Developing an algorithm for Tomato leaf disease detection and classification

Abstract: Tomato is widely cultivated economical crop in the India; so diseases in plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural products. Therefore, automatic detection of plant diseases is an essential research topic as it may prove benefits in monitoring fields of crops, and automatically detect the symptoms of diseases. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for detection and identification of plant diseases. Mostly diseases are seen on the leaves. Therefore, looking for fast, less expensive and accurate method to automatically detect the diseases from the symptoms that appear on the plant leaf is of great realistic significance. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies. Hence the algorithm is to design, implement and evaluate an image processing based software solution for automatic detection and classification of plant leaf diseases. The method used in this work is divided into two major phases. First phase concerns with training of healthy sample and diseased sample. Second phase concerns with the training of test sample and generates result based on the segmentation and feature extraction. And classifies the diseases into fungal, bacterial and viral. It also helps the farmer to take superior decision about many aspects of crop development process. Keywords: Image Processing, Image Segmentation, Feature Extraction, Disease Detection, Disease Classification.