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Gray level size zone matrix for rice grain classification using back propagation neural network: a comparative study

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Abstract

This paper presents classification of five different types of milled rice grain using various texture feature extraction models. Four different gray level based texture features extraction techniques are discussed in this work. The classification task is performed using an adaptive threshold back propagation neural network. The above four texture feature extraction techniques are compared with that of the proposed gray-level-size-zone matrix based rotation invariant texture model. The classification outcome of the proposed texture features extraction model is also validated through publicly available texture dataset from Brodatz’s database. Results show that classification task based on the proposed texture model is able to achieve higher accuracy both in rice and standard data as compare to other four different texture features extraction techniques discussed in this work. Results also show that back propagation neural network provides better accuracy of 99.4% when compared with other statistical classifiers presented in this work.

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  1. Electrical Engineering Department, Mizoram University, Aizawl, 796004, India

    Ksh. Robert Singh, Subir Datta & Subhasish Deb

  2. Electrical Engineering Department, National Institute of Technology Silchar, Silchar, 708810, India

    Saurabh Chaudhury

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  1. Ksh. Robert Singh
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  2. Saurabh Chaudhury
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  3. Subir Datta
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Correspondence to Ksh. Robert Singh.

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Singh, K.R., Chaudhury, S., Datta, S. et al. Gray level size zone matrix for rice grain classification using back propagation neural network: a comparative study. Int J Syst Assur Eng Manag 13, 2683–2697 (2022). https://doi.org/10.1007/s13198-022-01739-6

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