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: © 2012 |Pages: 17
DOI: 10.4018/978-1-60960-818-7.ch520
OnDemand:
(Individual Chapters)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

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.
Chapter Preview
Top

Background

As mentioned above, tomato growers are sometimes contracted to sell agreed quantities of produce to supermarkets. However, tomato yields often vary from week to week, and so the ability to accurately predict future yields would give them a competitive advantage. If a grower is forecast to have insufficient fruits in a given week they could source additional produce from elsewhere, and if they are predicted to have excess fruits they could look for alternative markets or arrange promotions. As a result there has been considerable interest from growers and researchers for developing tomato yield prediction systems. (S.R. Adams, Cockshull, & Cave, 2001; S.R. Adams, Valdes, Cave, & Fenlon, 2001)

Complete Chapter List

Search this Book:
Reset