Plant Leaf Disease Detection and Classification Using Particle Swarm Optimization

Abstract

The loss of crops due to diseases is a major danger to food security. It is important to develop the requisite infrastructure and tools for the detection of diseases in crops. The opportunity to detect diseases in crops has increased manifolds with the rise in the number of smartphone users and improved network connectivity. In this paper, we provide an approach to detect and classify plant leaf diseases. The methodology involves image acquisition, pre-processing of the images, feature extraction followed by feature selection and finally the classification of plant diseases. A deep convolutional neural network was trained to extract features from the input image. An optimal set of features is selected using Particle Swarm Optimization (PSO) and are classified into 23 different classes, including both healthy and diseased categories. Apropos, by employing this technique, the plant leaf images are classified with an accuracy of 97.39%.