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

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Author(s): F. Zhang (University of Warwick, UK), D. D. Iliescu (University of Warwick, UK), E. L. Hines (University of Warwick, UK), M. S. Leeson (University of Warwick, UK)and S. R. Adams (University of Warwick, UK)
Copyright: 2012
Pages: 17
Source title: Machine Learning: Concepts, Methodologies, Tools and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-60960-818-7.ch520

Keywords: Artificial Intelligence / Computational Intelligence / Computer Science & IT / Information Science Reference

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

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