Rice yield classification using Backpropagation Network

dc.contributor.author	Puteh, Saad
dc.contributor.author	Nor Khairah, Jamaludin
dc.contributor.author	Siti Sakira, Kamrudin
dc.contributor.author	Aryati, Bakri
dc.contributor.author	Nursalasawati, Rusli
dc.date.accessioned	2009-08-18T02:10:21Z
dc.date.available	2009-08-18T02:10:21Z
dc.date.issued	2004
dc.identifier.citation	Journal of ICT, vol.3(1), 2004, pages 67-81.	en_US
dc.identifier.uri	http://dspace.unimap.edu.my/123456789/6973
dc.description	Link to publisher's homepage at www.jict.uum.edu.my	en_US
dc.description.abstract	Among 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.iso	en	en_US
dc.publisher	Penerbit Universiti Utara Malaysia	en_US
dc.subject	Backpropagation network	en_US
dc.subject	Classification	en_US
dc.subject	Rice yield	en_US
dc.subject	Pests	en_US
dc.subject	Diseases	en_US
dc.subject	Weeds	en_US
dc.subject	Neural networks (Computer science)	en_US
dc.subject	Computer programming	en_US
dc.subject	Back propagation	en_US
dc.title	Rice yield classification using Backpropagation Network	en_US
dc.type	Article	en_US
dc.publisher.department	Fakulti Teknologi Maklumat	en_US