This thesis describes the research and development of a decision support system for tomato yield prediction. Greenhouse horticulture such as tomato growing offers an interesting test bed for comparing and refining different predictive modelling techniques. The ability to accurately predict future yields, even for as little as days ahead has considerable commercial value to growers. There are several (measurable) causal variables. Some such as temperature are under the grower's control, while others are not. Modern predictive techniques, based on data mining and self-calibrating models, may be able to forecast future yields per unit area of greenhouse better than the biological causal models implicitly now used by growers. Over the past few decades, it has been possible to use the recorded daily environmental conditions in a greenhouse to predict future crop yields. Existing models fail to accurately predict the weekly fluctuations of yield, yet predicting future yields is becoming desperately required especially with weather change. This research project used data collected during seasonal tomato life cycle to develop a decision support system that would assist growers to adjust crops to meet demand, and to alter marketing strategies. The three main objectives are: firstly, to research and utilize intelligent systems techniques for analysing greenhouse environmental variables to identify the variable or variables that most effect yield fluctuations, and Secondly, to research the use of these techniques for predicting tomato yields and produce handy rules for growers to use in decision-making. Finally, to combine some existing techniques to form a hybrid technique that achieves lower prediction errors and more confident results. There are a range of intelligent systems (IS), which are used to process environment data, including artificial neural networks (ANNs), genetic algorithms (GA) and fuzzy logic (FL). A model providing more accurate yield prediction was developed and tested using industrial data from growers. The author develops and investigates the application of an intelligent decision support system for yield management, and to provide an improved prediction model using intelligent systems (IS). Using real-world data, the intelligent system employs a combination of FL, NN and GA. The thesis presents a modified hybrid adaptive neural network with revised adaptive error smoothing, which is based on genetic algorithm to build a learning system for complex problem solving in yield prediction. This system can closely predict weekly yield values of a tomato crop. The proposed learning system is constructed as an intelligent technique and then further optimized. The method is evaluated using real-world data. The results show comparatively good accuracy.Use was made of existing algorithms, such as self-organizing maps (SOMs), and principal component analysis (PCA), to analyse our datasets and identify the critical input variables. The primary conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic algorithm, and fuzzy inference systems, can be successfully applied to the creation of tomato yield predictions, these predictions were better and hence support growers’ decisions. All of these techniques are benchmarked against published existing models, such as GNMM, and RBF.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:589863 |
| Date | January 2013 |
| Creators | Qaddoum, Kefaya |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/58333/ |
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