Color Features and KNN in Classification of Raw Arecanut images

Abstract

Arecanut is one of the important cash crops of Southern India. Classification of raw arecanut is one of the major tasks in grading, which is a vital part of crop management. In this work we proposed a model which classifies the raw arecanut. We used color histogram and color moments as features with K-NN classifier. Experiment is conducted on a dataset of 800 images of four classes using two color features and four distance measures with K-NN. A classification accuracy of 98.13% is achieved for 20% training with K value of 3 and Euclidean distance measure for color histogram features.