INTEGRATION OF IOT AND ARTIFICIAL INTELLIGENCE FOR AUTOMATED FOOD HAZARD MONITORING
Keywords:
IoT, Artificial Intelligence, Food safety, Hazard detection, Real-time monitoring, Machine learning, Cold chain logistics, Smart sensorsAbstract
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.References
[1] Aworh OC. Food safety issues in fresh produce supply chain with particular reference to sub-Saharan Africa. Food Control, 2021, 123: 107737.
[2] Jin J, Xing S, Ji E, et al. XGate: Explainable reinforcement learning for transparent and trustworthy API traffic management in IoT sensor networks. Sensors (Basel, Switzerland), 2025, 25(7): 2183.
[3] Olufemi OI, Ayeni O, Olagoke-Komolafe OE. Advancing real-time predictive systems for listeria and E coli detection in meat processing facilities across the USA, 2024.
[4] Xing S, Wang Y, Liu W. Multi-dimensional anomaly detection and fault localization in microservice architectures: A dual-channel deep learning approach with causal inference for intelligent sensing. Sensors (Basel, Switzerland), 2025.
[5] Balas VE, Kumar R, Srivastava R, eds. Recent trends and advances in artificial intelligence and internet of things. 2020.
[6] Liu Y, Guo L, Hu X, et al. A symmetry-based hybrid model of computational fluid dynamics and machine learning for cold storage temperature management. Symmetry, 2025, 17(4): 539.
[7] da Costa TP, Gillespie J, Cama-Moncunill X, et al. A systematic review of real-time monitoring technologies and its potential application to reduce food loss and waste: Key elements of food supply chains and IoT technologies. Sustainability, 2022, 15(1): 614.
[8] Pal A, Kant K. Smart sensing, communication, and control in perishable food supply chain. ACM Transactions on Sensor Networks 2020, 16(1): 1–41.
[9] Ahmad Z, Shahid Khan A, Nisar K, et al. Anomaly detection using deep neural network for IoT architecture. Applied Sciences, 2021, 11(15): 7050.
[10] Stavropoulou E, Bezirtzoglou E. Predictive modeling of microbial behavior in food. Foods 2019, 8(12): 654.
[11] Mu W, Kleter GA, Bouzembrak Y, et al. Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools. Comprehensive Reviews in Food Science and Food Safety, 2024, 23(1): e13296.
[12] Guo L, Hu X, Liu W, et al. Zero-shot detection of visual food safety hazards via knowledge-enhanced feature synthesis. Applied Sciences, 2025, 15(11): 6338.
[13] Firoozi AA. Intelligent decision-making frameworks. In: Neuromorphic computing: transforming disaster management and resilience in civil engineering. Cham: Springer Nature Switzerland, 2024: 57–66.
[14] Agnisarman S, Lopes S, Madathil KC, et al. A survey of automation-enabled human-in-the-loop systems for infrastructure visual inspection. Automation in Construction, 2019, 97: 52–76.
[15] Vilas-Boas JL, Rodrigues JJ, Alberti AM. Convergence of distributed ledger technologies with digital twins, IoT, and AI for fresh food logistics: Challenges and opportunities. Journal of Industrial Information Integration, 2023, 31: 100393.
[16] Boubiche DE, Athmani S, Boubiche S, et al. Cybersecurity issues in wireless sensor networks: Current challenges and solutions. Wireless Personal Communications, 2021, 117: 177–213.
[17] Dakhia Z, Russo M, Merenda M. AI-enabled IoT for food computing: Challenges, opportunities, and future directions. Sensors (Basel, Switzerland), 2025, 25(7): 2147.
[18] Rejeb A, Rejeb K, Zailani S, et al. Integrating the Internet of Things in the halal food supply chain: A systematic literature review and research agenda. Internet of Things, 2021, 13: 100361.
[19] Misra NN, Dixit Y, Al-Mallahi A, et al. IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal, 2020, 9(9): 6305–6324.
[20] Pal A, Kant K. Smart sensing, communication, and control in perishable food supply chain. ACM transactions on sensor networks (TOSN), 2020, 16(1): 1-41.
[21] Nasser N, Khan N, Karim L, et al. An efficient time-sensitive data scheduling approach for wireless sensor networks in smart cities. Computer Communications, 2021, 175: 112–122.
[22] Astill J, Dara RA, Campbell M, et al. Transparency in food supply chains: A review of enabling technology solutions. Trends in Food Science & Technology, 2019, 91: 240–247.
[23] Mazhar T, Irfan HM, Haq I, et al. Analysis of challenges and solutions of IoT in smart grids using AI and machine learning techniques: A review. Electronics, 2023, 12(1): 242.
[24] Eltouny K, Gomaa M, Liang X. Unsupervised learning methods for data-driven vibration-based structural health monitoring: A review. Sensors (Basel, Switzerland), 2023, 23(6): 3290.
[25] Qi R. Interpretable slow-moving inventory forecasting: A hybrid neural network approach with interactive visualization, 2025.
[26] Qadri SAA, Huang NF, Wani TM, et al. Advances and challenges in computer vision for image-based plant disease detection: A comprehensive survey of machine and deep learning approaches. IEEE Transactions on Automation Science and Engineering, 2024.
[27] Misra N N, Dixit Y, Al-Mallahi A, et al. IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of things Journal, 2020, 9(9): 6305-6324.
[28] Mustafa MFMS, Namasivayam N, Demirovic A. Food cold chain logistics and management: A review of current development and emerging trends. Journal of Agriculture and Food Research, 2024, 101343.
[29] Wang J, Tan Y, Jiang B, et al. Dynamic marketing uplift modeling: A symmetry-preserving framework integrating causal forests with deep reinforcement learning for personalized intervention strategies. Symmetry, 2025, 17(4): 610.
[30] Yeh RMM, Taha BA, Bachok NN, et al. Advanceme