Harum manis mango weevil infestation classification using backpropagation neural network

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Main Authors: Yasmin, M. Yacob, M. Shaiful, A.R.A, Zulkifli, Husin, Rohani, S Mohamed Farook, Abdul Hallis, Abdul Aziz
Other Authors: yasmin.yacob@unimap.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineering (IEEE) 2009
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Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/7372
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Institution: Universiti Malaysia Perlis
Language: English
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spelling my.unimap-73722010-11-24T02:44:52Z Harum manis mango weevil infestation classification using backpropagation neural network Yasmin, M. Yacob M. Shaiful, A.R.A Zulkifli, Husin Rohani, S Mohamed Farook Abdul Hallis, Abdul Aziz yasmin.yacob@unimap.edu.my Dielectric sensor Neural network Non-destructive detection Weevil Dielectric sensor Image processing Agricultural engineering Link to publisher's homepage at http://ieeexplore.ieee.org Postharvest non-destructive detection methods in fruit quality have been widely studied eversince. This include studies of maturity, bruises and detection of pests or weevil existence in fruits such as apple, banana, zucchini including mango. Regarding fruit grading, the non-destructive methods which can be used are image processing and dielectric properties. Either technique has its own benefits and drawbacks. As for image processing technique, the cost is high since suitable device to acquire the images are by using MRI or X-Ray. Whereas for dielectric method, permittivity is difficult to record because the reading is very small and are prone to environment and temperature influence. This paper analyze about classification of Harum Manis Mango infestation using dielectric sensor which was trained and tested using Back-propagation Neural Network. In addition, reviews regarding Neural Network design is also discussed. 2009-12-06T02:42:24Z 2009-12-06T02:42:24Z 2008-12-01 Working Paper p.1-6 978-1-4244-2315-6 http://ieeexplore.ieee.org/xpls/abs_all.jsp?=&arnumber=4786780 http://hdl.handle.net/123456789/7372 en Proceedings of the International Conference on Electronic Design (ICED 2008) Institute of Electrical and Electronics Engineering (IEEE)
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 Dielectric sensor
Neural network
Non-destructive detection
Weevil
Dielectric sensor
Image processing
Agricultural engineering
spellingShingle Dielectric sensor
Neural network
Non-destructive detection
Weevil
Dielectric sensor
Image processing
Agricultural engineering
Yasmin, M. Yacob
M. Shaiful, A.R.A
Zulkifli, Husin
Rohani, S Mohamed Farook
Abdul Hallis, Abdul Aziz
Harum manis mango weevil infestation classification using backpropagation neural network
description Link to publisher's homepage at http://ieeexplore.ieee.org
author2 yasmin.yacob@unimap.edu.my
author_facet yasmin.yacob@unimap.edu.my
Yasmin, M. Yacob
M. Shaiful, A.R.A
Zulkifli, Husin
Rohani, S Mohamed Farook
Abdul Hallis, Abdul Aziz
format Working Paper
author Yasmin, M. Yacob
M. Shaiful, A.R.A
Zulkifli, Husin
Rohani, S Mohamed Farook
Abdul Hallis, Abdul Aziz
author_sort Yasmin, M. Yacob
title Harum manis mango weevil infestation classification using backpropagation neural network
title_short Harum manis mango weevil infestation classification using backpropagation neural network
title_full Harum manis mango weevil infestation classification using backpropagation neural network
title_fullStr Harum manis mango weevil infestation classification using backpropagation neural network
title_full_unstemmed Harum manis mango weevil infestation classification using backpropagation neural network
title_sort harum manis mango weevil infestation classification using backpropagation neural network
publisher Institute of Electrical and Electronics Engineering (IEEE)
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/7372
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