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Feature decision-making ant colony optimization system for an automated recognition of plant species

In the present paper, an expert system for automatic recognition of different plant species through their leaf images is investigated by employing the ant colony optimization (ACO) as a feature decision-making algorithm. The ACO algorithm is employed to investigate inside the feature search space in...

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Main Authors: Ghasab, Mohammad Ali Jan, Khamis, Shamsul, Faruq, Mohammad, Fariman, Hessam Jahani
Format: Article
Language:English
Published: Elsevier 2015
Online Access:http://psasir.upm.edu.my/id/eprint/56581/1/Feature%20decision-making%20ant%20colony%20optimization%20system%20for%20an%20automated%20recognition%20of%20plant%20species.pdf
http://psasir.upm.edu.my/id/eprint/56581/
http://www.sciencedirect.com/science/article/pii/S0957417414006976
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spelling my.upm.eprints.565812017-08-03T04:52:57Z http://psasir.upm.edu.my/id/eprint/56581/ Feature decision-making ant colony optimization system for an automated recognition of plant species Ghasab, Mohammad Ali Jan Khamis, Shamsul Faruq, Mohammad Fariman, Hessam Jahani In the present paper, an expert system for automatic recognition of different plant species through their leaf images is investigated by employing the ant colony optimization (ACO) as a feature decision-making algorithm. The ACO algorithm is employed to investigate inside the feature search space in order to obtain the best discriminant features for the recognition of individual species. In order to establish a feature search space, a set of feasible characteristics such as shape, morphology, texture and color are extracted from the leaf images. The selected features are used by support vector machine (SVM) to classify the species. The efficiency of the system was tested on around 2050 leaf images collected from two different plant databases, FCA and Flavia. The results of the study achieved an average accuracy of 95.53% from the ACO-based approach, confirming the potentials of using the proposed system for an automatic classification of various plant species. Elsevier 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/56581/1/Feature%20decision-making%20ant%20colony%20optimization%20system%20for%20an%20automated%20recognition%20of%20plant%20species.pdf Ghasab, Mohammad Ali Jan and Khamis, Shamsul and Faruq, Mohammad and Fariman, Hessam Jahani (2015) Feature decision-making ant colony optimization system for an automated recognition of plant species. Expert Systems with Applications, 42 (5). pp. 2361-2370. ISSN 0957-4174; ESSN: 1873-6793 http://www.sciencedirect.com/science/article/pii/S0957417414006976 10.1016/j.eswa.2014.11.011
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 In the present paper, an expert system for automatic recognition of different plant species through their leaf images is investigated by employing the ant colony optimization (ACO) as a feature decision-making algorithm. The ACO algorithm is employed to investigate inside the feature search space in order to obtain the best discriminant features for the recognition of individual species. In order to establish a feature search space, a set of feasible characteristics such as shape, morphology, texture and color are extracted from the leaf images. The selected features are used by support vector machine (SVM) to classify the species. The efficiency of the system was tested on around 2050 leaf images collected from two different plant databases, FCA and Flavia. The results of the study achieved an average accuracy of 95.53% from the ACO-based approach, confirming the potentials of using the proposed system for an automatic classification of various plant species.
format Article
author Ghasab, Mohammad Ali Jan
Khamis, Shamsul
Faruq, Mohammad
Fariman, Hessam Jahani
spellingShingle Ghasab, Mohammad Ali Jan
Khamis, Shamsul
Faruq, Mohammad
Fariman, Hessam Jahani
Feature decision-making ant colony optimization system for an automated recognition of plant species
author_facet Ghasab, Mohammad Ali Jan
Khamis, Shamsul
Faruq, Mohammad
Fariman, Hessam Jahani
author_sort Ghasab, Mohammad Ali Jan
title Feature decision-making ant colony optimization system for an automated recognition of plant species
title_short Feature decision-making ant colony optimization system for an automated recognition of plant species
title_full Feature decision-making ant colony optimization system for an automated recognition of plant species
title_fullStr Feature decision-making ant colony optimization system for an automated recognition of plant species
title_full_unstemmed Feature decision-making ant colony optimization system for an automated recognition of plant species
title_sort feature decision-making ant colony optimization system for an automated recognition of plant species
publisher Elsevier
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/56581/1/Feature%20decision-making%20ant%20colony%20optimization%20system%20for%20an%20automated%20recognition%20of%20plant%20species.pdf
http://psasir.upm.edu.my/id/eprint/56581/
http://www.sciencedirect.com/science/article/pii/S0957417414006976
_version_ 1643836233571368960
score 13.211869

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