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
Affiliations
Dimitrios Kolosov
School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Lemonia-Christina Fengou
Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece
Jens Michael Carstensen
Videometer A/S, Hørkær 12B, 2730 Herlev, Denmark
Nette Schultz
Videometer A/S, Hørkær 12B, 2730 Herlev, Denmark
George-John Nychas
Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece
Iosif Mporas
School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
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.
