Ground Bud Necrosis Virus Identification in Tomato using Support Vector Machine

Abstract

Various diseases are mostly caused by bacteria, fungi, and viruses. The plants efficiency gets influenced if correct concern is not taken. Tomato is one of these general food crops. Ground bud necrosis virus infection on tomato shows symptoms like necrotic rings on leaf, stem necrosis, concentric rings and patchy colour on fruit. Such Ground Bud Necrosis Virus (GBNV) infection can be detected using Image processing technique. Image processing is upbringing method out of several technologies which is facilitating to determine such diseases with a variety of algorithms as well as techniques. In this paper the Support Vector Machine (SVM) based classifier is implemented to classify healthy tomato and tomato affected by GBNV. The idea of SVM is described for classification of images. The method is implemented with RGB values of pixel. 310 images are utilized to train classifier. The accuracy achieved is 84.72% which is improved than the classifiers like Maximum Likelihood Classifier (MLC) and Artificial Neural Network (ANN).