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AI-Enabled Efficient and Safe Food Supply Chain

1
School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
2
School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Amir Mosavi
Electronics 2021, 10(11), 1223; https://doi.org/10.3390/electronics10111223
Received: 28 April 2021 / Revised: 17 May 2021 / Accepted: 18 May 2021 / Published: 21 May 2021
(This article belongs to the Section Computer Science & Engineering)
This paper provides a review of an emerging field in the food processing sector, referring to efficient and safe food supply chains, ’from farm to fork’, as enabled by Artificial Intelligence (AI). The field is of great significance from economic, food safety and public health points of views. The paper focuses on effective food production, food maintenance energy management and food retail packaging labeling control, using recent advances in machine learning. Appropriate deep neural architectures are adopted and used for this purpose, including Fully Convolutional Networks, Long Short-Term Memories and Recurrent Neural Networks, Auto-Encoders and Attention mechanisms, Latent Variable extraction and clustering, as well as Domain Adaptation. Three experimental studies are presented, illustrating the ability of these AI methodologies to produce state-of-the-art performance in the whole food supply chain. In particular, these concern: (i) predicting plant growth and tomato yield in greenhouses, thus matching food production to market needs and reducing food waste or food unavailability; (ii) optimizing energy consumption across large networks of food retail refrigeration systems, through optimal selection of systems that can be shut-down and through prediction of the respective food de-freezing times, during peaks of power demand load; (iii) optical recognition and verification of food consumption expiry date in automatic inspection of retail packaged food, thus ensuring safety of food and people’s health. View Full-Text
Keywords: deep learning; deep neural networks; LSTM models; attention; latent variables; domain adaptation; yield prediction; growth prediction; greenhouses; energy optimization; refrigerator systems; expiry date; verification; recognition; food packaging deep learning; deep neural networks; LSTM models; attention; latent variables; domain adaptation; yield prediction; growth prediction; greenhouses; energy optimization; refrigerator systems; expiry date; verification; recognition; food packaging
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MDPI and ACS Style

Kollia, I.; Stevenson, J.; Kollias, S. AI-Enabled Efficient and Safe Food Supply Chain. Electronics 2021, 10, 1223. https://doi.org/10.3390/electronics10111223

AMA Style

Kollia I, Stevenson J, Kollias S. AI-Enabled Efficient and Safe Food Supply Chain. Electronics. 2021; 10(11):1223. https://doi.org/10.3390/electronics10111223

Chicago/Turabian Style

Kollia, Ilianna, Jack Stevenson, and Stefanos Kollias. 2021. "AI-Enabled Efficient and Safe Food Supply Chain" Electronics 10, no. 11: 1223. https://doi.org/10.3390/electronics10111223

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