Isolation Forests for Anomaly Detection in IoT-Enabled Food Quality Monitoring System

Effective anomaly detection techniques are required to provide the consistency of food safety in light of the growing use of Internet of Things (IoT) technology in food quality monitoring systems. In this research, the Internet of Things (IoT) may be used to monitor food quality using Isolation Forests, a machine learning method that is well-known for its outlier identification capabilities. Using the algorithm's strength in swiftly building random decision trees to extract abnormalities; used it to real-world IoT data streams from food quality sensors and assessed its performance. Our findings show that Isolation Forests are effective in spotting out-of-the-ordinary patterns linked to food quality issues. Timely and precise identification was made possible by the algorithm's high sensitivity to anomalies. Improving food safety standards as a whole is a top priority, and this method shows potential for making IoT-based food quality monitoring systems more responsive and reliable. Incorporating Isolation Forests is a big step towards guaranteeing that food products remain safe and high-quality in this age of smart and linked systems it offers an efficient and scalable solution to deal with the ever-changing nature of anomalies in environments enabled by the IoT.