AI-enabled Efficient and Safe Food Supply Chain

TL;DR

This paper reviews AI techniques applied across the food supply chain, demonstrating their effectiveness in improving food production, energy management, and safety through three experimental case studies.

Contribution

It introduces recent AI methodologies tailored for the food supply chain and provides empirical evidence of their state-of-the-art performance in real-world applications.

Findings

AI models accurately predict plant growth and yield.

Energy consumption in refrigeration systems is optimized.

Optical recognition ensures food safety by verifying expiry dates.

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.