AI-enabled Efficient and Safe Food Supply Chain

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.

EDLaaS:Fully Homomorphic Encryption over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting

“…As far as using machine learning is concerned, it is extremely difficult to build and deploy neural network models to forecast agricultural yields due to the aforementioned privacy/sensitivity concerns that mean data for training and using these neural networks are scarce. However, the impact of using machine learning technologies in agri-food supply chains has been shown to be substantial [ 5 , 6 , 7 ]. A solution that involved distributed learning was recently proposed with an application on soy bean yield forecasting [ 8 ], which assumes that distributed training is possible.…”