Graphical tracking systems revisited: a practical approach to computer scheduling in horticulture
Harwood, T. D. and Hadley, P. (2004) Graphical tracking systems revisited: a practical approach to computer scheduling in horticulture. In: Fink, M. and Feller, C. (eds.) Proceedings of the International Workshop on Models for Plant Growth and Control of Product Quality in Horticultural Production. Acta Horticulturae. International Society Horticultural Science, Leuven 1, pp. 179-185. ISBN 9066050292
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Graphical tracking is a technique for crop scheduling where the actual plant state is plotted against an ideal target curve which encapsulates all crop and environmental characteristics. Management decisions are made on the basis of the position of the actual crop against the ideal position. Due to the simplicity of the approach it is possible for graphical tracks to be developed on site without the requirement for controlled experimentation. Growth models and graphical tracks are discussed, and an implementation of the Richards curve for graphical tracking described. In many cases, the more intuitively desirable growth models perform sub-optimally due to problems with the specification of starting conditions, environmental factors outside the scope of the original model and the introduction of new cultivars. Accurate specification for a biological model requires detailed and usually costly study, and as such is not adaptable to a changing cultivar range and changing cultivation techniques. Fitting of a new graphical track for a new cultivar can be conducted on site and improved over subsequent seasons. Graphical tracking emphasises the current position relative to the objective, and as such does not require the time consuming or system specific input of an environmental history, although it does require detailed crop measurement. The approach is flexible and could be applied to a variety of specification metrics, with digital imaging providing a route for added value. For decision making regarding crop manipulation from the observed current state, there is a role for simple predictive modelling over the short term to indicate the short term consequences of crop manipulation.
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