EARLY PEST DETECTION FROM CROP USING IMAGE PROCESSING AND COMPUTATIONAL INTELLIGENCE

Abstract

One of the major obstacles in agriculture is dealing with insect infestations as soon as they occur. While pesticides can kill insects, using too much of them may be harmful to people and the environment. By combining physical and biological means, integrated pest control seeks to avoid parasite infestations. When it comes to protecting plants and increasing their yield, digital image processing is an essential tool in agricultural research. Using digital video cameras to take pictures of infected leaves, this research investigates a novel method for early parasite diagnosis. The presence of insects on leaves may be identified and reduced to grayscale pictures
using image classification methods and attribute extraction. After that, feature extraction methods are applied to these pictures using MATLAB software. In order to implement more precise pest management plans, Support Vector Machine classifiers are used to determine the specific pest species that have been detected.