Digital twins are coming: Will we need them in supply chains of fresh horticultural produce?. (March 2021)

• Record Type: Journal Article
• Authors: Defraeye, Thijs; Shrivastava, Chandrima; Berry, Tarl; Verboven, Pieter; Onwude, Daniel; Schudel, Seraina; Bühlmann, Andreas; Cronje, Paul; Rossi, René M.
• Is Part Of: Trends in food science & technology. Volume 109(2021)
• Page Start: 245
• Page End: 258
• Publication Date: 2021-03
• Subjects: Postharvest -- Physics-based -- Virtual -- Modeling -- Simulation -- Cyber-physical; Food industry and trade -- Periodicals; Food -- Biotechnology -- Periodicals; 664.005
• DOI: 10.1016/j.tifs.2021.01.025
• Languages: English
• ISSNs: 0924-2244

Abstract

Abstract: Background: Digital twins have advanced fast in various industries, but are just emerging in postharvest supply chains. A digital twin is a virtual representation of a certain product, such as fresh horticultural produce. This twin is linked to the real-world product by sensors supplying data of the environmental conditions near the target fruit or vegetable. Statistical and data-driven twins quantify how quality loss of fresh horticultural produce occurs by grasping patterns in the data. Physics-based twins provide an augmented insight into the underlying physical, biochemical, microbiological and physiological processes, enabling to explain also why this quality loss occurs. Scope and approach: We identify what the key advantages are of digital twins and how the supply chain of fresh horticultural produce can benefit from them in the future. Key findings and conclusions: A digital twin has a huge potential to help horticultural produce to tell its history as it drifts along throughout its postharvest life. The reason is that each shipment is subject to a unique and unpredictable set of temperature and gas atmosphere conditions from farm to consumer. Digital twins help to identify the resulting, largely uncharted, postharvest evolution of food quality. The benefit of digital twins particularly comes forward for perishable species and at low airflow rates. Digital twins provide actionable data for exporters, retailers, and consumers, such as the remaining shelf life for each shipment, on which logistics decisions and marketing strategies can be based. The twins also help diagnose and predict potential problems in supply chains that will reduce food quality and induce food loss. Twins can even suggest preventive shipment-tailored measures to reduce retail and household food losses. Highlights: We highlight differences between physics-based and data-driven digital twins. Digital twins help tailor supply chains to maximize shelf life and reduce food losses. Digital twins convert sensor data to predict postharvest evolution of food quality. Digital twins provide actionable data for exporters, retailers and consumers. Validation is essential to guarantee future trust in digital twins.