Grain drying is a complex heat and mass transfer process, which has the characteristics of a significant delay, multidisturbance, nonlinearity, strong coupling, and parameter uncertainty. Artificial intelligence (AI) control technology is suitable for solving such complex control problems. In this paper, the mechanism and data dual-drive with equivalent accumulated temperature (EAT) mutual-window AI-control method for continuous grain drying were proposed, and a control system was established. The experimental verification was carried out on the test platform of continuous grain drying. The results show that the method has the ability of implicit prediction, high accuracy, strong stability and self-adaptive ability, and the maximum control deviation of moisture at the outlet of the dryer is −0.58–0.3%.

In our study, we developed a system to reduce both energy consumption and pollutant discharge during the drying process. We present a new technology, a stationary bed grain-drying test device based on the internal circulation of the drying medium (ICODM). A rice-drying experiment was carried out inside of it, and the influences of air temperature (AT) and air velocity (AV) on the energy and exergy efficiencies (EEE) as well as the improvement potential rate (IPR) and the sustainability index (SI) of the rice-drying process were studied. The following conclusions were obtained: when the rice was dried at a temperature of below 55 °C and an AV across the grain layer of 0.5 m/s, the average EEE during the drying process was 48.27–72.17% and 40.27–71.07%, respectively, demonstrating an increasing trend as the drying medium temperature increased. When the rice was dried using an AV across the grain layer in the range of 0.33–0.5 m/s and a temperature of 40 °C, the two values were 39.79–73.9% and 49.66–71.04%, respectively, demonstrating a decreasing trend as the drying medium flow velocity increased. IPR and SI were 4.1–8.5 J/s and 1.9–2.7, respectively, at a drying temperature of 30–55 °C and an AV of 0.33–0.5 m/s. These conclusions can provide helpful guidance for the optimization and control of the rice-drying process in terms of saving energy.

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.

If paddy is not dried in time after harvesting, it very easily becomes moldy, which causes substantial losses. Inappropriate drying methods also affect the quality and safety of paddy when it enters the drying process. In order to select the optimal drying method, paddy with different moisture content was treated with sun drying, mechanical drying, and late harvesting, and the quality indexes of paddy were tested for safe moisture content. The effects of different drying methods on the quality of paddy were analyzed in terms of burst rate, taste value, appearance quality, and pasting characteristics. A paddy quality index system was constructed, and gray relational analysis (GRA) was used to evaluate comprehensively the paddy quality. The results showed that when the moisture content of paddy was 24.4%, the best quality of paddy was obtained after mechanical drying, when the gray relational degree was 0.996. Timely harvesting and mechanical drying can not only reduce the loss of paddy, but can also ensure the quality and safety of paddy.

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.