Development and verification of a graphical detection system for multi-field interactions in stored grain based on LF-NMR

Abstract

Stored grain is a complex ecosystem in which intricate multi-field interactions exist among abiotic and biotic factors, as well as the surrounding environment. Clearly understanding these interactions is crucial for ensuring grain storage security. This study developed a graphical detection system based on low-field nuclear magnetic resonance (LF-NMR) technology, which consists of an LF-NMR imaging analyzer, a small grain container, and dedicated software. This system can simultaneously detect the temperature, moisture, and humidity of a grain sample stored in the grain container and visually present the cloud maps of these fields through the dedicated software. To verify the system's performance, two laboratory storage experiments with paddy rice samples were conducted for 15 d. The results indicated that the measured cloud maps could accurately depict the variations in the temperature, moisture, and humidity fields within the stored paddy rice samples during the storage period. The areas with potential risks of fungal growth, grain sprouting, and moisture condensation due to the multi-field interactions could also be identified through the cloud maps, which demonstrated the credible performance of the system. This system could provide a new technical means to uncover the complex coupling relationships within grain storage ecosystems.