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Rice yield classification using backpropagation network

Among factors that affect rice yield 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 base...

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Main Authors: Saad, Puteh, Jamaludin, Nor Khairah, Kamarudin, Siti Sakira, Bakri, Aryati, Rusli, Nursalasawati
Format: Article
Published: UUM PRESS, Universiti Utara Malaysia 2004
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/28180/
http://www.jict.uum.edu.my/index.php/previous-issues/131-journal-of-information-and-communication-technology-jict-vol-3-no-1-june-2004
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spelling my.utm.281802018-11-30T07:07:15Z http://eprints.utm.my/id/eprint/28180/ Rice yield classification using backpropagation network Saad, Puteh Jamaludin, Nor Khairah Kamarudin, Siti Sakira Bakri, Aryati Rusli, Nursalasawati QA75 Electronic computers. Computer science Among factors that affect rice yield 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 shows that BPN is able to classify the rice yield to a deviation of 0.03. UUM PRESS, Universiti Utara Malaysia 2004-06 Article PeerReviewed Saad, Puteh and Jamaludin, Nor Khairah and Kamarudin, Siti Sakira and Bakri, Aryati and Rusli, Nursalasawati (2004) Rice yield classification using backpropagation network. Journal of Information and Communication Technology (JICT), 3 (1). pp. 67-81. ISSN 2180-3862 http://www.jict.uum.edu.my/index.php/previous-issues/131-journal-of-information-and-communication-technology-jict-vol-3-no-1-june-2004
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Saad, Puteh
Jamaludin, Nor Khairah
Kamarudin, Siti Sakira
Bakri, Aryati
Rusli, Nursalasawati
Rice yield classification using backpropagation network
description Among factors that affect rice yield 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 shows that BPN is able to classify the rice yield to a deviation of 0.03.
format Article
author Saad, Puteh
Jamaludin, Nor Khairah
Kamarudin, Siti Sakira
Bakri, Aryati
Rusli, Nursalasawati
author_facet Saad, Puteh
Jamaludin, Nor Khairah
Kamarudin, Siti Sakira
Bakri, Aryati
Rusli, Nursalasawati
author_sort Saad, Puteh
title Rice yield classification using backpropagation network
title_short Rice yield classification using backpropagation network
title_full Rice yield classification using backpropagation network
title_fullStr Rice yield classification using backpropagation network
title_full_unstemmed Rice yield classification using backpropagation network
title_sort rice yield classification using backpropagation network
publisher UUM PRESS, Universiti Utara Malaysia
publishDate 2004
url http://eprints.utm.my/id/eprint/28180/
http://www.jict.uum.edu.my/index.php/previous-issues/131-journal-of-information-and-communication-technology-jict-vol-3-no-1-june-2004
_version_ 1643648001758986240
score 13.211869

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