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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.
Other Authors: 57191952020
Format: Conference Paper
Published: 2023
Subjects:
Colour Recognition
Fruit Classification
HSI
Morphological Feature Analysis
PNN
RGB
Color
Feature extraction
Image processing
Neural networks
Classification methods
Color features
Fruit sorting
MATLAB/Simulink environment
Morphological features
Probabilistic neural networks
Fruits
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id my.uniten.dspace-30354
record_format dspace
spelling my.uniten.dspace-303542024-04-18T10:36:46Z Classification of fruits using Probabilistic Neural Networks - Improvement using color features Mustafa N.B.A. Arumugam K. Ahmed S.K. Sharrif Z.A.Md. 57191952020 54977516700 25926812900 6507195893 Colour Recognition Fruit Classification HSI Morphological Feature Analysis PNN RGB Color Feature extraction Image processing Neural networks Classification methods Color features Fruit sorting HSI MATLAB/Simulink environment Morphological features PNN Probabilistic neural networks RGB Fruits 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. Final 2023-12-29T07:47:00Z 2023-12-29T07:47:00Z 2011 Conference Paper 10.1109/TENCON.2011.6129105 2-s2.0-84856849332 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856849332&doi=10.1109%2fTENCON.2011.6129105&partnerID=40&md5=02bbe27e7a0fcad427202b17a368f4e0 https://irepository.uniten.edu.my/handle/123456789/30354 6129105 264 269 Scopus
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/
topic Colour Recognition
Fruit Classification
HSI
Morphological Feature Analysis
PNN
RGB
Color
Feature extraction
Image processing
Neural networks
Classification methods
Color features
Fruit sorting
HSI
MATLAB/Simulink environment
Morphological features
PNN
Probabilistic neural networks
RGB
Fruits
spellingShingle Colour Recognition
Fruit Classification
HSI
Morphological Feature Analysis
PNN
RGB
Color
Feature extraction
Image processing
Neural networks
Classification methods
Color features
Fruit sorting
HSI
MATLAB/Simulink environment
Morphological features
PNN
Probabilistic neural networks
RGB
Fruits
Mustafa N.B.A.
Arumugam K.
Ahmed S.K.
Sharrif Z.A.Md.
Classification of fruits using Probabilistic Neural Networks - Improvement using color features
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.
author2 57191952020
author_facet 57191952020
Mustafa N.B.A.
Arumugam K.
Ahmed S.K.
Sharrif Z.A.Md.
format Conference Paper
author 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 2023
_version_ 1806427375429222400
score 13.18916

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