plants, Convolutional Neural Network (CNN) is used. The main advantage of CNN is that it automatically detects the main features of the input without any supervision of human. In CNN identification of disease follow the steps like loading the image as the input image, convolution of the feature map and finally max pooling the layers to calculate the features of the image in detail. The plant diseases are classified with an accuracy of 93.8 %.