Review
Version 1
Preserved in Portico This version is not peer-reviewed
AI-enabled Efficient and Safe Food Supply Chain
Version 1
: Received: 28 April 2021 / Approved: 11 May 2021 / Online: 11 May 2021 (15:46:14 CEST)
A peer-reviewed article of this Preprint also exists.
Kollia, I.; Stevenson, J.; Kollias, S. AI-Enabled Efficient and Safe Food Supply Chain. Electronics 2021, 10, 1223. Kollia, I.; Stevenson, J.; Kollias, S. AI-Enabled Efficient and Safe Food Supply Chain. Electronics 2021, 10, 1223.
Abstract
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). Recent advances in machine and deep learning are used for effective food production, energy management and food labeling. 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 get 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.
Keywords
deep learning; deep neural networks; recurrent LSTM models; attention layers; latent variable extraction; domain adaptation; yield and growth prediction in greenhouses; energy optimization in retail refrigerator systems; verification and recognition of expiry date in retail food packaging.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
