Integrating advanced imaging techniques with Industry 4.0 technologies for real-time quality monitoring in the agri-food sector: A review

• Integration of Industry 4.0 technologies with advanced imaging techniques for real-time agri-food quality monitoring were summarized. • Highlights the real-world industrial application, where imaging and Industry 4.0 technologies were successfully adopted in a processing line. • The Unified Imaging Centric Quality & Traceability (ICQT) framework for the oilseed processing were proposed. • Future research requirements focusing on sensitivity, connectivity, and computation were proposed. . The digital transformation of the agri-food system is driven by the need for real-time quality assurance, traceability, and operational efficiency. When advanced technologies are adopted over traditional methods, they enable rapid, non-destructive, and real-time quality assessment throughout the supply chain. This review explores the practical implications of integrating imaging techniques with Industry 4.0 technologies. The article's framework mainly focuses on an industrial application perspective by providing an extensive comparative analysis of how these technologies are being adopted in industries for real-time monitoring. Imaging techniques provide important physical, chemical, and microbial information on food quality across all stages in the industrial processing line. Whereas, Industry 4.0 technologies enable automation, real-time data analysis, and interconnected sensors to optimize efficiency and decision-making across the food processing supply chain. Combined practical implications emphasize improved detection of contamination, real-time grading, and predictive estimation of shelf-life, as well as adaptive control in unit operations. Unlike previous studies restricted to laboratory-scale processes, this article discusses the existing industrial implementations, where imaging and Industry 4.0 technologies have already been adopted in very few processing lines. However, their adoption faces numerous barriers, and a case study highlighting implementation failure is discussed. This review bridges the gap between laboratory research and full-scale industrial adoption, facilitating the imaging-driven Industry 4.0 solution for the digital transformation in the agri-food system. .