Classification of Grape Leaves using KNN and SVM Classifiers
Anil A. Bharate, M. S. Shirdhonkar
Abstract
As it is known, in today's world gross domestic product (GDP) determines the prosperity of a nation. Agriculture directly adds to the GDP, subsequently incredible endeavors are to be made for its advancement. Automation is the key for the development of agriculture as there is lack of specialists in this field. So, automation will be a boon for farmers to prevent their plants from diseases and increase the yield. The proposed work includes applying techniques of image processing to automatically classify grape leaves in to healthy and non-healthy. Features such as color and texture are obtained from the leaf image and classifiers such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to classify the given grape leaf. It is discovered that for real time images, KNN (for K=1) gives better accuracy compared with SVM. The accuracy of proposed system is accomplished as 90% for SVM classifier and 96.66% for KNN classifier.