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Decision Support System for Greenhouse Tomato Yield Prediction using Artificial Intelligence Techniques

Decision Support System for Greenhouse Tomato Yield Prediction using Artificial Intelligence Techniques

F. Zhang, D. D. Iliescu, E. L. Hines, M. S. Leeson, S. R. Adams
Copyright: © 2010 |Pages: 18
ISBN13: 9781615208814|ISBN10: 161520881X|ISBN13 Softcover: 9781616923655|EISBN13: 9781615208821
DOI: 10.4018/978-1-61520-881-4.ch008
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MLA

Zhang, F., et al. "Decision Support System for Greenhouse Tomato Yield Prediction using Artificial Intelligence Techniques." Decision Support Systems in Agriculture, Food and the Environment: Trends, Applications and Advances, edited by Basil Manos, et al., IGI Global, 2010, pp. 155-172. https://doi.org/10.4018/978-1-61520-881-4.ch008

APA

Zhang, F., Iliescu, D. D., Hines, E. L., Leeson, M. S., & Adams, S. R. (2010). Decision Support System for Greenhouse Tomato Yield Prediction using Artificial Intelligence Techniques. In B. Manos, N. Matsatsinis, K. Paparrizos, & J. Papathanasiou (Eds.), Decision Support Systems in Agriculture, Food and the Environment: Trends, Applications and Advances (pp. 155-172). IGI Global. https://doi.org/10.4018/978-1-61520-881-4.ch008

Chicago

Zhang, F., et al. "Decision Support System for Greenhouse Tomato Yield Prediction using Artificial Intelligence Techniques." In Decision Support Systems in Agriculture, Food and the Environment: Trends, Applications and Advances, edited by Basil Manos, et al., 155-172. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-881-4.ch008

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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.

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