RT Journal
T1 CLASSIFICATION OF RICE GRAIN VARIETIES USING TWO ARTIFICIAL NEURAL NETWORKS (MLP AND NEURO-FUZZY)
A1 A. R. Pazoki 1
A1 F. Farokhi
A1 Z. Pazoki
JF Journal of Animal and Plant Sciences
JO JAPS
SN 1018-7081
VO 24
IS 1
SP 336
OP 343
YR 2014
FD 2014/02/01
DO DOI NA
AB <p>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.</p>
K1 Artificial neural networks (ANNs), Grain, Multi layer perceptron (MLP), Neuro-Fuzzy, Rice
PB Pakistan Agricultural Scientists Forum
LK https://thejaps.org.pk/AbstractView.aspx?mid=2014-JAPS-47