Color quality assessment of coconut sugar using artificial neural network (ANN)

Color quality assessment of coconut sugar using artificial neural network (ANN)

This paper presents a simple color recognition algorithm using digital image processing techniques and pattern recognition to eliminate the subjectiveness of manual inspection of the quality of coconut sugar based on Philippine National Standard. The image processing was built using MATLAB functions...

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Main Authors: Aquino, Aaron U., Bautista, Mary Grace Ann C., Bandala, Argel A., Dadios, Elmer P.
Format: text
Published: Animo Repository 2016
Subjects:
Color vision
Image processing—Digital techniques Sugar
Electrical and Computer Engineering
Electrical and Electronics
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2771
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Institution: De La Salle University
id oai:animorepository.dlsu.edu.ph:faculty_research-3770
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:faculty_research-37702022-08-25T05:59:39Z Color quality assessment of coconut sugar using artificial neural network (ANN) Aquino, Aaron U. Bautista, Mary Grace Ann C. Bandala, Argel A. Dadios, Elmer P. This paper presents a simple color recognition algorithm using digital image processing techniques and pattern recognition to eliminate the subjectiveness of manual inspection of the quality of coconut sugar based on Philippine National Standard. The image processing was built using MATLAB functions through RGB acquisition. The Backpropagation Artificial Neural Network was used in this project to enhance the accuracy and performance of image processing. The database of the network involved 300 images and 70% of these were used for training the network, 15% for validation and 15% for testing. © 2015 IEEE. 2016-01-25T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2771 Faculty Research Work Animo Repository Color vision Image processing—Digital techniques Sugar Electrical and Computer Engineering Electrical and Electronics
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Color vision
Image processing—Digital techniques Sugar
Electrical and Computer Engineering
Electrical and Electronics
spellingShingle Color vision
Image processing—Digital techniques Sugar
Electrical and Computer Engineering
Electrical and Electronics
Aquino, Aaron U.
Bautista, Mary Grace Ann C.
Bandala, Argel A.
Dadios, Elmer P.
Color quality assessment of coconut sugar using artificial neural network (ANN)
description This paper presents a simple color recognition algorithm using digital image processing techniques and pattern recognition to eliminate the subjectiveness of manual inspection of the quality of coconut sugar based on Philippine National Standard. The image processing was built using MATLAB functions through RGB acquisition. The Backpropagation Artificial Neural Network was used in this project to enhance the accuracy and performance of image processing. The database of the network involved 300 images and 70% of these were used for training the network, 15% for validation and 15% for testing. © 2015 IEEE.
format text
author Aquino, Aaron U.
Bautista, Mary Grace Ann C.
Bandala, Argel A.
Dadios, Elmer P.
author_facet Aquino, Aaron U.
Bautista, Mary Grace Ann C.
Bandala, Argel A.
Dadios, Elmer P.
author_sort Aquino, Aaron U.
title Color quality assessment of coconut sugar using artificial neural network (ANN)
title_short Color quality assessment of coconut sugar using artificial neural network (ANN)
title_full Color quality assessment of coconut sugar using artificial neural network (ANN)
title_fullStr Color quality assessment of coconut sugar using artificial neural network (ANN)
title_full_unstemmed Color quality assessment of coconut sugar using artificial neural network (ANN)
title_sort color quality assessment of coconut sugar using artificial neural network (ann)
publisher Animo Repository
publishDate 2016
url https://animorepository.dlsu.edu.ph/faculty_research/2771
_version_ 1743177759215058944

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