Determination of flowing grain moisture contents by machine learning algorithms using free space measurement data

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dc.authorid0000-0002-2748-0660en_US
dc.contributor.authorYiğit, Enes
dc.contributor.authorDuysak, Hüseyin
dc.date.accessioned2022-05-09T11:53:37Z
dc.date.available2022-05-09T11:53:37Z
dc.date.issued2022en_US
dc.departmentKMÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000785793200020
dc.description.abstractThe measurement of the moisture content of the stored grain in the silos provides the opportunity to take the necessary precautions to store the grain without spoiling. Since it is not possible to obtain all the moisture information of the stored grain with the current methods, in this study, a new method is proposed to determine the moisture content of the grain in real time during the loading processes. For this purpose, popular machine learning (ML) algorithms, i.e., KNN, SVR, and ANN, are used to predict the moisture content of the flowing grain. In order to measure the moisture content of the grain, a free-space electromagnetic measurement setup is constructed. Reflection and transmission coefficients are measured at 103 different frequency points between 1 and 2.48 GHz using a vector network analyzer (VNA) for three different grain types (Bulgur wheat, durum wheat, and corn silage kernel) with moisture content varying between 8% and 25%. In this way, three datasets are constituted as datasets 1-3 corresponding to each grain type. The k-fold cross-validation (k-CV) technique is used to train and test the ML algorithms and the performance of the algorithms is evaluated with five different metrics. In addition, for each grain type, the error rates corresponding to each moisture content are evaluated separately and the relationship between moisture content and performance of algorithms is revealed. While the best results are obtained with KNN for durum wheat and corn silage kernel, SVR method gives the best results for bulgur wheat. This study reveals that the moisture content of flowing grain can he determined, thanks to proper modeling of ML algorithms and measurement setup.en_US
dc.identifier.citationYiğit, E. Duysak, H. (2022). Determination of flowing grain moisture contents by machine learning algorithms using free space measurement data. IEEE Transactİons On Instrumentatİon And Measurement, 71.en_US
dc.identifier.doi10.1109/TIM.2022.3165740
dc.identifier.endpage8en_US
dc.identifier.issn1557-9662
dc.identifier.issue71en_US
dc.identifier.scopus2-s2.0-85128256336
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1109/TIM.2022.3165740
dc.identifier.urihttps://hdl.handle.net/11492/6298
dc.identifier.wosWOS:000785793200020
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.institutionauthorDuysak, Hüseyin
dc.language.isoen
dc.publisherIEEE-Inst Electrİcal Electronİcs Engİneers Incen_US
dc.relation.journalIEEE Transactİons On Instrumentatİon And Measurementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFree Spaceen_US
dc.subjectGrain Moistureen_US
dc.subjectMachine Learning (ML)en_US
dc.subjectMoisture Measurementen_US
dc.subjectCereal Grainen_US
dc.subjectSensoren_US
dc.subjectSystemen_US
dc.titleDetermination of flowing grain moisture contents by machine learning algorithms using free space measurement dataen_US
dc.typeArticle

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