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Rice Yield Classification Using Backpropogation Network

Saad P.., and Jamaludin N.K., and Kamarudin S.S., and Bakri A., and Rusli N., (2004) Rice Yield Classification Using Backpropogation Network. Journal of ICT, 3 (1). pp. 67-61. ISSN 1675414X

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Affiliations

Universiti Utara Malaysia. College of Engineering
Universiti Teknologi Malaysia. Facul(y of Computer Science and Information System
Universiti Utara Malaysia. Facul(y of Information Technology
Universiti Teknologi Malaysia. Facul(y of Computer Science and Information System
Universiti Utara Malaysia. College of Engineering

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:Journal
Additional Information:We are indebted to Ministry of Science Technology and Environment (MOSTE), Malaysia for providing funds to carry out this research project under the IRPA Grant No: 04-02-06- 0066 EA001 with the title of "Development of Intelligent Decision Support System for Rice Yield Prediction in Precision Farming". The duration of the grant is from 1/1/2003 to 31/12/2005.
Keywords:Backpropagation Network, classification, rice yield, pests, diseases and weeds
Subjects:Q Science, Computer Science
ID Code:7765

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