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.
