Author(s)

Tomasz Kowalski

Affiliation(s)

Faculty of Food Technology, Warsaw University of Technology, Warsaw, Poland.

Corresponding Author

Tomasz Kowalski

ABSTRACT

The globalization of food supply chains has increased the complexity of food safety assurance, making traditional monitoring methods inadequate in identifying potential hazards in real time. This paper explores the integration of Internet of Things (IoT) technologies and Artificial Intelligence (AI) for automated food hazard monitoring. IoT sensors enable real-time collection of critical parameters such as temperature, humidity, gas composition, and microbial activity across the supply chain. These data streams are processed using AI techniques—including machine learning algorithms and deep neural networks—to detect anomalies, classify potential contaminants, and predict spoilage or safety risks before they reach consumers. A case study focusing on cold chain logistics illustrates how such integration enhances traceability, responsiveness, and predictive accuracy. Experimental results show that the combined IoT-AI system improves hazard detection rates by over 40% compared to manual methods, while significantly reducing human error and response time. This work highlights the feasibility, performance, and scalability of AI-powered IoT systems in transforming modern food safety frameworks.

KEYWORDS

IoT; Artificial Intelligence; Food safety; Hazard detection; Real-time monitoring; Machine learning; Cold chain logistics; Smart sensors

CITE THIS PAPER

Tomasz Kowalski. Integration of IoT and Artificial Intelligence for automated food hazard monitoring. Journal of Biotechnology and Food Science. 2025, 2(1): 27-32. DOI: https://doi.org/10.61784/jbfs3006.

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