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

Dimitrios Kolosov1, Lemonia-Christina Fengou2, Jens Michael Carstensen3

  • 1School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning models analyze multispectral images of meat for microbial quality assessment on embedded systems. The XavierNX platform excels in speed, while Nano and RP4 offer better efficiency and value for food safety monitoring.

Keywords:
embedded systemsfood qualitymultispectral imagingspectroscopy

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Area of Science:

  • Food science and technology
  • Computer science and engineering
  • Spectroscopy and imaging

Background:

  • Assessing microbial populations in meat is crucial for food safety and quality control.
  • Traditional methods for microbial analysis are often time-consuming and labor-intensive.
  • Emerging technologies like spectroscopic imaging and deep learning offer potential for rapid, non-destructive quality assessment.

Purpose of the Study:

  • To develop and evaluate an architecture for estimating microbial populations in meat samples using multispectral imaging.
  • To implement deep learning models on embedded platforms for real-time, on-site food quality analysis.
  • To compare the performance of different deep learning models and embedded hardware for this application.

Main Methods:

  • Multispectral imaging was used to capture data from meat samples stored under various conditions.
  • Deep convolutional neural networks (CNNs) were trained and deployed on embedded platforms (e.g., XavierNX, Nano, RP4).
  • Performance metrics including latency, throughput, efficiency, and value were evaluated for different hardware and data processing setups.

Main Results:

  • The XavierNX platform demonstrated superior performance in terms of low latency and high throughput for microbial population estimation.
  • The Nano and RP4 platforms showed advantages in terms of energy efficiency and cost-effectiveness, respectively.
  • The developed system effectively estimated microbial populations, indicating its potential for practical food quality assessment.

Conclusions:

  • Embedded deep learning systems utilizing multispectral imaging are viable for on-site microbial quality assessment of meat.
  • Hardware selection (XavierNX, Nano, RP4) depends on specific application requirements regarding speed, efficiency, and cost.
  • This approach offers a promising alternative to traditional methods for ensuring food safety and quality.