Objective The construction method of the discrete element model and the setting of simulation parameters in the strip- and blade-shaped sugarcane leaf are unclear. The simulation model’s accuracy greatly influences the dynamic response characteristics between particles, and it is necessary to improve the accuracy of simulation parameters through parameter calibration. Method The discrete element parameters are optimized and calibrated based on the response surface methodology (RSM) with sugarcane leaf physical angle of repose as the response value. Firstly, the basic physical parameters and angle of repose of sugarcane leaves were measured by physical tests, and the simulation model of sugarcane leaf was established by the multi-sphere polymerization model and XML method. The effects of the sugarcane leaf model filled with different radii particles on the simulation angle of repose and simulation efficiency were analyzed to find the optimal filling particle size of the sugarcane leaf model. Then, a Plackett-Burman test was used to select the parameters that significantly influence the simulation angle of repose. Furthermore, the optimal value ranges of the three significant parameters were determined by a steepest ascent search test, and the second-order regression equation between the significant parameters and angle of repose was established based on the Box-Behnken test, the optimal combination of parameters was obtained with the physical angle of repose of 21.15° as the optimal target value. Finally, a gas-solid coupling simulation test was conducted with the trash content as the test index and compared with the field test. Result The optimal filling particle size of the sugarcane leaf simulation model was 2 mm. The optimal combination of significant parameters was as follows: the static and rolling friction coefficients between sugarcane leaves were 0.21 and 0.05, respectively, and the static friction coefficient between sugarcane leaves and steel was 0.30. There was no significant difference between the simulation value and the test value of trash content, and the maximum relative error between them was 8%, which further showed that the parameter calibration of the sugarcane leaf model was reliable. Conclusions The results showed that the modeling method and parameter calibration of the sugarcane leaf model was accurate and reliable and could be used for subsequent gas-solid coupling simulation research, as well as providing a reference for the calibration of the discrete element parameters of the strip-and blade-shape materials.

Extracting the navigation line of crop seedlings is significant for achieving autonomous visual navigation of smart agricultural machinery. Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. First, image preprocessing is performed to enhance the image quality and extract distinct crop regions. Redundant pixels can be eliminated by opening operation and eight-connected component filtering. Then, optimal region detection is applied to identify the fitting area. The optimal pixels of plantation rows are selected by cluster-centerline distance comparison and sigmoid thresholding. Ultimately, the navigation line is extracted by linear fitting, representing the autonomous vehicle’s optimal path. An assessment was conducted on a sugarcane dataset. Meanwhile, the generalization capacity of the proposed algorithm has been further verified on corn and rice datasets. Experimental results showed that for seedlings at different growth stages and diverse field environments, the mean error angle (MEA) ranges from 0.844° to 2.96°, the root mean square error (RMSE) ranges from 1.249° to 4.65°, and the mean relative error (MRE) ranges from 1.008% to 3.47%. The proposed algorithm exhibits high accuracy, robustness, and generalization. This study breaks through the shortcomings of traditional visual navigation line extraction, offering a theoretical foundation for classical image-processing-based visual navigation.

The cleaning system is a critical component of the sugarcane chopper harvester, facing challenges such as high impurity rate, elevated power consumption, and an inadequate understanding of the cleaning mechanism. This study aims to simulate the process of removing extraneous matter (represented by sugarcane leaves) from the cleaning system by employing a coupling approach of computational fluid dynamics (CFD) and the discrete element method (DEM) to determine the speed of the extractor fan. Initially, a CFD model was established to analyze the airflow field within the extractor, and its accuracy was verified on a test bench for the cleaning system. Subsequently, a DEM model was developed for sugarcane billets and leaves, which was then integrated with the CFD model to form a gas–solid coupling model. The efficacy of this integrated model was confirmed through experimental measurements of impurity rate. Furthermore, a ternary quadratic regression orthogonal combination design was utilized in the gas–solid coupling simulation to assess the impacts of feed rate, leaf–stalk ratio, and extractor fan speed on impurity rate. Finally, the extractor fan speeds were obtained for various feed rates and leaf–stalk ratios under impurity rates of 5%, 6%, 7%, and 8%. This research can guide in controlling the extractor fan speed during sugarcane chopper harvester field operations and can serve as a foundation for extractor fan design.

Given the problems of the high trash content and loss rate for mechanized sugarcane harvesting, taking the HN4GDL-194 sugarcane chopper harvester extractor developed by South China Agricultural University as the research object, three types of extractor negative pressure structures were designed and internal flow field simulation analysis was conducted. Simulation results showed that the aerodynamic performance of the flow field in the negative pressure area of the extractor negative pressure structure two is the best and the wind velocity and negative pressure are the largest. The measurement results of wind velocity, wind pressure, and flow showed that the changing trend in the actual value of wind velocity and wind pressure is basically consistent with the simulation value. The relative error between the actual flow of the air outlet and the simulation value is less than 10% under different speeds, indicating that the simulation has high accuracy. Field tests of the original extractor and the optimal extractor were conducted. The test results for the trash content showed that when the feeding rate was 1.5 kg/s, there was no significant difference in the trash content between the optimal extractor and the original extractor under various fan speeds. When the feeding rate increased to 7.5 kg/s and the fan speed was low (950 r/min) and medium (1100 r/min), the trash content of the optimal extractor was significantly lower than that of the original extractor, decreasing 2.5% and 1.63%, respectively. The loss rate test results showed that when the fan speed is low and high (1250 r/min), there is no significant difference between the loss rate of the optimal extractor and the original extractor. When the fan speed was 1100 r/min and the feeding rate was 1.5, 4.5, and 7.5 kg/s, compared with the original extractor, the loss rate of the optimal extractor decreased by 0.53%, 0.21%, and 0.19%, respectively.