Qaddoum, Kefaya (2013) Intelligent real-time decision support systems for tomato yield prediction management. PhD thesis, University of Warwick.

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Abstract

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.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	S Agriculture > S Agriculture (General) T Technology > T Technology (General)
Library of Congress Subject Headings (LCSH):	Decision support systems, Real-time data processing, Tomatoes -- Yields -- Mathematical models, Neural networks (Computer science)
Official Date:	May 2013
Dates:	Date Event May 2013 Submitted
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Hines, Evor, 1957-
Sponsors:	Wight Salads Group; Horticultural Development Council (Great Britain) (HDC)
Extent:	276 leaves.
Language:	eng

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