Swarm Intelligence Based Detection of Citrus Plant Diseases and Their Severity Level

Abstract

The quality of food production is reduced directly through plant diseases. The citrus plants are widely grown fruits worldwide. Each year, a large amount of waste is produced by citrus manufacturers, who annually destroy 50 percent of the citrus peel due to various plant diseases. The discovery of citrus plant diseases and their severity level will improve the quality of agricultural production. Image processing techniques are widely for detection of citrus plant disease. In this paper, evolutionary algorithms are introduced to detect citrus plant diseases and their severity level. The leaf images of citrus plants are collected and those images are pre-processed by removing noise using filtering technique. Then, the diseased portion in the leaf image is extracted by partitioning the image into multiple segments. From the segmented images, the features such as contrast, color, energy, local homogeneity, cluster shade and prominence are extracted using co-occurrence method and these features are processed in GA and PSO to detect the citrus plant diseases and their severity level. In GA, each gene randomly selects the features and classification rule is formed by chromosome. In PSO, each particle randomly selects the features and classification rule is generated. The best classification rule is selected based on the classification accuracy of selected classification rule. After the selection of best classification rule, it is applied to the testing data to detect citrus plant diseases and their severity level.