CLASSIFICATION OF RICE GRAIN VARIETIES USING TWO ARTIFICIAL NEURAL NETWORKS (MLP AND NEURO-FUZZY)
A. R. Pazoki 1* , F. Farokhi2 , Z. Pazoki 3
1,* Department of Agronomy and Plant breading, Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran
2 Department of Electrical & Electronic Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3Young Researchers and Elite Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Corresponding author email: firstname.lastname@example.org
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.
Key words: Artificial neural networks (ANNs), Grain, Multi layer perceptron (MLP), Neuro-Fuzzy, Rice