Image segmentation in Diagnosing the Ground Bud Necrosis Virus in Tomatoes using K-Means Clustering

Abstract

Early-stage fruit disease detection will ensure the natural product quality for the organic agriculture business. The potential of using K-Means segmentation for diagnosing tomatoes fruit disease was intended to be explored by this proposed method. The main goal of paper is to increase classification accuracy by locating tomatoes with Ground Bud Necrosis Virus in Tomatoes disease using an image segmentation approach. The K-means clustering algorithm is intended to boost segmentation effectiveness. In the end product, the images are divided into three classes: Grade 0—00-15%; Grade 1—16-35%; Grade 2—36-65%; Grade 3—66-85%; and Class 4—86-100%. Moreover, the tested results of the proposed approach explore a variety of unhealthy images and disease Tomatoes and demonstrate that, when compared to existing methods, the proposed method has the highest accuracy.