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

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 s

SUBMITTER: Wu Y 

PROVIDER: S-EPMC12523284 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature