Prediction model and influencing factors analysis method for seed pickup performance of air-suction seeder under multi-physics coupling

Air-suction seeders are essential for precision sowing of small seeds, where the accuracy and stability of the seed pickup process are crucial. To achieve high-precision prediction of seed pickup performance and interpret the influence mechanisms of operating conditions, this study proposes a prediction model and quantitative analysis method for seed pickup qualification rate based on an established multi-physics coupled model. A Uniformity-Oriented Latin Hypercube Sampling method is proposed to generate a uniformly distributed and representative training dataset, derived from a multi-physics coupled model accounting for airflow-particle-vibration in air-suction seeders during field operations. A prediction model for seed pickup qualification rate is developed using the Stacking strategy that integrates multiple elementary learners and a meta-learner. A Lévy flight-improved Black-winged Kite Algorithm (BKA-Lévy) is introduced for model optimization, enabling accurate predictions under varying disc speeds, vacuum magnitudes, and vibration frequencies and amplitudes. The model achieves R 2 of 0.9215, with RMSE and MAE values of 0.0884 and 0.0715, respectively. Bench test results yield an R 2 of 0.9699, further supporting the feasibility of proposed prediction model under the tested bench conditions. Finally, a surrogate model based on Gaussian Process Regression is further developed for explicit parameter-performance mapping. Kernel SHapley Additive exPlanations (Kernel SHAP) and Partial Dependence Plot (PDP) analyzes identify seeder disc speed (30 rpm) and vacuum magnitude (1700 Pa) as model-derived transition points within the parameter ranges, with vibration parameters showing strong coupling effects.