Performance Evaluation of Classification Techniques for Computer Vision based Cashew Grading System

Abstract

The performance of various classification algorithms greatly depends on the characteristics of the data to be classified. There is no single classifier that works best on all given problems. The purpose of this study is to develop the computer vision based cashew grading system in conjunction with most accurate classification technique. The performance of different classification techniques including Multi-Layer Perceptron, Naive Bayes, K-Nearest Neighbor, Decision tree, Support Vector Machine are evaluated using WEKA toolbox to have most suitable classification technique for the cashew grading system. Subsequently, the classification technique that has the potential to significantly improve the performance of the system is suggested to be utilized in cashew grading system.