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Classification of Diseased Arecanut based on Texture Features

Published on February 2014 by Suresha M, Ajit Danti, S. K Narasimhamurthy
National Conference on Recent Advances in Information Technology
Foundation of Computer Science USA
NCRAIT - Number 3
February 2014
Authors: Suresha M, Ajit Danti, S. K Narasimhamurthy
b3bc2dfd-802d-4029-921e-1b472d2cfb19

Suresha M, Ajit Danti, S. K Narasimhamurthy . Classification of Diseased Arecanut based on Texture Features. National Conference on Recent Advances in Information Technology. NCRAIT, 3 (February 2014), 1-6.

@article{
author = { Suresha M, Ajit Danti, S. K Narasimhamurthy },
title = { Classification of Diseased Arecanut based on Texture Features },
journal = { National Conference on Recent Advances in Information Technology },
issue_date = { February 2014 },
volume = { NCRAIT },
number = { 3 },
month = { February },
year = { 2014 },
issn = 0975-8887,
pages = { 1-6 },
numpages = 6,
url = { /proceedings/ncrait/number3/15152-1419/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Advances in Information Technology
%A Suresha M
%A Ajit Danti
%A S. K Narasimhamurthy
%T Classification of Diseased Arecanut based on Texture Features
%J National Conference on Recent Advances in Information Technology
%@ 0975-8887
%V NCRAIT
%N 3
%P 1-6
%D 2014
%I International Journal of Computer Applications
Abstract

In the proposed work, classification of diseased and undiseased arecanut have been determined using texture features of Local Binary Pattern (LBP), Haar Wavelets, GLCM and Gabor. This work has been carried out in two stages. In the first stage, LBP have been applied on each color component of HSI and YCbCr color models and histogram of LBP is generated. The statistical method correlation is used to measure the distance between histogram of training set and query sample and obtained a success rate of 92. 00%. We have not achieved better results in the first stage. In the second stage, texture features of Haar wavelets, GLCM and Gabor have been used. In this stage, RGB input arecanut image is transformed to HSI and YCbCr color models and texture features are extracted from each color component. Subset of texture features with high degree of discrimination power has been identified empirically based on combination of texture features. The kNN classifier gave a success rate of 100% for discriminative subset of texture features.

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Index Terms

Computer Science
Information Sciences

Keywords

Arecanut Classification Discriminative Texture Features Gabor Filters Glcm Haar Wavelets Lbp