Examination of wheat kernels for the presence of Fusarium damage and mycotoxins using near-infrared hyperspectral imaging

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dc.contributor.authorBrown, Jennifer
dc.contributor.examiningcommitteeHewko, Mark (Biosystems Engineering) Fernando, Dilantha (Plant Science)en_US
dc.contributor.supervisorPaliwal, Jitendra (Biosystems Engineering) Morrison, Jason (Biosystems Engineering)en_US
dc.date.accessioned2015-01-09T21:33:11Z
dc.date.available2015-01-09T21:33:11Z
dc.date.issued2015-01-09
dc.degree.disciplineBiosystems Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractThe agriculture industry experiences severe economic losses each year when wheat crops become infected with Fusarium and the mycotoxin Deoxynivalenol (DON). This research investigated the feasibility of using near infrared hyperspectral imaging to detect Fusarium damage and DON in Canadian Western Red Spring wheat. Four samples were selected from each grain grade resulting in 16 samples and 240 hyperspectral data cubes. The data cubes were calibrated to the system, the consistent spectra was found and then a 1- nearest neighbour classifier was generated. Grade percentages were computed and used to generate two 3- nearest neighbour classifiers, one for identifying Fusarium damage and the other for identifying DON content. The Fusarium damage classifier had an accuracy of 85% and the DON content classifier had an accuracy of 80%. While a single sample image classification will not replace manual testing, the use of multiple samples from one harvest could reduce manual inspections.en_US
dc.description.noteFebruary 2015en_US
dc.identifier.urihttp://hdl.handle.net/1993/30189
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectWheaten_US
dc.subjectFusariumen_US
dc.subjectNear-Infrared Spectroscopyen_US
dc.subjectHyperspectral Imagingen_US
dc.subjectDeoxynivalenolen_US
dc.titleExamination of wheat kernels for the presence of Fusarium damage and mycotoxins using near-infrared hyperspectral imagingen_US
dc.typemaster thesisen_US
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