Future sensors and digital twins to improve perishable food quality during distribution and production

To improve the performance of current supply chains of perishable food products in terms of product quality, greenhouse gas emissions and food waste prevention, we submit that in each stage of the supply and production chain real-time access to supply chain conditions and product information is needed in order to make optimal decisions. To realise this, we need (i) advanced IoT sensor systems to monitor products in the supply chain, (ii) real-time data access and data integration to create the full picture of what is going on in the supply chain, and (iii) relevant models that allow for prediction of product characteristics at each moment of its lifetime. With these elements in place, a digital twin of the product in the food supply chain is created and continuously updated. The digital twin allows for simulation of future behaviour in various scenarios, and thereby enables chain actors to make optimal decisions at each moment in time.

In this project, we will implement two prototypes of digital twins with three use cases. The digital twin of the fresh supply chain, in which the emphasis lies on the long transportation and quality development of tropical fruits such as bananas and on non-destructive quality sensing of glasshouse vegetables such as tomatoes. And the digital twin of the meat production chain, in which the emphasis lies on individual quality monitoring of carcasses.

The project is a close collaboration between system and data integrators, sensor developers, use case partners and research institutes Wageningen Research and imec as part of their collaboration in OnePlanet. OnePlanet is an innovation center for chip and digital technology in agri, food, health and environment. It is a collaboration between nano-technology R&D institute imec, Radboud University, Radboudumc and Wageningen University & Research.

The project has economic and societal impact by reducing food waste, increasing product quality, reducing greenhouse gas emission, and increasing the profitability of the food chains. The project contributes to the MMIP goal of showing how the digital transformation can take place in practice and what its benefits are. Use of the innovative sensor systems and the digital twins results in real-time status information and decision support. This opens possibilities for advanced quality-based control allowing for more efficient food chains with better matching supply and demand, more insight in the quality of the food products and less food waste.