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.

To address the issue of reduced yield in the second season caused by damaged stubbles resulting from being compressed during the harvesting process of the first season’s ratoon rice, a device for rectifying the compressed stubbles was designed. Utilizing the DEM-MBD coupling simulation method, a simulation analysis was conducted to determine the range of key parameters and verify the feasibility of the solution. Using rotational speed, forward speed, and stubble entry angle as experimental factors and stubble rectification rate and second-season yield as evaluation metrics, a three-factor, three-level Box–Behnken response surface field trial was conducted. The theoretically optimal working parameter combination was found to be a forward speed of 1.4 m/s, device rotational speed of 75 rpm, and stubble entry angle of 39°. Under these conditions, three parallel experiments were performed, resulting in a rectification rate of 90.35% in the mechanically harvested and compressed area and a second-season yield of 2202.64 ± 35 kg/hm². The deviation from the numerical simulation results of parameter optimization was less than 5%. These findings suggest that the designed stubble rectification device for ratoon rice can meet the requirements of stubble rectification during the first-season harvest of ratoon rice. Furthermore, it provides valuable insights for reducing harvest losses in the first season and further improving the level of mechanized harvesting for ratoon rice.

The heading stage of rice is a critical period for disease control, such as for panicle blast. The rapid and accurate monitoring of rice growth is of great significance for plant protection operations in large areas for mobilizing resources. For this paper, the canopy multispectral information acquired continuously by an unmanned aerial vehicle (UAV) was used to obtain the heading rate by inversion. The results indicated that the multi-vegetation index inversion model is more accurate than the single-band and single-vegetation index inversion models. Compared with traditional inversion algorithms such as neural network (NN) and support vector regression (SVR), the adaptive boosting algorithm based on ensemble learning has a higher inversion accuracy, with a correlation coefficient (R²) of 0.94 and root mean square error (RMSE) of 0.12 for the model. The study suggests that a more effective inversion model of UAV multispectral remote sensing and heading rate can be built using the AdaBoost algorithm based on the multi-vegetation index, which provides a crop growth information acquisition and processing method for determining the timing of rice tassel control.

Tractor traction power consumption is one of the main causes of energy consumption in agricultural production. Scientific and accurate control of tractor traction power consumption can obviously save energy and reduce consumption. In view of the backward load-testing technology and low measurement accuracy in field work, this study designed an array test equipment, which formed a measurement matrix based on a six-dimensional force sensor to accurately measure tractor hauled load, which could provide a reference signal for intelligent operation. In this paper, the static calibration test was carried out on the six-dimension force sensor, and the linearity, sensitivity, and zero drift were analyzed. The static characteristics of the test unit meet the measurement requirements. A static decoupling model was established. The decoupling errors of each channel were stable at 0.02%FS, 0.02%FS, 0.8%FS, 0.36%FS, 0.018%FS, and 0.06%FS, respectively. Finally, the whole hanging test of the measuring equipment was carried out—the error was 1.24%, −1.2% respectively—to verify the accuracy of the measurement of the sensor device under different working conditions.

Current status of orchard mechanization technologies and key development priorities in the 15th Five-Year Plan period