Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)

Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)

Agriculture products are being more demanding in market today. To increase its productivity, automation to produce these products will be very helpful. The purpose of this work is to measure and determine the ripeness and quality of watermelon. The textures on watermelon skin will be captured using...

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Bibliographic Details
Published in:World Academy of Science, Engineering and Technology
Main Author: Shah Rizam M.S.B.; Farah Yasmin A.R.; Ahmad Ihsan M.Y.; Shazana K.
Format: Article
Language:English
Published: 2009
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-77649303416&partnerID=40&md5=21d52ce14948265a66d57102e5418fe7
id 2-s2.0-77649303416
spelling 2-s2.0-77649303416
Shah Rizam M.S.B.; Farah Yasmin A.R.; Ahmad Ihsan M.Y.; Shazana K.
Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)
2009
World Academy of Science, Engineering and Technology
38


https://www.scopus.com/inward/record.uri?eid=2-s2.0-77649303416&partnerID=40&md5=21d52ce14948265a66d57102e5418fe7
Agriculture products are being more demanding in market today. To increase its productivity, automation to produce these products will be very helpful. The purpose of this work is to measure and determine the ripeness and quality of watermelon. The textures on watermelon skin will be captured using digital camera. These images will be filtered using image processing technique. All these information gathered will be trained using ANN to determine the watermelon ripeness accuracy. Initial results showed that the best model has produced percentage accuracy of 86.51%, when measured at 32 hidden units with a balanced percentage rate of training dataset. © 2009 WASET.ORG.

20103778
English
Article
author Shah Rizam M.S.B.; Farah Yasmin A.R.; Ahmad Ihsan M.Y.; Shazana K.
spellingShingle Shah Rizam M.S.B.; Farah Yasmin A.R.; Ahmad Ihsan M.Y.; Shazana K.
Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)
author_facet Shah Rizam M.S.B.; Farah Yasmin A.R.; Ahmad Ihsan M.Y.; Shazana K.
author_sort Shah Rizam M.S.B.; Farah Yasmin A.R.; Ahmad Ihsan M.Y.; Shazana K.
title Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)
title_short Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)
title_full Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)
title_fullStr Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)
title_full_unstemmed Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)
title_sort Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)
publishDate 2009
container_title World Academy of Science, Engineering and Technology
container_volume 38
container_issue
doi_str_mv
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-77649303416&partnerID=40&md5=21d52ce14948265a66d57102e5418fe7
description Agriculture products are being more demanding in market today. To increase its productivity, automation to produce these products will be very helpful. The purpose of this work is to measure and determine the ripeness and quality of watermelon. The textures on watermelon skin will be captured using digital camera. These images will be filtered using image processing technique. All these information gathered will be trained using ANN to determine the watermelon ripeness accuracy. Initial results showed that the best model has produced percentage accuracy of 86.51%, when measured at 32 hidden units with a balanced percentage rate of training dataset. © 2009 WASET.ORG.
publisher
issn 20103778
language English
format Article
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record_format scopus
collection Scopus
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