Parameter Optimization Design and Experiment of Root-Cutting Device of Self-Propelled Chinese Cabbage Harvester

Abstract

The root-cutting device is a key device in the mechanical harvesting equipment of Chinese cabbage. Aiming at the problems of high energy consumption of Chinese cabbage harvest cutting and low qualified rate of root cutting, a root cutting device was developed based on the physical and mechanical properties of Chinese cabbage. Plackett-Burman design, steepest ascent, and Box Behnken design were used to screen and evaluate the influencing factors of the cutting system. Analysis of variance was used to determine the degree of influence of each structural parameter on the maximum root-cutting reaction force. The optimal parameter combination of the root-cutting device is determined by integrating the Gray Wolf Optimization algorithm and radial basis function neural network model. Finally, the model performance was evaluated and compared with the original algorithm and other mainstream algorithms, and the optimized root-cutting device was installed on a test prototype for field trials. The experimental results showed that the key factors affecting the maximum root cutting reaction force were cutter speed, walking speed, and cutting pitch angle, and the optimal parameter combinations had values of 202 r/min, 0.28 m/s, and 12°. Compared with other mainstream models, the RBFNN-GWO model outperforms other models in terms of prediction accuracy, convergence speed, and loss error value. The average productivity of the harvester under the optimal parameter combination was 0.13 hm2/h, and the qualified rate of root cutting was 93.16%, which meets the requirements of mechanized harvesting of Chinese cabbage, and provides a theoretical basis for the realization of high-efficiency and low-loss harvesting of Chinese cabbage.

Keywords

• Chinese cabbage
• mechanical harvesting
• root cutting
• parameter optimization
• RBFNN-GWO