Parameter calibration of the simulation model for coated rice seeds in UAV seeding based on discrete element methodology

The discrete element method (DEM) is widely used in agricultural mechanization, but accurate parameter calibration for UAV seeding simulation of coated rice seeds remains challenging due to empirical parameter selection and single-stage optimization in existing studies. This study combined physical and simulation experiments to calibrate DEM parameters for coated rice seeds (CRS). Physical tests measured the intrinsic parameters and actual repose angle (18.84°). A three-stage progressive optimization strategy was then adopted: Plackett-Burman test to screen significant parameters, steepest ascent test to narrow the optimal range, and quadratic rotation orthogonal combination test to establish a regression model. Four significant parameters were identified: the CRS-CRS coefficient of restitution (C), the CRS-CRS coefficient of static friction (D), the CRS-CRS coefficient of rolling friction (E), and the CRS-stainless steel coefficient of static friction (G). The optimal combination was determined as 0.35, 0.22, 0.09, and 0.51, respectively. Verification using static repose angle, dynamic repose angle, and UAV total seeding rate showed average relative errors below 5% for all three indices. These results demonstrate that the calibrated parameters are reliable for DEM simulation of coated rice seeds, and the proposed multi-stage method effectively improves calibration accuracy and model applicability for UAV seeding operations.