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Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system

Plant species recognition system (PSRS) plays important roles in automated agricultural systems. Usually shape and texture based techniques are used at the same time in object recognition problems. Shape based features are important features in image processing literature and also as a method that t...

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Main Authors: Seyed Mohammad Hussein,, Ahmad, Siti Anom, Hassan, Mohd Khair, Ishak, Asnor Juraiza
Format: Article
Language:English
Published: Federation of Engineering Institutions of Islamic Countries 2013
Online Access:http://psasir.upm.edu.my/id/eprint/18192/1/Introducing%20new%20statistical%20shape%20based%20and%20texture%20feature%20extraction%20methods%20in%20the%20plant%20species%20recognition%20system.pdf
http://psasir.upm.edu.my/id/eprint/18192/
http://www.ijet.feiic.org/index.php/volume-10-2013
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spelling my.upm.eprints.181922016-01-28T04:56:30Z http://psasir.upm.edu.my/id/eprint/18192/ Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system Seyed Mohammad Hussein, Ahmad, Siti Anom Hassan, Mohd Khair Ishak, Asnor Juraiza Plant species recognition system (PSRS) plays important roles in automated agricultural systems. Usually shape and texture based techniques are used at the same time in object recognition problems. Shape based features are important features in image processing literature and also as a method that the human visual system applies to recognize objects Therefore that is a feature representation considered here. In this paper, features such as median, mean, variance and standard deviation (SD) is engaged in shape representation. In addition to shape based features, ROI (region of interest) -entropy average (REA) is introduced to extract texture base features. These two methods are tested on the leaf samples to evaluate the performance of two proposed methods. In addition, in this research NNUGA used for PSRS problem which could increase the accuracy of feed forward neural networks. The results show the outperformance of the two proposed methods for image processing and optimized classifier for classification part. As the classification result, radial basis neural networks (RBFNN), feed forward neural networks (FFNN), neural networks using genetic algorithm (NNUGA) shows 100%, 93%, 97.3% of accuracy respectively . Federation of Engineering Institutions of Islamic Countries 2013-08-04 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/18192/1/Introducing%20new%20statistical%20shape%20based%20and%20texture%20feature%20extraction%20methods%20in%20the%20plant%20species%20recognition%20system.pdf Seyed Mohammad Hussein, and Ahmad, Siti Anom and Hassan, Mohd Khair and Ishak, Asnor Juraiza (2013) Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system. International Journal of Engineering and Technology, 10 (2). pp. 56-61. ISSN 1823-1039 http://www.ijet.feiic.org/index.php/volume-10-2013
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Plant species recognition system (PSRS) plays important roles in automated agricultural systems. Usually shape and texture based techniques are used at the same time in object recognition problems. Shape based features are important features in image processing literature and also as a method that the human visual system applies to recognize objects Therefore that is a feature representation considered here. In this paper, features such as median, mean, variance and standard deviation (SD) is engaged in shape representation. In addition to shape based features, ROI (region of interest) -entropy average (REA) is introduced to extract texture base features. These two methods are tested on the leaf samples to evaluate the performance of two proposed methods. In addition, in this research NNUGA used for PSRS problem which could increase the accuracy of feed forward neural networks. The results show the outperformance of the two proposed methods for image processing and optimized classifier for classification part. As the classification result, radial basis neural networks (RBFNN), feed forward neural networks (FFNN), neural networks using genetic algorithm (NNUGA) shows 100%, 93%, 97.3% of accuracy respectively .
format Article
author Seyed Mohammad Hussein,
Ahmad, Siti Anom
Hassan, Mohd Khair
Ishak, Asnor Juraiza
spellingShingle Seyed Mohammad Hussein,
Ahmad, Siti Anom
Hassan, Mohd Khair
Ishak, Asnor Juraiza
Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system
author_facet Seyed Mohammad Hussein,
Ahmad, Siti Anom
Hassan, Mohd Khair
Ishak, Asnor Juraiza
author_sort Seyed Mohammad Hussein,
title Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system
title_short Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system
title_full Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system
title_fullStr Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system
title_full_unstemmed Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system
title_sort introducing new statistical shape based and texture feature extraction methods in the plant species recognition system
publisher Federation of Engineering Institutions of Islamic Countries
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/18192/1/Introducing%20new%20statistical%20shape%20based%20and%20texture%20feature%20extraction%20methods%20in%20the%20plant%20species%20recognition%20system.pdf
http://psasir.upm.edu.my/id/eprint/18192/
http://www.ijet.feiic.org/index.php/volume-10-2013
_version_ 1643826734494121984
score 13.211869

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