On account of a lack of suitable and specialized harvesting equipment for cabbage species and planting modes in China, in this study, a type of 4GCSD-1200 type cabbage harvester was designed to further optimize the working performance of the cabbage harvester. First, the structure and working principles of the harvester were introduced, and the cabbage harvesting process was analyzed. Based on the test method and theoretical analysis, a single-factor test was carried out on the main working parameters of the sample machine, the advancing speed, rotating speed of the pulling roller, rotating speed of the conveyor belt, and the cutter-head were taken as independent variables, and the qualifying rate of cabbage harvesting was taken as the response value. According to the Box–Behnken test design principles, a four-factor three-level response surface analysis was adopted to establish a mathematical model between all test factors and the qualifying rate of cabbage harvesting, then all test factors and their interaction effects were analyzed. The test results showed that the optimal working parameters of the harvester were: the advancing speed was 1.1 km/h, the rotating speed of the pulling roller was 90 r/min, the rotating speed of the conveyor belt was 205 r/min, and the rotating speed of the cutter-head was 395 r/min. The verification test results showed that the qualifying rate of cabbage harvesting was 96.3%, showing a good harvesting effect, with uniformly cut notches and a low damage rate. The test indicates that by optimizing the working parameters, the damage during the mechanized harvesting of cabbage can be reduced and the qualifying rate of harvesting can be improved; the working effect could, therefore, satisfy the requirements of market harvesting.

In order to explore the mechanism and influence mechanism of cabbage harvest damage, a low-loss cabbage harvest test platform was designed on the basis of combining the physical characteristics of cabbage with the mechanical characteristics of mechanical harvest and the cabbage harvest operation process. Through the design of key components of the test platform harvesting, the key parameters of the pulling-out device, the reel device, the flexible clamping and conveying device, and the double-disc cutting device were determined. The movement changes of cabbage during pulling out, conveying, and cutting were analyzed to clarify the process of damage generation and critical conditions of damage in cabbage harvesting operations. The test results showed that when the speed of the pulling out device was controlled at 80–120 r/min, the speed of the clamping and conveying device was controlled at 120–240 r/min, and the speed of the double disc cutter was controlled at 140–180 r/min, the average success rate of pulling on the low-loss harvesting test platform was 92.7%; the average damage rate of the pulling process was 7.32%; the average success rate of clamping and conveying was 88.6%; the average damage rate of the clamping and conveying link was 12%; the average success rate of root cutting was 89.3%; and the average damage rate of the cutting link was 11.34%. The average qualified rate of harvesting in the pulling link was 86.7%, the average qualified rate of harvesting in the clamping and conveying link was 75.3%, and the average qualified rate of harvesting in the cutting link was 77.3%. All the performance indicators meet the design requirements and relevant standards, and the research results can provide a reference for the development and structural improvement of low-loss harvesting equipment for cabbage.

Neural Network has been used in approximation of dead zone nonlinearity when modeling the manipulator control systems. However the existed method fail to minimize the possible input saturation effect and the NN mapping accuracy also be degraded, which leads to degrading in tracking precise and accuracy. To establish an accurate control model of the tea picking robot, an adaptive compensator of m-RBF (modified radial basis function neural network) and adaptive law were designed aimed at the nonlinearity units and dead zone. The closed-loop tracking error of the proposed control system is eventually going to be stable and bounded. A simulation was carried out with Simulink, which show that m-RBF provides a good approximation to characteristic of Dead zone nonlinearity, and that the control scheme based on m-RBF had a excellent and stable tracking accuracy. The tea picking experiment with six-axis manipulator verifies the effectiveness of the proposed algorithm, in which the proposed method got a higher score of 95.3, near two times that of traditional PID control methods. It can be drown that the m-RBF has a faster learning rate and can avoid local minima; the control scheme based on m-RBF has a excellent performance on control accuracy, robustness and self-adaption, which is especially appropriate for real-time control, like tea picking robot.