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Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features

by Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 11
Year of Publication: 2014
Authors: Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi
10.5120/16259-5910

Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi . Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features. International Journal of Computer Applications. 93, 11 ( May 2014), 17-24. DOI=10.5120/16259-5910

@article{ 10.5120/16259-5910,
author = { Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi },
title = { Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 11 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 17-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number11/16259-5910/ },
doi = { 10.5120/16259-5910 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:15:32.147426+05:30
%A Jagadeesh D. Pujari
%A Rajesh Yakkundimath
%A Abdulmunaf S. Byadgi
%T Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 11
%P 17-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we present a reduced feature set based approach for recognition and classification of normal and affected agriculture produce types. Color and texture features are extracted from normal and affected image samples of agriculture produce. The color features are reduced from eighteen to eight and texture features are reduced from thirty to five. A classifier based on Back Propagation Neural Network (BPNN) is developed which uses reduced color and texture features to recognize and classify the different normal and affected agricultural produce. A feedback from classifier performance is used in reducing the features. The average classification accuracies using reduced color features are 78. 08% and 75. 17% for normal and affected agriculture produce type respectively. The average classification accuracies using reduced texture features are 85. 53% and 77. 43% for normal and affected agriculture produce type respectively. The average classification accuracies have increased to 88. 28% and 83. 80% for normal and affected agriculture produce type respectively, when the reduced color and texture features are combined. The work finds application in developing a machine vision system in agriculture fields in the area of recognition and classification of agriculture produce.

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Index Terms

Computer Science
Information Sciences

Keywords

agriculture produce color features texture features bulk normal produce bulk affected produce artificial neural network

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