Classification of Rice Grains Using Neural Networks

Abstract

ABSTRACT This paper presents a neural network approach for classification of rice varieties. A total of 9 different rice verities were considered for the study. Samples were drawn from each variety and images of seeds were captured. Algorithms were developed to extract thirteen morphological features, six colour features and fifteen texture features from colour images of individual seed samples. A different neural network models were developed for individual feature sets and for the combined feature set. High classification accuracy was given by textural features than morphological and colour features. An overall classification accuracy of 92% was obtained from combined feature model. Individual classification accuracies of AT307, BG250, BG358, BG450, BW262, BW267, W361, BW363 and BW364 were 94%, 98%, 84%, 100%, 94%, 68%, 98%, 94% and 94% respectively. It was noted that different neural network architectures tend to produce different accuracies.