Diagnosis of Tomato Plant Diseases using Random Forest

Abstract

Plant diseases are responsible for economic loss in agricultural industry, as they destroy the crops. Although pesticides have been used to modify crop production, the excess amount of use of pesticides affects the environment negatively. Hence detection of diseases and differentiating it from nutritional deficiency has a considerable impact in deciding the requirement of pesticides. The conventional methods for plant diseases identification involve tedious chemical processes to be carried out in the laboratory and is also time consuming. This paper presents an automated system for identification and classification of plant diseases using Machine Learning (ML) and image processing technique. The feature extraction method is applied on images for training the algorithm. The skill of different Machine Learning algorithms is evaluated using the training data to find the best suiting algorithm for disease identification. The test folder contains the unseen images and is used for validating the performance of system in identification of plant diseases. The overall accuracy of the system is 95 percent. The system can be trained with large amount of images and yields accurate results at a faster rate.