Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems

Dimitrios Kolosov, Lemonia-Christina Fengou, Jens Michael Carstensen, Nette Schultz, George-John Nychas, Iosif Mporas, Marco Grossi (Editor)

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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.
Original languageEnglish
Article number4233
Pages (from-to)1-17
Number of pages17
JournalSensors
Volume23
Issue number9
Early online date24 Apr 2023
DOIs
Publication statusPublished - 24 Apr 2023

Keywords

  • Article
  • food quality
  • spectroscopy
  • multispectral imaging
  • embedded systems
  • Computers
  • Diagnostic Imaging
  • Neural Networks, Computer
  • Meat/microbiology
  • Machine Learning

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