Enhanced Convolution Neural Network for Tomato Leaf Disease Classification

Abstract

When plants and crops are affected by pests it affects the agricultural production of the country. Agricultural productivity depends heavily on the economy. This is one of the reasons why plant disease detection plays a major role in agriculture. Usually farmers or experts observe the plants with naked eye for detection and identification of disease. But this method can be time processing, expensive and inaccurate. Detection of crop disease using a few instantaneous strategy is helpful as it decreases comprehensive surveillance job in huge crop farms and locates disease side effects quite soon, i.e. if they tend on leaves and stems. Enhanced Convolutional neural networks (ECNN) have demonstrated great performance in object recognition and image classification problems. Using a public dataset images of infected and healthy Tomato leaves collected under controlled conditions, we trained a deep convolutional neural network to identify diseases in tomato. As the result, few diseases that usually occur in tomato plants such as Late blight, Gray spot and bacterial canker are detected.