Data-driven virtual sensor systems for dynamic temperature monitoring along food supply chains

Abstract

Continuous monitoring of perishable food temperatures along supply chains is crucial for quality assurance and reducing food loss and waste. However, cost and installation constraints restrict sensor deployment, compromising the reliability of temperature monitoring. This study proposes a data-driven virtual sensor system that leverages deep learning to integrate multi-source data, enabling temperature estimation at sensor-inaccessible locations and thus reducing dependence on extensive physical sensor deployment. The system was evaluated across postharvest processing, storage, and transport. Results indicate that, with a fixed number of physical sensors, increasing the virtual-to-physical sensor ratio from 16 to 32 maintains the root mean square error below 0.3 °C. Further analysis shows that sensor placement within pallets has minimal impact on performance, whereas the choice of data sources and model architecture exerts a significant influence. Notably, a configuration of one sensor per pallet with a BiLSTM + attention model outperforms shallow networks, demonstrating the potential of data-driven virtual sensor system to enhance temperature monitoring and efficiency along food supply chains.