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Classification of fruits using Probabilistic Neural Networks - Improvement using color features

This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image Processing and Artificial Neural Networks. The aim is to develop a fast and effective classification method along with a target of 100% efficiency. Five fruits; i.e., a...

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Main Authors: Mustafa, N.B.A., Arumugam, K., Ahmed, S.K., Sharrif, Z.A.Md.
Format: Conference Paper
Language:English
Published: 2017
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spelling my.uniten.dspace-50132017-11-14T04:49:22Z Classification of fruits using Probabilistic Neural Networks - Improvement using color features Mustafa, N.B.A. Arumugam, K. Ahmed, S.K. Sharrif, Z.A.Md. This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image Processing and Artificial Neural Networks. The aim is to develop a fast and effective classification method along with a target of 100% efficiency. Five fruits; i.e., apples, bananas, carrots, mangoes and oranges were analysed and seventeen features were extracted based on the fruits' morphological and colour characteristics. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB/SIMULINK environment. The results obtained were a significant improvement over a previous study. © 2011 IEEE. 2017-11-14T03:21:18Z 2017-11-14T03:21:18Z 2011 Conference Paper 10.1109/TENCON.2011.6129105 en IEEE Region 10 Annual International Conference, Proceedings/TENCON 2011, Article number 6129105, Pages 264-269
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image Processing and Artificial Neural Networks. The aim is to develop a fast and effective classification method along with a target of 100% efficiency. Five fruits; i.e., apples, bananas, carrots, mangoes and oranges were analysed and seventeen features were extracted based on the fruits' morphological and colour characteristics. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB/SIMULINK environment. The results obtained were a significant improvement over a previous study. © 2011 IEEE.
format Conference Paper
author Mustafa, N.B.A.
Arumugam, K.
Ahmed, S.K.
Sharrif, Z.A.Md.
spellingShingle Mustafa, N.B.A.
Arumugam, K.
Ahmed, S.K.
Sharrif, Z.A.Md.
Classification of fruits using Probabilistic Neural Networks - Improvement using color features
author_facet Mustafa, N.B.A.
Arumugam, K.
Ahmed, S.K.
Sharrif, Z.A.Md.
author_sort Mustafa, N.B.A.
title Classification of fruits using Probabilistic Neural Networks - Improvement using color features
title_short Classification of fruits using Probabilistic Neural Networks - Improvement using color features
title_full Classification of fruits using Probabilistic Neural Networks - Improvement using color features
title_fullStr Classification of fruits using Probabilistic Neural Networks - Improvement using color features
title_full_unstemmed Classification of fruits using Probabilistic Neural Networks - Improvement using color features
title_sort classification of fruits using probabilistic neural networks - improvement using color features
publishDate 2017
_version_ 1644493590271885312
score 13.18916

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