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Maize picking is the main form of maize harvest in China. Maize picking loss accounts for a large proportion of the current maize harvest loss. An experimental study and a theoretical analysis were conducted to explore the influencing factors and rules of maize picking loss. First, the boundary conditions, established by analyzing the mechanism of maize picking, determined the influences of maize picking loss. Then, single-factor experiments and a central composite design (CCD) method were used to determine the influence of various factors and their interactions on maize picking loss. Finally, the models of kernel loss and ear loss were set up to determine the optimal parameter combination of maize picking harvest. Field experiment verification was conducted. The results indicated that the optimal parameters of the maize picking harvest were the rotational speed of pulling rollers of 1120 r/min, operating speed of 1.94 m/s, the inclination of the header of 18° and clearance between the picking plates of 30 mm. By establishing these optimal parameters, the kernel loss rate was 0.065%, and the ear loss rate was 0%. The obtained experimental results and regression models could be used to predict the performance of the maize picking harvest, guide the adjustment of header working parameters, and provide a theoretical basis for reducing the mechanical loss of maize harvesting.

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Abstract. In order to improve the safety and comfort of the vehicles on rural curved roads, the paper proposed a safe curve speed model based on the BP Neural Network. A series of drivers' manual operation state data during cornering were gathered and observed according to the driver experiments under real traffic conditions. Three factors, referring to the speed calculated based on road trajectory parameters, the adhesion workload and the yaw rate computed from the processed data, were used as inputs of the model to obtain the target vehicle speed. Finally, tests verify the applicability of the modified model. It indicates that the developed speed model can adjust to the individual curve speed behavior of each driver.