Classification of chestnuts according to moisture levels using impact sound analysis and machine learning

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dc.contributor.authorKurtulmus, Ferhat
dc.contributor.authorOztufekci, Sencer
dc.contributor.authorKavdir, Ismail
dc.date.accessioned2025-01-27T20:51:56Z
dc.date.available2025-01-27T20:51:56Z
dc.date.issued2018
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractIn this study, a prototype system was designed, built and tested to classify chestnuts using impact sound signals and machine learning methods according to moisture contents. Briefly, the system consisted of a shotgun microphone, a sliding platform, an impact surface, a triggering system, a sound device and a computer. Sound signal data were acquired from 2028 chestnut samples with three different moisture levels. Acoustic signals from chestnut samples were filtered to alleviate negative effects of unwanted noise. Four machine learning classifiers using three different feature sets obtained from two feature groups applying feature reduction methods were trained and tested to classify pairs of chestnut moisture group categories as 35% versus 45%, 35% versus 55%, 45% versus 55% (classification with two outputs) and 35% versus 45% versus 55% (classification with three outputs), respectively. The highest classification success (88%) was achieved for the classification application category of 35 versus 55%.
dc.description.sponsorshipTUBITAK, Administration Unit of Scientific Projects [114O783]
dc.description.sponsorshipThis work was supported by TUBITAK, Administration Unit of Scientific Projects (Project No. 114O783, 2016).
dc.identifier.doi10.1007/s11694-018-9897-y
dc.identifier.endpage2834
dc.identifier.issn2193-4126
dc.identifier.issn2193-4134
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85052098596
dc.identifier.scopusqualityQ1
dc.identifier.startpage2819
dc.identifier.urihttps://doi.org/10.1007/s11694-018-9897-y
dc.identifier.urihttps://hdl.handle.net/20.500.12428/25589
dc.identifier.volume12
dc.identifier.wosWOS:000452363700059
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Food Measurement and Characterization
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectChestnut classification
dc.subjectMoisture level
dc.subjectImpact acoustics
dc.subjectMachine learning
dc.titleClassification of chestnuts according to moisture levels using impact sound analysis and machine learning
dc.typeArticle

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