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Rice yield classification using backpropagation network


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

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Official URL: http://jict.uum.edu.my

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Backpropagation network, classification, rice yield, pests, diseases, and weeds
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: UNSPECIFIED
Depositing User: Mrs. Norazmilah Yaakub
Date Deposited: 05 Sep 2010 04:55
Last Modified: 05 Sep 2010 04:55
URI: http://repo.uum.edu.my/id/eprint/1043

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