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Ficusdeltoidea (Jack) Moraceae Varietal Identification Using Statistical Recognition Approach

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

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Plant species recognition; Statistical pattern recognition; Data mining; Feature selection
Subjects: T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 28 Aug 2017 02:46
Last Modified: 28 Aug 2018 06:56
URI: http://umpir.ump.edu.my/id/eprint/18476
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