Developing a learning mechanism for a spatial decision support system for medfly control in citrus.

A spatial decision support system (SDSS), designated MedCila was developed for controlling Medfly on citrus in Israel. The development involved four main phases: (1) acquisition of relevant expert and domain knowledge; (2) identification of the relevant criteria and modeling each criterion and the overall decision-making procedure; (3) integration of the MedCila into a GIS environment; and (4) initial evaluation of MedCila performance. The criteria found to be most relevant for control decision-making were the number of flies and the presence of a 'blue eye' in the nearest trap, the host-species susceptibility, the relative development of the Medfly based on accumulative day-degree model, the history of trapping, and the Medfly population in the nearby traps. Binary, linear, logarithmic and biological-based models were developed for the criteria identified. The overall decision-making procedure of the MedCila was based on the Stanford Certainty Theory integrated with a rule-based decision tree. Initial evaluation of the MedCila performance was done by retrospective comparison between the MedCila recommendations and the coordinator decisions. It was shown that the MedCila provides recommendations that are generally accepted by the coordinators; it reduces the number of unnecessary spray actions in the absence of a Medfly threat in space and in time; and it reduces the number of plots for which the coordinator needs to make a decision for.

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