Texture Analysis for Rice Grain Classification Using Wavelet Decomposition and Back Propagation Neural Network

Abstract

This paper deals with the classification of eight types of rice grain using image processing and neural network. Three different texture feature extraction schemes based on co-occurrence matrix, run length matrix and wavelet decomposition were considered. The contribution of these texture feature extraction techniques towards rice grain classification were analysed and compared. A back propagation neural network is used for this classification task. The performance in terms of classification accuracy of the above three texture feature extraction schemes where tested. It is found that texture feature based on wavelet decomposition is able to classify eight different types of rice grain with an overall classification accuracy of 98.87% as compared to other texture feature extraction schemes discussed in this paper.