Development of an Optimization Method for Dry-Type Rice Straw Modeling Considering Mechanical Properties Using the Discrete Element Method

Accurate load prediction is essential for optimizing the performance and design of agricultural machinery. However, obtaining field-based load data is challenging due to the limited harvesting period of crops. To address this, the Discrete Element Method (DEM) has been widely applied to simulate crop–machine interactions under controlled virtual conditions. Previous DEM studies on rice straw often assumed uniform mechanical properties throughout the stem, neglecting sectional heterogeneity and limiting the accuracy of tensile and shear response prediction. This study developed an optimized DEM-based modeling approach by dividing rice straw into four sections—Top, Mid, Node, and Bottom—and experimentally determining their mechanical properties for model calibration. The Mid section exhibited the highest average tensile strength (178.71 N), while the Node showed the greatest shear resistance (114.08 N). One-way ANOVA confirmed significant sectional differences in both tensile (F = 18.12, p < 0.001) and shear (F = 23.61, p < 0.001) strengths. Two DEM models were validated: a multi-particle (Model A) and a simplified single-particle (Model B) configuration. Both achieved over 95% prediction accuracy, with Model B reducing computation time by 77.5% (80→18 min). Although the modeling was based on fully dried straw, future studies should incorporate moisture-dependent properties to enhance predictive fidelity. The proposed approach improves both accuracy and efficiency, providing a foundation for raking and baling load simulations.