Rice yield classification using Backpropagation Network
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Date
Journal Title
Journal ISSN
Volume Title
Publisher
Penerbit Universiti Utara Malaysia
Abstract
Among 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.
Description
Link to publisher's homepage at www.jict.uum.edu.my
Keywords
Backpropagation network, Classification, Rice yield, Pests, Diseases, Weeds, Neural networks (Computer science), Computer programming, Back propagation
Citation
Journal of ICT, vol.3(1), 2004, pages 67-81.