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Artificial Intelligence of Things-Enhanced Automated Surveillance System for Global Antimicrobial Resistance in Food Supply Chain

LIU, J.; Hua, M. Z.; Yan, X.; Ma, L.; Li, S.; Wang, Y.; Yang, T.; He, Y.; Konkel, M. E.; Greta, G.; Alter, T.; Feng, J.; Liu, V. Q.; Lu, X.

2026-02-11 microbiology
10.64898/2026.02.10.705121 bioRxiv
Show abstract

Antimicrobial resistance (AMR) threatens food safety across the farm-to-fork continuum. Real-time surveillance is crucial to mitigate its global escalation, yet conventional antimicrobial susceptibility testing (AST) remains slow, labor-intensive, and impractical for large-scale monitoring. We developed an Artificial Intelligence of Things (AIoT)-integrated multiplex microfluidic platform enabling automated AMR surveillance of pathogens in food supply chain. Each node combines a single-board AIoT controller (Orange Pi 5B), portable incubator, colorimetric microfluidic chips, and environmental sensors, reducing costs by 98% compared with standard AST. A lightweight YOLOv5 model embedded in the controller achieved >99% accuracy in identifying bacterial growth and inhibition under antibiotic pressure, showing 96% and 95% agreement with standard results for Salmonella and Campylobacter, respectively. Data are synchronized to a cloud server for real-time aggregation and early resistance warning. This fully automated and low-cost system minimizes human error and workload, providing a scalable sample-to-answer solution for AMR surveillance in global agri-food system.

Published in Journal of Advanced Research · training set

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