Canker Detection in Citrus Plants with an Efficient Finite Dissimilar Compatible Histogram Leveling Based Image Pre-processing and SVM Classifier

Abstract

The massive loss in agricultural yield mainly caused by leaf and fruit diseases. These diseases reduce both quality and quantity of agricultural products. As a major provenience of nutrients like vitamin C, citrus plants such as lemons, mandarins, oranges, tangerines, grapefruits, and limes are commonly grown fruits all over the world. As a result of various plant diseases, citrus producing companies make a huge amount of waste every year whereby 50% of citrus peel is destroyed every year. The disease canker is one of the mentionable leaf and fruit diseases. The main goal of this paper is to recognize and classify the canker disease precisely from the contrived leaf images by employing image processing techniques to detect plant leaf diseases from digital images. We offer a method consists of two phases to enhance the clarity of leaf images. The primary stage uses Finite Dissimilar Compatible Histogram Leveling (FDCHL) in preliminary step which advances the dissimilar level of disease influenced leaf image, segment the region of interest using fuzzy feature selection. The second phase by adopting the Support Vector Machine classifier to find out the canker leaf image and implements these methods in lemon citrus canker disease identification. Experimental results show effective accuracy detection and reduced execution time of canker disease detection.