As important factors in discrete elements, the physical parameters of watermelon seeds play a pivotal role in discrete element method. To obtain the discrete element parameters of watermelon seeds and improve the accuracy of the discrete element model, through a combination of actual and simulation tests, this research has calibrated the seeds’ physical and contact parameters with the seed metering device. Employing the Plackett-Burman experiment, this study has identified three critical factors affecting the stacking angle: the static and rolling friction coefficients between seeds, and the collision recovery coefficient between seeds and plexiglass. Using the steepest-climbing design and Box-Behnken response surface analysis, this research has optimized these factors to values of 0.716, 0.051, and 0.787, achieving a calibration error of just 2.60%. Verification with an air suction precision seed metering device confirmed the parameters’ accuracy, with relative errors below 7.65%. The discrepancy between the simulation and actual test results, as measured by the qualified index error, is successfully reduced to below 4.38%. This study thus establishes a solid foundation for the structural optimization of air suction precision watermelon seed metering devices.

The existing discrete element model of wheat plants lacks the glume, which hinders the simulation of the entire threshing process. To address this issue, this paper takes wheat at the harvest stage as the research object and constructs a complete discrete element model of wheat plants with glumes based on the Hertz-Mindlin with bonding model in the EDEM simulation software. The parameter calibration of wheat glumes discrete element model is studied through collision bounce experiments, slope experiments, and accumulation experiments. The results show that the coefficient of restitution, coefficient of static friction, and coefficient of rolling friction between glume and steel are 0.488, 0.625, and 0.048, respectively, and the coefficient of restitution, coefficient of static friction, and coefficient of rolling friction between glume and glume are 0.232, 0.966, and 0.059, respectively. The relative errors between the simulation results and the measured values are less than 5%, and the calibration parameters are effective. Based on the structural parameters of the self-developed experiment-bed of tangential axial-flow grain threshing device, a three-dimensional model of the wheat threshing device is established to simulate the whole threshing process of the complete wheat plant, and the bench-scale experiments are carried out with the non-threshing rate as the performance index. The results indicate that the model can completely simulate the separation process of glume and grain and the movement law of different grains, and the relative error of non-threshing rate between the simulation experiments and bench-scale experiments is 4.36%. This further demonstrates that the proposed model can provide a reference for the wheat threshing process research and device performance optimization design.

To realize the visual navigation of agricultural robots in the complex environment of orchards, this study proposed a method for fruit tree recognition and navigation based on YOLOv5. The YOLOv5s model was selected and trained to identify the trunks of the left and right rows of fruit trees; the quadratic curve was fitted to the bottom center of the fruit tree recognition box, and the identified fruit trees were divided into left and right columns by using the extreme value point of the quadratic curve to obtain the left and right rows of fruit trees; the straight-line equation of the left and right fruit tree rows was further solved, the median line of the two straight lines was taken as the expected navigation path of the robot, and the path tracing navigation experiment was carried out by using the improved LQR control algorithm. The experimental results show that under the guidance of the machine vision system and guided by the improved LQR control algorithm, the lateral error and heading error can converge quickly to the desired navigation path in the four initial states of [0 m, −0.34 rad], [0.10 m, 0.34 rad], [0.15 m, 0 rad] and [0.20 m, −0.34 rad]. When the initial speed was 0.5 m/s, the average lateral error was 0.059 m and the average heading error was 0.2787 rad for the navigation trials in the four different initial states. Its average driving was 5.3 m into the steady state, the average value of steady state lateral error was 0.0102 m, the average value of steady state heading error was 0.0253 rad, and the average relative error of the robot driving along the desired navigation path was 4.6%. The results indicate that the navigation algorithm proposed in this study has good robustness, meets the operational requirements of robot autonomous navigation in orchard environment, and improves the reliability of robot driving in orchard.

To improve the quality and efficiency of peanut half-feed harvesting in clay hilly areas in South China and address problems such as excessive soil clumps, broken branches, and seedlings in pods, difficulty in cleaning impurities, and the need for manual picking owing to the high soil viscosity and easy hardening, a new half-feed peanut cleaning picker suitable for southern clay hilly areas, including its overall structure and transmission system, was designed. The picker can perform the operations of soil removal, clamping and conveying of seedlings, and orderly pod picking and pod gathering. The structural design of key components and the analysis and determination of key parameters were carried out. By adopting a crank rocker mechanism, a soil removal device was designed. A single-side chain clamping conveying device, which consists of a clamping chain, a pretightening spring, and a guide rail, was designed. A phase tangent configuration of opposite rollers was used to design a pod picking device. Thus, the functions of the half-feed peanut picker, such as cleaning and removing soil, smooth and reliable clamping and conveying, and flexible pod picking, were realized. The field test revealed that when the picking rate was greater than 97%, the soil removal pods drop rate was less than 10%, and the soil removal rate was greater than 50%. The performance indicators meet the design requirements. This study provides a technical reference for the research and development of clean picking technology for upright peanuts in the clay hilly areas of southern China.