最近欧洲科学家利用Videometer多光谱成像系统，发表了题为“Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems”,Sensors 2023, 23, 4233。

VideometerLab 4是款功能强大的多光谱食品品质可视化无损检测成像品平台，目前已经发表了近400篇文章。

基于嵌入式硬件系统的深层细胞神经网络用于肉类微生物质量评估

摘要 

通过深度机器学习模型元处理的食品样本的光谱传感器成像可用于评估样本的质量。本文提出了一种使用多光谱成像和深度卷积神经网络估计肉类样本中微生物种群的架构。深度学习模型在嵌入式平台上运行，而非在单独的计算机或云服务器上离线运行。使用不同的肉类样品储存条件，并对各种深度学习模型和嵌入式平台进行了评估。此外，在不同的数据预处理和成像类型设置上，对硬件板的延迟、通量、效率和价值进行了评估。实验结果分别显示了XavierNX平台在延迟和通量方面的优势，Nano和RP4在效率和价值方面的具有优势。

关键词：

食品质量；光谱学；多光谱成像；嵌入式系统

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

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.

Keywords: 

food quality; spectroscopy; multispectral imaging; embedded systems