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dc.contributor.authorWoodford, Brendon Jen_NZ
dc.date.available2011-04-07T03:16:49Z
dc.date.copyright2003-12-11en_NZ
dc.identifier.citationWoodford, B. J. (2003, December 11). Connectionist-based intelligent information systems for image analysis and knowledge engineering: applications in horticulture (Thesis, Doctor of Philosophy). Retrieved from http://hdl.handle.net/10523/1469en
dc.identifier.urihttp://hdl.handle.net/10523/1469
dc.description.abstractNew Zealand’s main export earnings come from the primary production area including agriculture, horticulture, and viticulture. One of the major contributors in this area of horticulture is the production of quality export grade fruit; specifically apples. In order to maintain a competitive advantage, the systems and methods used to grow the fruit are constantly being refined and are increasingly based on data collected and analysed by both the orchardist who grows the produce and also researchers who refine the methods used to determine high levels of fruit quality. To support the task of data analysis and the resulting decision-making process requires efficient and reliable tools. This thesis attempts to address these issues by applying the techniques of Connectionist-Based Intelligent Information Systems (CBIIS) for Image Analysis and Knowledge Discovery. Using advanced neurocomputing techniques and a novel knowledge engineering methodology, this thesis attempts to seek some solutions to a set of specific problems that exist within the horticultural domain. In particular it describes a methodology based on previous research into neuro-fuzzy systems for knowledge acquisition, manipulation, and extraction and furthers this area by introducing a novel and innovative knowledge-based architecture for knowledge-discovery using an on-line/real-time incremental learning system based on the Evolving Connectionist System (ECOS) paradigm known as the Evolving Fuzzy Neural Network (EFuNN). The emphases of this work highlights knowledge discovery from these data sets using a novel rule insertion and rule extraction method. The advantage of this method is that it can operate on data sets of limited sizes. This method can be used to validate the results produced by the EFuNN and also allow for greater insight into what aspects of the collected data contribute to the development of high quality produce.en_NZ
dc.description.sponsorshipNew Zealand Foundation for Research Science and Technologyen_NZ
dc.format.mimetypeapplication/pdf
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleConnectionist-based intelligent information systems for image analysis and knowledge engineering: applications in horticultureen_NZ
dc.typeThesisen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages259en_NZ
otago.date.accession2008-11-20 21:28:53en_NZ
otago.schoolInformation Scienceen_NZ
thesis.degree.disciplineInformation Scienceen_NZ
thesis.degree.nameDoctor of Philosophyen_NZ
otago.openaccessOpen
dc.identifier.eprints791en_NZ
otago.school.eprintsKnowledge, Intelligence & Web Informatics Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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