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Decision support system for greenhouse tomato yield prediction using artificial intelligence techniques

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Zhang, Fu, Ph.D., Iliescu, Daciana, Hines, Evor L., Leeson, Mark S., 1963- and Adams, S. R. (Steven R.) (2010) Decision support system for greenhouse tomato yield prediction using artificial intelligence techniques. In: Manos, Basil and Matsatsinis, Nikolaos and Paparrizos, Konstantinos and Papathanasiou, Jason, (eds.) Decision Support Systems in Agriculture, Food and the Environment. Hershey, PA, U.S.A.: IGI Global, pp. 155-172. ISBN 9781615208814

Full text not available from this repository.
Official URL: http://dx.doi.org/10.4018/978-1-61520-881-4.ch008

Abstract

This chapter introduces a decision support system which is capable of predicting the weekly yields of tomatoes in a greenhouse. The development of this system involves a set of Artificial Intelligence based techniques, namely Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), and Grey System Theory (GST). The prediction was performed by an ANN using a set of optimised input variables, chosen from all available environmental and measured yield parameters. The reduction and optimisation of the inputs was done using either GAs or GST and compared in terms of the ANN’s performance. It was shown that the use of artificial intelligence based methods can offer a promising approach to yield prediction and compared favourably with traditional methods.

Item Type: Book Item
Subjects: S Agriculture > SB Plant culture
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science > Life Sciences (2010- ) > Biological Sciences ( -2010)
Faculty of Science > Engineering
Faculty of Science > Life Sciences (2010- )
Publisher: IGI Global
Place of Publication: Hershey, PA, U.S.A.
ISBN: 9781615208814
Book Title: Decision Support Systems in Agriculture, Food and the Environment
Editor: Manos, Basil and Matsatsinis, Nikolaos and Paparrizos, Konstantinos and Papathanasiou, Jason
Date: 15 June 2010
Number of Pages: 18
Page Range: pp. 155-172
Identification Number: 10.4018/978-1-61520-881-4.ch008
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
URI: http://wrap.warwick.ac.uk/id/eprint/46526

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