Machine Learning Approach towards Tomato Leaf Disease Classification
Gadade H.D.
Abstract
India in an agricultural country and the detection of diseases in first stage is very important to increase the crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. In this paper, to detect symptoms of disease, we have developed a module that classifies the plant leaf disease automatically. This paper presents a performance measure for different feature extraction techniques for tomato leaf disease detection including GLCM, Gabor and SURF and classification techniques including decision trees, SVM, KNN and Nave Bayes. The dataset contains 500 images of tomato leaves with seven symptoms of diseases. We have modeled a system for automatic feature extraction and classification. We have evaluated the performance of the system using different performance measures to conclude with appropriate features set and classification technique for tomato leaf disease classification. The experimental results validate that Gabor features effectively recognizes different types of tomato leaf diseases. Accuracy of SVM is better as compared to other classification techniques but the execution time is more.