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dc.contributor.supervisorJayas, Digvir S.(Biosystems Engineering) Paliwal, Jitendra(Biosystems Engineering)en
dc.contributor.authorSingh, Chandra B.
dc.date.accessioned2009-09-14T21:53:39Z
dc.date.available2009-09-14T21:53:39Z
dc.date.issued2009-09-14T21:53:39Z
dc.identifier.citationSingh, C.B., D.S. Jayas, J. Paliwal, and N.D.G. White (2007). Fungal detection in wheat using near-infrared hyperspectral imaging, Transactions of the ASABE 50(6): 2171-2176.en
dc.identifier.citationSingh, C.B.., D.S. Jayas, J. Paliwal, and N.D.G. White (2009). Detection of sprouted and midge-damaged wheat kernels using near-infrared hyperspectral imaging, Cereal Chemistry 86(3): 256-260.en
dc.identifier.citationSingh, C.B., D.S. Jayas, J. Paliwal, and N.D.G. White. 2009. Detection of insect- damaged wheat kernels using near-infrared hyperspectral imaging, Journal of Stored Products Research 45(3): 151-158.en
dc.identifier.urihttp://hdl.handle.net/1993/3217
dc.description.abstractWheat grain quality is defined by several parameters, of which insect and fungal damage and sprouting are considered important degrading factors. At present, Canadian wheat is inspected and graded manually by Canadian Grain Commission (CGC) inspectors at grain handling facilities or in the CGC laboratories. Visual inspection methods are time consuming, less efficient, subjective, and require experienced personnel. Therefore, an alternative, rapid, objective, accurate, and cost effective technique is needed for grain quality monitoring in real-time which can potentially assist or replace the manual inspection process. Insect-damaged wheat samples by the species of rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum); fungal-damaged wheat samples by the species of storage fungi namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger; and artificially sprouted wheat kernels were obtained from the Cereal Research Centre (CRC), Agriculture and Agri-Food Canada, Winnipeg, Canada. Field damaged sprouted (midge-damaged) wheat kernels were procured from five growing locations across western Canada. Healthy and damaged wheat kernels were imaged using a long-wave near-infrared (LWNIR) and a short-wave near-infrared (SWNIR) hypersprctral imaging systems and an area scan color camera. The acquired images were stored for processing, feature extraction, and algorithm development. The LWNIR classified 85-100% healthy and insect-damaged, 95-100% healthy and fungal-infected, and 85-100% healthy and sprouted/midge-damaged kernels. The SWNIR classified 92.7-100%, 96-100% and 93.3-98.7% insect, fungal, and midge-damaged kernels, respectively (up to 28% false positive error). Color imaging correctly classified 93.7-99.3%, 98-100% and 94-99.7% insect, fungal, and midge-damaged kernels, respectively (up to 26% false positive error). Combined the SWNIR features with top color image features correctly classified 91-100%, 99-100% and 95-99.3% insect, fungal, and midge- damaged kernels, respectively with only less than 4% false positive error.en
dc.format.extent1788422 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectHyperspectral imagingen
dc.subjectNear-infrareden
dc.subjectMachine visionen
dc.subjectGrain qualityen
dc.titleDetection of insect and fungal damage and incidence of sprouting in stored wheat using near-infrared hyperspectral and digital color imagingen
dc.typeinfo:eu-repo/semantics/doctoralThesis
dc.typedoctoral thesisen_US
dc.degree.disciplineBiosystems Engineeringen_US
dc.contributor.examiningcommitteeWhite, Noel D.G.(Biosystems Engineering) Thomas, Gabriel (Electrical and Computer Engineering) Sokhansanj, Shahab (Chemical & Biological Engineering, University of British Columbia)en
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.noteOctober 2009en


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