Tomato Plant Leaf Disease Detection using CNN

Abstract

Agriculture is a vital revenue source for a developing nation like India. India is the world's second-largest tomato producer. Tomatoes are the world's most common crop, and they can be found in any kitchen in various ways, regardless of the cuisine. It is the third most widely grown crop after potato and sweet potato. However, owing to numerous diseases, the standard and abundance of the tomato crop suffers. Leaves are essential for rapid growth of any plant and increased crop production. Farmers and researchers alike face difficulties in identifying diseases in plant leaves. Leaf diseases in plants must be identified at an early stage and predictive methods implemented to make them stable and prevent losses to the agri-based industry. A Convolution Neural Network (CNN) based approach is used for disease identification and classification. The classified accuracy ranges from 76% to 100% depending on the class, and the proposed model's average accuracy is 98.2 % for the nine disease and one healthy class.