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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/6973
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dc.contributor.authorPuteh, Saad-
dc.contributor.authorNor Khairah, Jamaludin-
dc.contributor.authorSiti Sakira, Kamrudin-
dc.contributor.authorAryati, Bakri-
dc.contributor.authorNursalasawati, Rusli-
dc.date.accessioned2009-08-18T02:10:21Z-
dc.date.available2009-08-18T02:10:21Z-
dc.date.issued2004-
dc.identifier.citationJournal of ICT, vol.3(1), 2004, pages 67-81.en_US
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/6973-
dc.descriptionLink to publisher's homepage at www.jict.uum.edu.myen_US
dc.description.abstractAmong factors that affect rice yield area are diseases, pests and weeds. It is intractable to model the correlation between plant diseases, pests and weeds on the amount of rice yield statistically and mathematically. In this study, a backpropagation network (BPN) is developed to classify rice yield based on the aforementioned factors in MUDA irrigation area Malaysia. The result of this study showns that BPN is able to classify the rice yield to a deviation of 0.03.en_US
dc.language.isoenen_US
dc.publisherPenerbit Universiti Utara Malaysiaen_US
dc.subjectBackpropagation networken_US
dc.subjectClassificationen_US
dc.subjectRice yielden_US
dc.subjectPestsen_US
dc.subjectDiseasesen_US
dc.subjectWeedsen_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectComputer programmingen_US
dc.subjectBack propagationen_US
dc.titleRice yield classification using Backpropagation Networken_US
dc.typeArticleen_US
dc.publisher.departmentFakulti Teknologi Maklumaten_US
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