  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.
