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Identification of Infected Pomegranates using Color Texture Feature Analysis

by Meenakshi M. Pawar, Sanman Bhusari, Akshay Gundewar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 22
Year of Publication: 2012
Authors: Meenakshi M. Pawar, Sanman Bhusari, Akshay Gundewar
10.5120/6404-8792

Meenakshi M. Pawar, Sanman Bhusari, Akshay Gundewar . Identification of Infected Pomegranates using Color Texture Feature Analysis. International Journal of Computer Applications. 43, 22 ( April 2012), 30-34. DOI=10.5120/6404-8792

@article{ 10.5120/6404-8792,
author = { Meenakshi M. Pawar, Sanman Bhusari, Akshay Gundewar },
title = { Identification of Infected Pomegranates using Color Texture Feature Analysis },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 22 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number22/6404-8792/ },
doi = { 10.5120/6404-8792 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:00.361403+05:30
%A Meenakshi M. Pawar
%A Sanman Bhusari
%A Akshay Gundewar
%T Identification of Infected Pomegranates using Color Texture Feature Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 22
%P 30-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this study, a new approach is used to automatically detect the infected pomegranates. In the development of automatic grading and sorting system for pomegranate, critical part is detection of infection. Color texture feature analysis is used for detection of surface defects on pomegranates. Acquired image is initially cropped and then transformed into HSI color space, which is further used for generating SGDM matrix. Total 18 texture features were computed for hue (H), saturation (S) and intensity (I) images from each cropped samples. Best features were used as an input to Support Vector Machine (SVM) classifier and tests were performed to identify best classification model. Out of selected texture features, features showing optimal results were cluster shade (99. 8835%), product moment (99. 8835%) and mean intensity (99. 8059%).

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Index Terms

Computer Science
Information Sciences

Keywords

Pomegranate Disease Detection Machine Vision Color Co-occurrence Method Texture Features Svm