Mechanism and Data-Driven Grain Condition Information Perception Method for Comprehensive Grain Storage Monitoring

TL;DR

A new framework combines physical models and sensor data to monitor grain storage conditions more accurately, helping prevent spoilage and losses.

Contribution

A novel framework integrating physical mechanisms with sensor data for comprehensive grain storage monitoring.

Findings

The novel initialization technique achieved 1.89% error in low-temperature zone predictions.

Temperature fields were reconstructed with ≤0.5 °C deviation and SSIM > 0.97 cloud map fidelity.

Low-temperature zones showed 22.97% area reduction with strong temperature-humidity correlation.

Abstract

Conventional grain monitoring systems often rely on isolated data points (e.g., point-based temperature measurements), limiting holistic condition assessment. This study proposes a novel Mechanism and Data Driven (MDD) framework that integrates physical mechanisms with real-time sensor data. The framework quantitatively analyzes solar radiation and external air temperature effects on silo boundaries and introduces a novel interpolation-optimized model parameter initialization technique to enable comprehensive grain condition perception. Rigorous multidimensional validation confirms the method’s accuracy: The novel initialization technique achieved high precision, demonstrating only 1.89% error in Day-2 low-temperature zone predictions (27.02 m2 measured vs. 26.52 m2 simulated). Temperature fields were accurately reconstructed (≤0.5 °C deviation in YOZ planes), capturing spatiotemporal dynamics with ≤0.45 m2 maximum low-temperature zone deviation. Cloud map comparisons showed superior simulation fidelity (SSIM > 0.97). Further analysis revealed a 22.97% reduction in total low-temperature zone area (XOZ plane), with Zone 1 (near south exterior wall) declining 27.64%, Zone 2 (center) 25.30%, and Zone 3 20.35%. For dynamic evolution patterns, high-temperature zones exhibit low moisture (<14%), while low-temperature zones retain elevated moisture (>14%). A strong positive correlation between temperature and relative humidity fields; temperature homogenization drives humidity uniformity. The framework enables holistic monitoring, providing actionable insights for smart ventilation control, condensation risk warnings, and mold prevention. It establishes a robust foundation for intelligent grain storage management, ultimately reducing post-harvest losses.

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