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dc.contributor.authorYasmin, M. Yacob
dc.contributor.authorM. Shaiful, A.R.A
dc.contributor.authorZulkifli, Husin
dc.contributor.authorRohani, S Mohamed Farook
dc.contributor.authorAbdul Hallis, Abdul Aziz
dc.date.accessioned2009-12-06T02:42:24Z
dc.date.available2009-12-06T02:42:24Z
dc.date.issued2008-12-01
dc.identifier.citationp.1-6en_US
dc.identifier.isbn978-1-4244-2315-6
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?=&arnumber=4786780
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/7372
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.orgen_US
dc.description.abstractPostharvest 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.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineering (IEEE)en_US
dc.relation.ispartofseriesProceedings of the International Conference on Electronic Design (ICED 2008)en_US
dc.subjectDielectric sensoren_US
dc.subjectNeural networken_US
dc.subjectNon-destructive detectionen_US
dc.subjectWeevilen_US
dc.subjectDielectric sensoren_US
dc.subjectImage processingen_US
dc.subjectAgricultural engineeringen_US
dc.titleHarum manis mango weevil infestation classification using backpropagation neural networken_US
dc.typeWorking Paperen_US
dc.contributor.urlyasmin.yacob@unimap.edu.myen_US


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