Farm-to-fork smart sensing: integrating AI-enabled, sustainable biosensors for multi-threat detection in modern agri-food systems

Abstract

The increasing concern about food security, climatic changes, depletion of resources, and complex supply chains has created a need for advanced monitoring systems at all stages in the farm-to-table process. Conventional monitoring approaches lack the ability to detect pathogens, contaminants, pesticides, and other threats in terms of their speed, sensitivity, and real-time capabilities. The advancement of intelligent sensing devices, biosensors, Internet of Things (IoT) and AI has brought a new perspective to the process of continuous monitoring and evidence-based decision-making in agri-food systems. With the aid of such technologies, one can promptly recognize biological, chemical, environmental and quality factors while improving traceability and efficiency of operations. Current biosensors have demonstrated sensitivity in the nanogram (ng) to femtomolar (fm) level with response times of less than a minute. In contrast to previous studies which have analyzed these three elements of technology separately, this study incorporates all three into a single farm-to-table approach using peer-reviewed sources within the last 5 years. This paper examines the current trends related to intelligent sensors and AI-powered biosensing, data management architecture, and computing framework utilized within modern food production and supply chain management. In addition, it outlines key challenges associated with system integration, scalability, data management, regulatory compliance, and fair use of technologies. The reviewed studies highlight the potential benefits of integrating AI into intelligent sensing devices to increase the safety and sustainability of agri-food production.