AI-enabled Efficient and Safe Food Supply Chain [article]

Ilianna Kollia and Jack Stevenson and Stefanos Kollias
<span title="2021-05-01">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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,
more &raquo; ... ders 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.00333v1">arXiv:2105.00333v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/45sy5yjjfjeg7oozqq37s46lmu">fatcat:45sy5yjjfjeg7oozqq37s46lmu</a> </span>
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