Food security is intrinsically linked to maintaining optimal physical health and promoting active lifestyles. Stored Grain Ecosystems (SGEs) are complex systems comprising a range of grains, microorganisms, and environmental elements. To ensure sustainable grain storage and promote food-friendly SGEs, careful regulation and monitoring of these factors are vital. This review traces the evolution of the Eco-concept of stored grain in China, focusing on micro- and macro-structural composition, the Multi-field/Re-coupling structure, and Smart Construction of SGEs, while introducing the four development lines and Wisdom Methodology of SGEs. The current status and challenges of SGEs in China are also discussed. The Eco-concept of stored grain in China has progressed through the initial exploration period, formation and practice periods, and has now entered its fourth stage, marked by a shift to include interactions of multiple biological fields. This evolution extends beyond the traditional binary relationship and offers emerging technologies greater scope for scientific and intelligent theoretical analysis of grain storage practices. The Wisdom Methodology employs a multifaceted, Mechanism and Data-driven approach, incorporating four driving methods, and is now widely recognized as a leading strategy for researching Smart Grain Systems. Digital Twin technology enables precise simulations and mappings of real-world SGEs in a virtual environment, supporting accurate assessments and early warnings for issues concerning grain conditions. Driven by Mechanism and Data, Digital Twin solutions are a pioneering trend and emerging hotspot with vast potential for enhancing the intelligence and wisdom of future grain storage processes. Overall, this review provides valuable guidance to practitioners for advancing high-quality Smart Grain Systems, enhancing sustainable and intelligent grain storage practices.

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%). 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.

Current grain monitoring systems often rely on isolated data points, such as point-based temperature measurements, which limits their ability to assess grain conditions holistically. This study proposes a novel Mechanism and Data Driven (MDD) framework integrating physical mechanisms with real-time sensor data to enable comprehensive grain condition perception. The novel model parameter initialization method achieves high-precision results, demonstrating only a 1.89% error in Day-2 low-temperature zone prediction (27.02 m² measured vs. 26.52 m² simulated) and accurately reconstructs temperature fields (≤0.5℃ deviation in YOZ plane) while capturing spatiotemporal dynamics of low-temperature zones. Validation shows superior simulation fidelity (SSIM >0.97 in cloud map comparisons). Compared to the initial values, the simulated total area of low-temperature zones in XOZ plane decreased by 22.97%. Among these, Zone 2 (Center Area) showed a 25.30% reduction, while Zone 1 (near the south exterior wall) exhibited a more substantial decline of 27.64%, higher than the 20.35% reduction in Zone 3. High-temperature zones exhibit low moisture content, whereas low-temperature zones retain elevated moisture levels (>14%). A strong positive correlation exists between relative humidity and temperature fields. As the temperature field progressively homogenizes, it induces corresponding uniformity in humidity distribution. The proposed framework provides a comprehensive approach to grain condition monitoring, delivering both holistic insights and actionable data for smart ventilation control, early condensation risk warnings, and mold prevention. It establishes a robust data foundation and practical methodologies for intelligent storage management, ultimately reducing post-harvest grain.