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dc.contributor.authorKolosov, Dimitrios
dc.contributor.authorFengou, Lemonia-Christina
dc.contributor.authorCarstensen, Jens Michael
dc.contributor.authorSchultz, Nette
dc.contributor.authorNychas, George-John
dc.contributor.authorMporas, Iosif
dc.contributor.editorGrossi, Marco
dc.date.accessioned2023-05-09T15:30:01Z
dc.date.available2023-05-09T15:30:01Z
dc.date.issued2023-04-24
dc.identifier.citationKolosov , D , Fengou , L-C , Carstensen , J M , Schultz , N , Nychas , G-J , Mporas , I & Grossi , M (ed.) 2023 , ' Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems ' , Sensors , vol. 23 , no. 9 , 4233 , pp. 1-17 . https://doi.org/10.3390/s23094233
dc.identifier.issn1424-3210
dc.identifier.otherJisc: 1064844
dc.identifier.otherJisc: 1064844
dc.identifier.otherpublisher-id: sensors-23-04233
dc.identifier.urihttp://hdl.handle.net/2299/26197
dc.description© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).
dc.description.abstractSpectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively.en
dc.format.extent17
dc.format.extent5719897
dc.language.isoeng
dc.relation.ispartofSensors
dc.subjectArticle
dc.subjectfood quality
dc.subjectspectroscopy
dc.subjectmultispectral imaging
dc.subjectembedded systems
dc.subjectComputers
dc.subjectDiagnostic Imaging
dc.subjectNeural Networks, Computer
dc.subjectMeat/microbiology
dc.subjectMachine Learning
dc.subjectAnalytical Chemistry
dc.subjectInformation Systems
dc.subjectInstrumentation
dc.subjectAtomic and Molecular Physics, and Optics
dc.subjectElectrical and Electronic Engineering
dc.subjectBiochemistry
dc.titleMicrobiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systemsen
dc.contributor.institutionSchool of Engineering and Technology
dc.contributor.institutionCentre for Future Societies Research
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionBioEngineering
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85159355087&partnerID=8YFLogxK
rioxxterms.versionofrecord10.3390/s23094233
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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