AN ENHANCED TOMATO PLANT DISEASE DETECTION AND CLASSIFICATION METHODOLOGY USING CNN

Abstract: Agriculture plays an important role in the growth of a country; also, economic growth relies on the quality of the crops produced which is proportional to the diseases occurring on it. The problem occurs when the leaves of the plants get affected by multiple diseases, which requires a solution by accurately detecting the disease. Here we will be taking tomato leaves with multiple diseases into consideration. The research is based on CNN based architecture VGG16 which helps to achieve accuracy above 92% when performed on tomato leaves dataset which consist of eleven classes. PlantVillage and Tomato Leaf Diseases are two dataset sources for the collection of images for the model.

Keywords: Convolution Neural Network, VGG16, Tomato leaf diseases detection, Tomato leaf diseases classification