An Intelligent Food Safety Monitoring System Using IoT and Machine Learning Techniques

Abstract

Food safety has emerged as a critical public health concern due to frequent outbreaks of foodborne diseases and contamination incidents globally. Traditional food safety inspection methods are labor‑intensive, periodic, and often unable to detect rapid changes in storage conditions. In this paper, we propose an Intelligent Food Safety Monitoring System that integrates Internet of Things (IoT) sensors with Machine Learning models to continuously monitor environmental parameters affecting food quality. The proposed system real‑time detects anomalies in temperature, humidity, gas concentrations, and microbial growth factors to ensure enhanced food safety and minimize health risks. Experimental results demonstrate an improvement in early detection accuracy and timely alerts, leading to reduced food spoilage and enhanced consumer safety.