Digital Twin Applications in Food Processing and Supply Chains: A Systematic Review

Digital twin technology creates dynamic, real-time virtual replicas of physical entities across their full lifecycle. Enabled by real-time data acquisition, multi-physics coupled simulation, and intelligent decision optimization, it can accurately characterize the core inherent laws of food systems, including heat and mass transfer, dynamic microbial evolution, and quality deterioration during processing, thus emerging as a key enabler for the digital and intelligent transformation of the global food industry. The food sector faces persistent systemic challenges, including skilled labor shortages, raw material batch variability, processing quality fluctuations, high supply chain losses, and limited responsiveness to diversified consumer demands. By integrating Internet of Things (IoT), artificial intelligence (AI), advanced simulation, and big data analytics, digital twin technology enables precise end-to-end control across the food processing value chain. This paper proposes a four-tier technical architecture (perception, model, simulation, and application layers) and a scenario-based implementation framework for core food processing domains, systematically analyzing the technical pathways, operational boundaries, and research gaps of digital twin applications. We further identify cross-cutting coupling challenges across data, model, process, and governance dimensions, along with corresponding mitigation strategies. Our analysis confirms that digital twin adoption delivers real-time monitoring, predictive quality assurance, and holistic process optimization across the food chain, reducing manual reliance, operational losses, and improving product consistency and production efficiency. Finally, we outline priority research directions to advance the inclusive, standardized, and sustainable application of digital twin technology in the food industry.