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dc.contributor.authorMartin, Charles Maxwell
dc.date.accessioned2014-03-04T18:24:52Z
dc.date.available2014-03-04T18:24:52Z
dc.date.issued2009-12
dc.identifier.othermartin_charles_m_200912_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms
dc.identifier.urihttp://hdl.handle.net/10724/26098
dc.description.abstractPrevious research has established that large-scale climatological phenomena influence local weather conditions in various parts of the world. These weather conditions have a direct effect on crop yield. Consequently, much research has been done exploring the connections between large-scale climatological phenomena and crop yield. Artificial neural networks have been demonstrated to be powerful tools for modeling and prediction, and can be combined with genetic algorithms to increase their effectiveness. The goal of the research presented in this thesis was to develop artificial neural network models using genetic algorithm-selected inputs in order to predict southeastern US maize yield at various points throughout the year.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectArtificial Neural Networks
dc.subjectGenetic Algorithms
dc.subjectCrop Yield
dc.subjectMaize
dc.subjectTeleconnections
dc.subjectSea Surface Temperature
dc.subjectDecision Support Systems
dc.titleCrop yield prediction using artificial neural networks and genetic algorithms
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence Center
dc.description.majorArtificial Intelligence
dc.description.advisorGerrit Hoogenboom
dc.description.committeeGerrit Hoogenboom
dc.description.committeeWalter D. Potter
dc.description.committeeRon McClendon


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