CLASSIFICATION OF RICE GRAIN VARIETIES USING TWO ARTIFICIAL NEURAL NETWORKS (MLP AND NEURO-FUZZY) Authors: A. R. Pazoki 1, F. Farokhi, Z. Pazoki Journal: Journal of Animal and Plant Sciences (JAPS) ISSN: 1018-7081 (Print), 2309-8694 (Online) Volume: 24 Issue: 1 Pages: 336-343 Year: 2014 DOI: NA URL: https://doi.org/NA Publisher: Pakistan Agricultural Scientists Forum Abstract:

Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological features and 4 shape factors that were extracted from color images of each grain of rice. The rice grains were then classified according to variety by multi layer perceptron (MLP) and neuro-fuzzy neural networks. The topological structure of the MLP model contained 39 neurons in the input layer, 5 neurons (Khazar, Gharib, Ghasrdashti, Gerdeh and Mohammadi) in the output layer and two hidden layers; neuro-fuzzy classifier applied the same structure in input and output layers with 60 rules. Average accuracy amounts for classification of rice grain varieties computed 99.46% and 99.73% by MLP and neuro-fuzzy classifiers alternatively. The accuracy of MLP and neuro-fuzzy networks changed after feature selections were 98.40% and 99.73 % alternatively.

Keywords: Artificial neural networks (ANNs), Grain, Multi layer perceptron (MLP), Neuro-Fuzzy, Rice