This study aimed to improve the measurement accuracy of the discrete element method (DEM) simulation parameters of sorghum seeds for enhanced model accuracy. We measured the intrinsic and contact parameters of sorghum seeds, extracted the outline of the seeds by 3D scanning, and used the multi-spherical particle model filling method in the EDEM (Version 2020) software to obtain the simulation model of the seeds. By simulating the free-fall, slope slip, slope rolling, and rolling experiments of sorghum seeds–photosensitive resin material, we calibrated the collision restitution coefficient of the sorghum seeds–photosensitive resin material to 0.690, static friction coefficient to 0.345, and rolling friction coefficient to 0.040. Through the steepest ascent search and central composite design experiments, we calibrated the collision restitution coefficient of sorghum seeds–sorghum seeds to 0.400, the static friction coefficient to 0.450, and the rolling friction coefficient to 0.043. The angle of repose of the parameter combination and the angle of repose of the physical experiment (33.82°) were verified, and the relative errors of the coefficients were 0.7%, 0.25%, and 0.3%, respectively, indicating that the established model had a high simulation accuracy and reliability. These results show that the established sorghum seeds model and the optimally calibrated parameter combination are effective for DEM studies on sorghum seeds.

Discrete element simulation is an effective method to reveal the interaction between tillage components and work objects. However, due to the lack of discrete element modelling parameters of maize root and its mixture with soil, existing tillage models cannot accurately simulate the farmland environment under a no-tillage system. This study developed single maize root (SMR) with different diameters and maize root-soil mixture (MRSM) DEM models based on calibrated parameters through the angle of repose (AOR) tests. First, the Plackett–Burman and the steepest climb tests were performed to identify the range of essential parameters for the AOR of the SMR. Then, the optimal parameters for the SMR and MRSR models were obtained by Box–Behnken design (BBD) testing. The results showed that the static friction coefficient of SMR-SMR and the rolling friction coefficient of SMR-SMR and SMR-steel significantly affected the AOR. In addition, the AOR of MRSM was extremely sensitive to the restitution coefficient and surface energy coefficient of root soil. Based on optimal parameters, the relative errors between the simulated and measured AOR and pixel peak values of the piles’ contour curve were less than 5% for SMR and MRSM. The error of the dynamic AOR of the measured and simulated MSRM was less than 10%. These results indicate that the parameter calibration method and the developed models can be valuable references for DEM simulation for maize stubble and tillage.

Aiming at the unstable filling effect under high-speed operating conditions of the maize mechanical precision metering device, which easily causes the problem of leakage and multiple filling, a novel filling method was proposed to limit the number of seeds accumulation in front of the filling port by a composite seeding tray and improve the filling effect for single-seed. Meanwhile, a quantitative precision filling seed-metering device for maize was presented. The structural parameter design of the key components was completed, and the principle of improving the seed-filling effect was analyzed and elucidated. The optimal type of grooved teeth for the composite seeding tray was selected, and a Box–Behnken orthogonal optimization experiment was conducted using EDEM simulation. The high-speed seed-metering performance optimization results were validated through a platformed performance experiment. The results indicated that the seed-metering device had higher seed supply capacity, better seed-filling effect, and superior seed-metering performance under the type A grooved teeth condition. When the opening height of the seed barrier was 19.4 mm, the depth of the grooved teeth was 1.2 mm, and the operating speed was 10.7 km·h⁻¹, the seed-metering performance was optimal. The passing, repetitive, and miss rates were 95.1%, 1.6%, and 3.3%, respectively. When the operating speed was 8–14 km·h⁻¹, the passing rate of the seed-metering device was higher than 94.1%, the repetitive rate was lower than 2.3%, and the miss rate was lower than 3.7%. This work provides a reference for enhancing the seed-filling effect of mechanical precision metering devices under high-speed operating conditions.