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access icon free Efficient technique for rice grain classification using back-propagation neural network and wavelet decomposition

© The Institution of Engineering and Technology
Received 07/01/2016, Accepted 02/05/2016, Revised 09/04/2016, Published 05/05/2016

This study describes the classification of four varieties of bulk rice grain images using back-propagation neural network (BPNN). Eighteen colour features, 27 texture features using grey-level co-occurrence matrix, 24 wavelet features and 45 combined features (combination of colour and texture) were extracted from the colour images of bulk rice grains. Classification was carried out on three different data set of images under different environmental conditions. It is seen that BPNN is able to classify faithfully the four varieties of rice grain even with a poor image quality. It is also found that classification based on reduced wavelet features outperform the classification using all other features (such as colour, texture features taken separately) for two data set of images with minimum resolution. The authors have further compared the proposed BPNN technique with other classifiers such as support vector machine, k-nearest neighbour and naive Bayes classifier on all the three data sets. It is found that the average classification accuracy of more than 96% was able to achieve using BPNN consistently on all different features for each data set.

Inspec keywords: neural nets; image colour analysis; image classification; backpropagation; wavelet transforms; agricultural products; image texture

Other keywords: support vector machine; BPNN technique; naive Bayes classifier; environmental conditions; colour features; wavelet decomposition; texture features; image quality; backpropagation neural network; feature extraction; rice grain classification; colour images; grey-level cooccurrence matrix; bulk rice grain images; k-nearest neighbour; wavelet features

Subjects: Mathematical analysis; Neural computing techniques; Integral transforms; Computer vision and image processing techniques; Image recognition; Agriculture, forestry and fisheries computing; Integral transforms; Agriculture

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