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  • Design of an Expert System for Rice Kernel Identification using Optimal Morphological Features and Back Propagation Neural Network

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Number 2

Design of an Expert System for Rice Kernel Identification using Optimal Morphological Features and Back Propagation Neural Network

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  • S. J. Mousavi Rad and F. Akhlaghian Tab and K. Mollazade 2012. Design of an Expert System for Rice Kernel Identification using Optimal Morphological Features and Back Propagation Neural Network. International Journal of Applied Information Systems. 3, 2 (July 2012), 33-37. DOI=http://dx.doi.org/10.5120/ijais450472 
  • @article{10.5120/ijais2017451568,
author = {S. J. Mousavi Rad and F. Akhlaghian Tab and K. Mollazade},
title = {Design of an Expert System for Rice Kernel Identification using Optimal Morphological Features and Back Propagation Neural Network},
journal = {International Journal of Applied Information Systems},
issue_date = {July 2012},
volume = {3},
number = {},
month = {July},
year = {2012},
issn = {},
pages = {33-37},
numpages = {},
url = {/archives/volume3/number2/207-0472},
doi = { http:/ijais12-450472},
publisher = { xA9 2010 by IJAIS Journal},
address = {}
}

  • %1 450472
%A S.  J.  Mousavi Rad
%A F.  Akhlaghian Tab
%A K.  Mollazade
%T Design of an Expert System for Rice Kernel Identification using Optimal Morphological Features and Back Propagation Neural Network
%J International Journal of Applied Information Systems
%@ 
%V 3
%N 
%P 33-37
%D 2012
%I  xA9 2010 by IJAIS Journal

Abstract

In this paper, an algorithm for identifying five different varieties of rice, using the morphological features is presented. The proposed algorithm consists of several steps: image acquisition, segmentation, feature extraction, feature selection, and classification. Eighteen morphological features were extracted from rice kernels. The Set of features contained redundant, noisy or even irrelevant information so features were examined by four different algorithms. Finally six features were selected as the superior ones. A back propagation neural network-based classifier was developed to classify rice varieties. The overall classification accuracy was achieved as 98. 4%.

References

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Keywords

Rice Kernel Identification, Feature Extraction, Feature Selection, Neural Networks, Morphological Feature

Index Terms

Computer Science
Information Sciences