Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Saad, P., Jamaludin, N.K., Kamarudin, S. S., Rusli, N.
Format: Article
Language:English
Published: Universiti Utara Malaysia 2004
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://repo.uum.edu.my/1043/1/P._Saad.pdf
http://repo.uum.edu.my/1043/
http://jict.uum.edu.my
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uum.repo.1043
record_format eprints
spelling my.uum.repo.10432010-09-05T04:55:31Z http://repo.uum.edu.my/1043/ Rice yield classification using backpropagation network Saad, P. Jamaludin, N.K. Kamarudin, S. S. Rusli, N. 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. Universiti Utara Malaysia 2004 Article PeerReviewed application/pdf en http://repo.uum.edu.my/1043/1/P._Saad.pdf Saad, P. and Jamaludin, N.K. and Kamarudin, S. S. and Rusli, N. (2004) Rice yield classification using backpropagation network. Journal of ICT, 3 (1). pp. 67-81. ISSN 1675-414X http://jict.uum.edu.my
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Saad, P.
Jamaludin, N.K.
Kamarudin, S. S.
Rusli, N.
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, P.
Jamaludin, N.K.
Kamarudin, S. S.
Rusli, N.
author_facet Saad, P.
Jamaludin, N.K.
Kamarudin, S. S.
Rusli, N.
author_sort Saad, P.
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 Universiti Utara Malaysia
publishDate 2004
url http://repo.uum.edu.my/1043/1/P._Saad.pdf
http://repo.uum.edu.my/1043/
http://jict.uum.edu.my
_version_ 1644277911737335808
score 13.18916

Similar Items