IoT-Enabled Smart Food Supply Chain Monitoring for Real-Time Adulteration Detection and Safety Assurance
Abstract
The study proposes a smart food supply chain monitoring system using Internet of Things (IoT) to detect food adulteration in real time, predict the freshness of food and ensure the food safety in a distributed food supply chain. The framework relies on the multimodal IoT sensing, edge computing, hybrid CNN–LSTM deep learning, blockchain-based traceability, and XAI, which can be implemented for continuous monitoring of food quality. The experiments were conducted with milk, edible oils, fruits, vegetables and packaged perishable products with dynamically changing storage and transportation conditions and the results were validated. The success rate of the proposed framework was able to detect the adulteration successfully with the high accuracy (97.14%) and low latency edge assisted monitoring. The multimodal sensor fusion system greatly reduced the number of false-positive alerts and increased the stability of the contamination prediction system as compared to traditional single-sensor systems.