Tomato Leaf Disease Detection and Classification using Convolution Neural Network

Abstract

Mostly development of country depends of growth of agriculture sectors. Now a day agriculture is facing lot of challenges like unavailability of labor, drastic climate change, uncertainty in rain, natural disaster, different diseases on plant leaf and crops, no fixed prices and unavailability of markets and many more. But as the world continuously increasing the demands of food and more production needed in next 50 years. There are huge numbers of threats in agriculture field. The use of artificial intelligence technology, found best for the all agriculture challenges. So our proposed research focus on detecting and classifying the accurate type of diseases occurred on leaf at early stage. Our research aims to address the problem using the Deep Learning (DL) techniques. The AgroDeep mobile application developed for collection of real database of agriculture leafs and crop. The real diseased leaf images collected and captured through our mobile application. The captured images uploaded over database. There are total six types of crops leaf images (tomato, grapes, soybean, sugarcane, cotton and onion) collected. The tomato diseased leaf selected for detection and classification. The techniques supported whether diseases affected on leaf or not with percentage of accuracy. The best Convolution Neural Network (CNN) algorithm suitable for these analysis. The CNN based model gave the highest accuracy of 97 % which is highest forever for real captured diseased images. Our research playing exquisite role in agriculture sector and farmers. The proposed research supported to increase food production in the agriculture. Ultimately it gives more profit in the farming sector which motivate the farmers for agriculture.