Rice yield classification using Backpropagation Network

Simple item page
dc.contributor.authorPuteh, Saad
dc.contributor.authorNor Khairah, Jamaludin
dc.contributor.authorSiti Sakira, Kamrudin
dc.contributor.authorAryati, Bakri
dc.contributor.authorNursalasawati, Rusli
dc.date.accessioned2025-10-28T04:24:27Z
dc.date.issued2004
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.identifier.citationJournal of ICT, vol.3(1), 2004, pages 67-81.en_US
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/6973
dc.identifier.urihttps://dspace.unimap.edu.my/handle/123456789/107780
dc.language.isoenen_US
dc.publisherPenerbit Universiti Utara Malaysiaen_US
dc.publisher.departmentFakulti Teknologi Maklumaten_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

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Rice Yield Classification Using Backpropagation Network.pdf
Size:
5.46 MB
Format:
Adobe Portable Document Format
Description:
Access is limited to UniMAP community.
Download

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.9 KB
Format:
Plain Text
Description:
Download

Collections

School of Computer and Communication Engineering (Articles)