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dc.contributor.authorNg, Wartini
dc.date.accessioned2019-09-10T02:40:16Z
dc.date.available2019-09-10T02:40:16Z
dc.date.issued2019-06-28
dc.identifier.urihttp://hdl.handle.net/2123/21071
dc.description.abstractNo soil, no life. Know soil, know life. Soil provides the basis for life. To promote soil security, soil monitoring is essential. However, conventional methods of soil analysis are costly and time-consuming. This thesis explores contemporary data analytics for analyzing soil infrared spectroscopy data. New data analytics take soil infrared spectral data and convert them to soil properties that are useful for assessing its conditions. This thesis deals with issues of sampling, spectral reduction techniques, deep learning models, and application in soil contamination assessment. Soil spectral data has to be trained using machine learning models to provide predictions for soil properties. The effect of sampling size and designs on the model performance were evaluated. Various ways of spectra data dimension reductions were explored using variable selection techniques to prevent model overfitting when a limited number of samples was available. To deal with large data collected from regional and national soil spectral libraries, deep learning techniques were explored. The convolutional neural network (CNN) was demonstrated as a highly accurate method for predicting soil properties on a large database. A method was derived to enable the interpretability of the CNN model. The application of infrared spectroscopy in screening soil contaminants (microplastics and petroleum hydrocarbons) were illustrated. Collectively, this thesis provides novel data analytics that enabled enhanced applications of infrared spectroscopy in soil science.en_AU
dc.publisherUniversity of Sydneyen_AU
dc.publisherFaculty of Scienceen_AU
dc.publisherSchool of Life and Environmental Sciencesen_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectspectroscopyen_AU
dc.subjectvisible-near-infrareden_AU
dc.subjectmid-infrareden_AU
dc.subjectsoil propertiesen_AU
dc.subjectsoil contaminantsen_AU
dc.subjectdeep learningen_AU
dc.titleContemporary data analytics for soil spectroscopyen_AU
dc.typePhD Doctorateen_AU
dc.type.pubtypeDoctor of Philosophy Ph.D.en_AU
dc.description.disclaimerAccess is restricted to staff and students of the University of Sydney . UniKey credentials are required. Non university access may be obtained by visiting the University of Sydney Library.en_AU


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