A Model of Plant Identification System Using GLCM, Lacunarity and Shen Features

Kadir, Abdul (2014) A Model of Plant Identification System Using GLCM, Lacunarity and Shen Features. Research Journal of Pharmaceutical, Biological and Chemical Sciences, 5 (2). pp. 1-10. ISSN 0975-8585

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Recently, many approaches have been introduced by several researchers to identify plants. Now, applications of texture, shape, color and vein features are common practices. However, there are many possibilities of methods can be developed to improve the performance of such identification systems. Therefore, several experiments had been conducted in this research. As a result, a new novel approach by using combination of Gray-Level Co-occurrence Matrix, lacunarity and Shen features and a Bayesian classifier gives a better result compared to other plant identification systems. For comparison, this research used two kinds of several datasets that were usually used for testing the performance of each plant identification system. The results show that the system gives an accuracy rate of 97.19% when using the Flavia dataset and 95.00% when using the Foliage dataset and outperforms other approaches.

Item Type: Article
Uncontrolled Keywords: GLCM, Lacunarity, Plant identification system, PFT, Shen features
Subjects: T Technology > T Technology (General)
S Agriculture > SB Plant culture
Divisions: Faculty of Engineering Technology > Department of Electronics & Computer Engineering Technology
Depositing User: Dr. Abdul Kadir
Date Deposited: 24 Mar 2014 02:03
Last Modified: 28 May 2015 04:20
URI: http://eprints.utem.edu.my/id/eprint/11803
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