Feasibility of impact-acoustic emissions for detection of damaged wheat kernels

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage633en_US
dc.citation.issueNumber3en_US
dc.citation.spage617en_US
dc.citation.volumeNumber17en_US
dc.contributor.authorPearson, T.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.contributor.authorTewfik, A. H.en_US
dc.contributor.authorHaff, R. P.en_US
dc.date.accessioned2015-07-28T11:57:38Z
dc.date.available2015-07-28T11:57:38Z
dc.date.issued2007-05en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractA non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain signal variances and maximums in short-time windows, analysis of the frequency spectrum magnitudes, and analysis of a derivative spectrum. Features were used as inputs to a stepwise discriminant analysis routine, which selected a small subset of features for accurate classification using a neural network. For a network presented with only insect damaged kernels (IDK) with exit holes and undamaged kernels, 87% of the former and 98% of the latter were correctly classified. It was also possible to distinguish undamaged, IDK, sprout-damaged, and scab-damaged kernels.en_US
dc.identifier.doi10.1016/j.dsp.2005.08.002en_US
dc.identifier.eissn1095-4333
dc.identifier.issn1051-2004
dc.identifier.urihttp://hdl.handle.net/11693/11422
dc.language.isoEnglishen_US
dc.publisherElsevier BVen_US
dc.relation.isversionofhttps://doi.org/10.1016/j.dsp.2005.08.002en_US
dc.source.titleDigital Signal Processingen_US
dc.subjectNeural networken_US
dc.subjectSpectral analysisen_US
dc.subjectInsect damage kernelsen_US
dc.subjectSortingen_US
dc.subjectAcoustic emissionsen_US
dc.titleFeasibility of impact-acoustic emissions for detection of damaged wheat kernelsen_US
dc.typeArticleen_US
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