Integrated Deep Learning and Internet of Things: A Review of Technologies, Applications and Challenges in Smart Food Systems

Abstract

The global food system faces unprecedented challenges, including population growth, climate change, resource scarcity, and supply chain inefficiencies. In response, a technological paradigm shift towards "Smart Food Systems" is underway, driven by the integration of the Internet of Things (IoT) and Deep Learning (DL). This paper provides a comprehensive review of the synergies between IoT and DP and their transformative impact across the food value chain. We begin with an overview of the core concepts of IoT and DP. We then trace technological advancements that enable their application, from historical milestones to the roles of enabling technologies such as sensors, cloud/edge computing, 5G, blockchain, and robotics. The paper examines the application of this integrated framework to transform the food system digitally—from smart farming and processing to intelligent retail and supply chain management—with a focus on concepts such as digital twins. Methodologically, this review synthesises recent peer-reviewed articles, industry reports, and case studies published between 2023 and 2025, organising insights across technological evolution, industrial applications, and adoption barriers, and provides a detailed analysis of specific applications within the meat, dairy, baking, and beverage industries, highlighting practical use cases and their benefits. Finally, we identify and discuss the significant technical, economic, and regulatory challenges that impede widespread adoption and propose future research directions, including the potential of Explainable AI (XAI) and federated learning, to realise a more efficient, sustainable, and resilient global food system by 2025 and beyond.