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Texture Feature Extraction for Identification of Medicinal Plants and comparison of different classifiers
by
C. H. Arun,
W. R. Sam Emmanuel,
D. Christopher Durairaj
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 62 - Number 12 |
| Year of Publication: 2013 |
| Authors: C. H. Arun, W. R. Sam Emmanuel, D. Christopher Durairaj |
10.5120/10129-4920
|
C. H. Arun, W. R. Sam Emmanuel, D. Christopher Durairaj . Texture Feature Extraction for Identification of Medicinal Plants and comparison of different classifiers. International Journal of Computer Applications. 62, 12 ( January 2013), 1-9. DOI=10.5120/10129-4920
@article{
10.5120/10129-4920,
author = {
C. H. Arun,
W. R. Sam Emmanuel,
D. Christopher Durairaj
},
title = { Texture Feature Extraction for Identification of Medicinal Plants and comparison of different classifiers },
journal = {
International Journal of Computer Applications
},
issue_date = { January 2013 },
volume = { 62 },
number = { 12 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = {
1-9
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume62/number12/10129-4920/
},
doi = { 10.5120/10129-4920 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:11:34.186675+05:30
%A C. H. Arun
%A W. R. Sam Emmanuel
%A D. Christopher Durairaj
%T Texture Feature Extraction for Identification of Medicinal Plants and comparison of different classifiers
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 12
%P 1-9
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents an automated system for recognizing the medicinal plant leaves that are taken from the suburbs of the western ghats region. The dataset comprises of 250 different leaf images, of five species. Texture analyses of the leaf images have been done in this work using the feature computation. The features include grey textures, grey tone spatial dependency matrices(GTSDM) and Local Binary Pattern(LBP) operators. For each leaf image, a feature vector is generated from the statistical values. 70% of the images in the dataset are the training dataset and the rest are included in the test set. Six different classifiers are used to classify the plant leaves based on feature values. When features are combined without any preprocessing, it yielded a classification performance of 94. 7%.
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