Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach

Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach

Plant species identification is one of important application in pattern recognition. Selection of relevant features for classification is a classical problem in statistical pattern recognition and data mining. There are two main branches in solving this problem which is by using feature selection or...

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Main Authors: Ahmad Fakhri, Ab. Nasir, M. Nordin, A. Rahman, Nashriyah, Mat, A. Rasid, Mamat, Ahmad Shahrizan, Abdul Ghani
Format: Article
Language:English
English
Published: IDOSI Publications 2017
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/18476/
http://umpir.ump.edu.my/id/eprint/18476/
http://umpir.ump.edu.my/id/eprint/18476/1/Ficusdeltoidea%20%28Jack%29%20Moraceae%20Varietal%20Identification%20Using%20Statistical%20Recognition%20Approach.pdf
http://umpir.ump.edu.my/id/eprint/18476/7/Ficusdeltoidea%20%28Jack%29%20Moraceae%20Varietal%20Identification%20Using%20Statistical%20Recognition%20Approach%201.pdf
id ump-18476
recordtype eprints
spelling ump-184762018-08-28T06:56:24Z http://umpir.ump.edu.my/id/eprint/18476/ Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach Ahmad Fakhri, Ab. Nasir M. Nordin, A. Rahman Nashriyah, Mat A. Rasid, Mamat Ahmad Shahrizan, Abdul Ghani TS Manufactures Plant species identification is one of important application in pattern recognition. Selection of relevant features for classification is a classical problem in statistical pattern recognition and data mining. There are two main branches in solving this problem which is by using feature selection or feature extraction. Currently, in statistical plant species recognition domain, researchers focus mainly on providing an automatic system using feature extraction method such as Principal Component Analysis. This paper presents a hybrid of filter and wrapper in feature selection as well as an empirical comparison of feature selection wrapper method using Sequential Forward Selection and feature extraction method using Principal Component Analysis on a benchmark of 420 images of Ficus deltoidea leaf with 6 varieties. At first, the leaf images are processed using image pre processing techniques. Then, 23 leaf features are extracted such as shape, texture and vein. Finally, in classification process, different feature selection and feature extraction techniques are computed using Support Vector Machine and Nearest Neighbor classifiers. The recognition results reveal that a combination of filter and wrapper approach in feature selection outperformed the other approaches for F. deltoidea varietal identification. IDOSI Publications 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18476/1/Ficusdeltoidea%20%28Jack%29%20Moraceae%20Varietal%20Identification%20Using%20Statistical%20Recognition%20Approach.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/18476/7/Ficusdeltoidea%20%28Jack%29%20Moraceae%20Varietal%20Identification%20Using%20Statistical%20Recognition%20Approach%201.pdf Ahmad Fakhri, Ab. Nasir and M. Nordin, A. Rahman and Nashriyah, Mat and A. Rasid, Mamat and Ahmad Shahrizan, Abdul Ghani (2017) Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach. World Applied Sciences Journal. pp. 82-88. ISSN 1818-4952 DOI: 10.5829/idosi/wasj.2017.82.88
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic TS Manufactures
spellingShingle TS Manufactures
Ahmad Fakhri, Ab. Nasir
M. Nordin, A. Rahman
Nashriyah, Mat
A. Rasid, Mamat
Ahmad Shahrizan, Abdul Ghani
Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach
description Plant species identification is one of important application in pattern recognition. Selection of relevant features for classification is a classical problem in statistical pattern recognition and data mining. There are two main branches in solving this problem which is by using feature selection or feature extraction. Currently, in statistical plant species recognition domain, researchers focus mainly on providing an automatic system using feature extraction method such as Principal Component Analysis. This paper presents a hybrid of filter and wrapper in feature selection as well as an empirical comparison of feature selection wrapper method using Sequential Forward Selection and feature extraction method using Principal Component Analysis on a benchmark of 420 images of Ficus deltoidea leaf with 6 varieties. At first, the leaf images are processed using image pre processing techniques. Then, 23 leaf features are extracted such as shape, texture and vein. Finally, in classification process, different feature selection and feature extraction techniques are computed using Support Vector Machine and Nearest Neighbor classifiers. The recognition results reveal that a combination of filter and wrapper approach in feature selection outperformed the other approaches for F. deltoidea varietal identification.
format Article
author Ahmad Fakhri, Ab. Nasir
M. Nordin, A. Rahman
Nashriyah, Mat
A. Rasid, Mamat
Ahmad Shahrizan, Abdul Ghani
author_facet Ahmad Fakhri, Ab. Nasir
M. Nordin, A. Rahman
Nashriyah, Mat
A. Rasid, Mamat
Ahmad Shahrizan, Abdul Ghani
author_sort Ahmad Fakhri, Ab. Nasir
title Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach
title_short Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach
title_full Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach
title_fullStr Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach
title_full_unstemmed Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach
title_sort ficusdeltoidea (jack) moraceae varietal identification using statistical recognition approach
publisher IDOSI Publications
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/18476/
http://umpir.ump.edu.my/id/eprint/18476/
http://umpir.ump.edu.my/id/eprint/18476/1/Ficusdeltoidea%20%28Jack%29%20Moraceae%20Varietal%20Identification%20Using%20Statistical%20Recognition%20Approach.pdf
http://umpir.ump.edu.my/id/eprint/18476/7/Ficusdeltoidea%20%28Jack%29%20Moraceae%20Varietal%20Identification%20Using%20Statistical%20Recognition%20Approach%201.pdf
first_indexed 2023-09-18T22:26:12Z
last_indexed 2023-09-18T22:26:12Z
_version_ 1777415982733590528

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