dc.contributor.author | Kolosov, Dimitrios | |
dc.contributor.author | Fengou, Lemonia-Christina | |
dc.contributor.author | Carstensen, Jens Michael | |
dc.contributor.author | Schultz, Nette | |
dc.contributor.author | Nychas, George-John | |
dc.contributor.author | Mporas, Iosif | |
dc.contributor.editor | Grossi, Marco | |
dc.date.accessioned | 2023-05-09T15:30:01Z | |
dc.date.available | 2023-05-09T15:30:01Z | |
dc.date.issued | 2023-04-24 | |
dc.identifier.citation | Kolosov , 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.issn | 1424-3210 | |
dc.identifier.other | Jisc: 1064844 | |
dc.identifier.other | Jisc: 1064844 | |
dc.identifier.other | publisher-id: sensors-23-04233 | |
dc.identifier.uri | http://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.abstract | Spectroscopic 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.extent | 17 | |
dc.format.extent | 5719897 | |
dc.language.iso | eng | |
dc.relation.ispartof | Sensors | |
dc.subject | Article | |
dc.subject | food quality | |
dc.subject | spectroscopy | |
dc.subject | multispectral imaging | |
dc.subject | embedded systems | |
dc.subject | Computers | |
dc.subject | Diagnostic Imaging | |
dc.subject | Neural Networks, Computer | |
dc.subject | Meat/microbiology | |
dc.subject | Machine Learning | |
dc.subject | Analytical Chemistry | |
dc.subject | Information Systems | |
dc.subject | Instrumentation | |
dc.subject | Atomic and Molecular Physics, and Optics | |
dc.subject | Electrical and Electronic Engineering | |
dc.subject | Biochemistry | |
dc.title | Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems | en |
dc.contributor.institution | School of Engineering and Technology | |
dc.contributor.institution | Centre for Future Societies Research | |
dc.contributor.institution | Department of Engineering and Technology | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Communications and Intelligent Systems | |
dc.contributor.institution | BioEngineering | |
dc.contributor.institution | Centre for Engineering Research | |
dc.contributor.institution | School of Computer Science | |
dc.description.status | Peer reviewed | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85159355087&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.3390/s23094233 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |