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Classification for the fruit maturity using Neural Network

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Bibliographic Details
Main Author: Mohamad Naeem Hussien
Other Authors: Zulkifli Husin (Advisor)
Format: Learning Object
Language:English
Published: Universiti Malaysia Perlis 2008
Subjects:
Agriculture > Technological innovations
Fruit > Testing
Optical data processing
Neural networks (Computer science
Fruit maturity > Testing kit
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/3297
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spelling my.unimap-32972008-11-24T01:42:02Z Classification for the fruit maturity using Neural Network Mohamad Naeem Hussien Zulkifli Husin (Advisor) Agriculture -- Technological innovations Fruit -- Testing Optical data processing Neural networks (Computer science Fruit maturity -- Testing kit Access is limited to UniMAP community. Since lately steadily improving agricultural sector especially in the production fruit. There were various methods to improve productivity fruit production. The classification for the maturity of fruits is not easily determined. This is especially true, for some fruits whose color have no direct correlation with to its level of maturity or ripeness. The levels of maturity can be determined by human expert, however for larger quantity inspection, this method is not practical. Therefore, accurate automatic classification for fruit maturity may be advantageous for the agriculture industry. In addition, consumers in supermarkets may also benefit from this system. This project is a classification for fruit maturity using neural networks system. For this study, banana was chosen because it is easy to identify its maturity level by just looking to its colors and ease of availability. Hence the data can be collected without destroying the fruit. Multilayer Perceptron (MLP) was used to classify the samples for four types of maturity levels; under ripe, unripe, ripe and over ripe maturity level. (MLP) training algorithm was used to train the MLP network and it was shown that the network was able to produce accurately for the classification of fruit samples weather it were under ripe, unripe, ripe and over ripe. 2008-11-24T01:42:02Z 2008-11-24T01:42:02Z 2008-04 Learning Object http://hdl.handle.net/123456789/3297 en Universiti Malaysia Perlis School of Computer and Communication Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Agriculture -- Technological innovations
Fruit -- Testing
Optical data processing
Neural networks (Computer science
Fruit maturity -- Testing kit
spellingShingle Agriculture -- Technological innovations
Fruit -- Testing
Optical data processing
Neural networks (Computer science
Fruit maturity -- Testing kit
Mohamad Naeem Hussien
Classification for the fruit maturity using Neural Network
description Access is limited to UniMAP community.
author2 Zulkifli Husin (Advisor)
author_facet Zulkifli Husin (Advisor)
Mohamad Naeem Hussien
format Learning Object
author Mohamad Naeem Hussien
author_sort Mohamad Naeem Hussien
title Classification for the fruit maturity using Neural Network
title_short Classification for the fruit maturity using Neural Network
title_full Classification for the fruit maturity using Neural Network
title_fullStr Classification for the fruit maturity using Neural Network
title_full_unstemmed Classification for the fruit maturity using Neural Network
title_sort classification for the fruit maturity using neural network
publisher Universiti Malaysia Perlis
publishDate 2008
url http://dspace.unimap.edu.my/xmlui/handle/123456789/3297
_version_ 1643787782843269120
score 13.214268

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