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

by S. J. Mousavi Rad, F. Akhlaghian Tab, K. Mollazade
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 2
Year of Publication: 2012
Authors: S. J. Mousavi Rad, F. Akhlaghian Tab, K. Mollazade
http:/ijais12-450472

S. J. Mousavi Rad, F. Akhlaghian Tab, K. Mollazade . 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:/ijais12-450472

@article{ http:/ijais12-450472,
author = { S. J. Mousavi Rad, F. Akhlaghian Tab, 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 = { 2 },
month = { July },
year = { 2012 },
issn = { 2249-0868 },
pages = { 33-37 },
numpages = {9},
url = { https://www.ijais.org/archives/volume3/number2/207-0472/ },
doi = { http:/ijais12-450472 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:45:29.062468+05:30
%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
%@ 2249-0868
%V 3
%N 2
%P 33-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
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%.

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Index Terms

Computer Science
Information Sciences

Keywords

Rice Kernel Identification Feature Extraction Feature Selection Neural Networks Morphological Feature

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