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dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorCunningham, Sally Jo
dc.contributor.authorDela Rue, B. T.
dc.contributor.authorBollen, A. F.
dc.date.accessioned2008-10-20T01:15:06Z
dc.date.available2008-10-20T01:15:06Z
dc.date.issued1998-04
dc.identifier.citationHolmes, G., Cunningham, S. J., Dela Rue, B. T. & Bollen, A. F. (1998). Predicting apple bruising relationships using machine learning. (Working paper 98/7). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1052
dc.description.abstractMany models have been used to describe the influence of internal or external factors on apple bruising. Few of these have addressed the application of derived relationships to the evaluation of commercial operations. From an industry perspective, a model must enable fruit to be rejected on the basis of a commercially significant bruise and must also accurately quantify the effects of various combinations of input features (such as cultivar, maturity, size, and so on) on bruise prediction. Input features must in turn have characteristics which are measurable commercially; for example, the measure of force should be impact energy rather than energy absorbed. Further, as the commercial criteria for acceptable damage levels change, the model should be versatile enough to regenerate new bruise thresholds from existing data. Machine learning is a burgeoning technology with a vast range of potential applications particularly in agriculture where large amounts of data can be readily collected [1]. The main advantage of using a machine learning method in an application is that the models built for prediction can be viewed and understood by the owner of the data who is in a position to determine the usefulness of the model, an essential component in a commercial environment.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikato, Department of Computer Scienceen_US
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.subjectMachine learning
dc.titlePredicting apple bruising relationships using machine learningen_US
dc.typeWorking Paperen_US
uow.relation.series98/7


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