The performance of plant protection drones can depend mainly on the atmospheric environment in plateau areas. In this study, a plant protection unmanned aerial vehicle (UAV) rotor test bench was designed with adjustable rotor speed and real-time monitoring of engine speed, rotor lift, and output torque. The rotor speed was adjusted via the engine throttle, where the throttle line was pulled by the servo crank. The output speed of the engine was achieved to adjust the rudder angle, according to the PWM signal duty cycle. A set of data acquisition software was developed for the rotor test bench, in order to monitor engine speed, rotor lift, and torque parameters, and then display them in real time. The overall structure of the test bench consisted of the rotor, transmission, power, data acquisition, and servo control system, together with the platform. A sensor, control, and data acquisition were built with a data acquisition card as the core, and then the infrared remote control was added to increase the safety of the test. The rotor test bench was equipped with a DLE430 dual-cylinder inline two-stroke engine, with a rotor radius of 1.51 m, an airfoil of NACA 8-H-12, and a blade number of 2. This design fully met the technical indicators of the rotor system in the test state, such as the strength, stiffness, vibration, and accuracy. The blade element momentum was adopted to explain the aerodynamic characteristics of blades. The computational fluid dynamics (CFD) simulation was used to complete the solution. The rotor aerodynamic performance was numerically simulated at the speeds of 800, 1 000, and 1 200 r/min, respectively, within the altitude of 0, 1, 2, 3, and 4 km, respectively. The second-order upwind scheme was used in the numerical simulation, indicating a more accurate performance than the first-order upwind scheme. A systematic investigation was made to explore the effects of blade angle and rotor speed on rotor lift, test bench torque, and power using quadratic rotation orthogonal experiments and response surface method (RSM). The rotor performance tests were conducted, where the lift was taken as an indicator. The viewing performance tests of spread rotor test benches were also carried out, where the torque and power were as indicators. The quadratic regression equations were established for the lift, torque, and power. The relationship was determined between the rotor lift, test bench output torque, as well as the power and blade angle. The rotor speed shared a significant correlation and a good fitting level. The experimental results show that the rotor power decreased significantly with the increase of altitude, whereas, the descent rate increased. The power increased with the increase of speed at the same altitude. Furthermore, the power at an altitude of 2 km decreased by about 26% at a rotor speed of 1 000 r/min, compared with an altitude of 0 km. The optimized rotor speed was 1 116 r/min, the blade angle was 10.44°, the maximum lift was 356.28 N, the torque was 227.35 N·m, the power was 26.54 kW, and the efficiency of the rotor test bench was 85.92% at an altitude of 4 km. Compared with the experiment at an altitude of 134 meters, the lift of the rotor at an altitude of 1.941 km decreased by 22.38%, which was consistent with the decrease of 20.22% in numerical simulation. The driving torque of the rotor decreased by about 24.21%, and the engine power difference was about 3.99%. There was a reasonable range in the error ratio between the experimental and simulation. In addition, the variation trend of the experimental results was consistent with the numerical simulation, indicating a relatively small error. The main reason for the error was the frictional resistance of the power device composed of the rotor and engine in the experimental device during sliding. The findings can provide a strong reference for the high-load plant protection UAV at high altitudes.

To address problems encountered in current potato harvesting machines in hilly and ‎mountainous ‎areas, such as potato damage, poor adaptability, low operational efficiency, and ‎the inability of ‎‎traditional harvesters to meet the requirements in these ‎areas, a new potato ‎harvester equipped with excavation and ‎a multi-stage separation conveyor‎ was developed by using design and ‎simulation programs as an innovative way to identify the best operating factors. SolidWorks ‎Software was used to design an excavation and ‎a multi-stage separation conveyor. ANSYS ‎Workbench ‎‎machine ‎static structure analyzed stress, strain, and deformation. The working process of soil ‎and tuber ‎separation ‎was tested and kinematically analyzed by EDEM-RecurDyn and a 5F01M camera. A field ‎‎experiment was ‎also conducted on the machine under several factors: working speed (W)‎, ‎‎excavation depth (D), ‎vibration intensity level (V), and conveyor inclination angle (N)‎‏.‏‎ ‎The ‎quadratic regression orthogonal rotating combination experiment tested four factors with five ‎levels.‎‏ ‏The results of the ‎non-load ‎experiment showed that the lowest ratio of impurities ‎was at the linear speed level (Q3, ‎S5, ‎O3) for the first and second separation conveyor and the ‎side conveyor, respectively. The results ‎‎of the field experiment showed that the optimal parameters were the working speed of 1.05 m/s, ‎‎the digging depth of 180 mm, and the vibration force Ⅱ ‎inclination angle on the screen surface of ‎‎22 ‎degrees, which gave the highest potato lifting rate of 98.8%, and the bruising ‎rate was 1.37%. The ‎damage rate was 1.43%, superior to national industry ‎standards.‎ With its exceptional ‎performance, the machine can effectively meet and solve the challenges of harvesting requirements, making it a ‎valuable tool for the industry.‎

Intelligent harvesting is often required for object detection under complex field conditions, especially in the process of the real-time monitoring of harvesting quality and accurate grading conveyor. Taking Panax Notoginseng as the research plant, this study aims to propose lightweight object detection using YOLOv5s. The complex field conditions included the large variations in the light intensity, the difficulty in separating the roots from the soil, easy entanglement of roots, variable lifting speed, vibration amplitude, and frequency. The optimal model was also obtained with the high accuracy, and low complexity of a large model suitable for the deployment of mobile terminals. Firstly, a sample dataset was collected from the Panax Notoginseng in the complex field. The influence parameters of transportation and separation were also determined for the complex root-soil system; Secondly, real-time detection was realized under complex field conditions. Slim-neck lightweight neck network was introduced into the lightweight convolution of GSConv. The original SPPF feature fusion module was retained, while the ShuffleNetv2 lightweight feature extraction network was used to improve the original backbone network, which greatly reduced the model complexity with the model accuracy; Finally, the loss function with angular penalty metric (SCYLLA-IoU, SIoU) was used to optimize the bounding box loss function, in order to enhance the detection accuracy and generalization performance of the lightweight improved model. Ablation experiments were carried out to verify three improvement strategies, namely the Slim-neck neck feature extraction network, ShuffleNetv2 backbone feature extraction network, and SIoU bounding box loss function. The experimental results showed that the improved lightweight model (PN-YOLOv5s) had 3.27×10⁶ M parameters, 5.4 G computational complexity, 6.85 MB weight size, and a detection speed of 108 frames per second. The number of parameters and weight size were approximately half of the original YOLOv5s, while the computational complexity was about one-third of the original model, and the detection speed increased by 1.2 times. Additionally, the precision of the improved model reached 93.15%, which was almost the same as the original model. The recall reached 89.46% with an improvement of 0.48 percent points, compared with the original model. The F₁ score reached 91.27% with an improvement of 0.22 percent points. The mean average precision reached 94.20%, only 0.6 percent points lower than the original. Compared with the mainstream SSD, Faster R-CNN, YOLOv4-tiny, YOLOv7-tiny, and YOLOv8s models, the improved lightweight model greatly reduced the complexity of the model, indicating better overall performance, in terms of model accuracy and real-time detection. The best performance was achieved in the actual harvesting, indicating the stronger robustness more suitable for deployment into mobile terminals. The bench tests showed that when the lifting inclination angle and vibration amplitude remained unchanged, the detection performance of the improved model decreased with the increase in lifting speeds and vibration frequencies. On the whole, the target detection of Panax Notoginseng was achieved with a precision of over 90%, an F₁ score of over 86%, and a mean average precision of over 87% under four conditions of conveying and separation operating. There was little difference in the detection speed. But the improved model can fully meet the requirements of real-time detection under actual harvesting conditions. The finding can provide technical support to the subsequent monitoring of harvesting quality and adaptive grading conveyor in the Panax Notoginseng combine harvesters.

Tracked vehicles have demonstrated superior passability in hilly and mountainous terrains. Among them, the control system of path tracking has been focused mainly on wheeled agricultural equipment rather than tracked vehicles, particularly with single-sided braking tracks. In this study, the fuzzy control of adaptive preview tracking was proposed to improve the parallel control and tracking accuracy with the lower turning count of tracked vehicles. The 3B55 tracked transport vehicle was modified as the experimental platform from Jiangsu Zhushui Agricultural Machinery. A preview tracking model was constructed via the kinematic analysis of single-sided brake-tracked vehicles. Efficient parallel control was achieved in the steering and straight-line travel within the same control cycle. A multiple input/output fuzzy controller was designed with the lateral and heading deviation as the inputs, while the motion distance and turning ratio as the outputs. Furthermore, an adaptive line-of-sight solution was introduced with the improved sparrow search algorithm (SSA) to enhance the accuracy of path tracking with less turning count. The chaotic factor and clustering scavengers were also added to the sparrow population, in order to improve the convergence accuracy and susceptibility to local optima. This adaptive front view distance(L_os) solution with the improved SSA was analytically determined as the optimal L_os, considering the lateral deviation and steering path angle constraints of the vehicle's current state. Simulation and field experiments were carried out to evaluate the tracking accuracy and turning count. Simulation results demonstrate the convergence speed was significantly improved during the early iterations of the improved Sparrow Search. Moreover, the superiority of the improved SSA was validated to successfully escape the local optima with the optimal fitness value after 37 iterations. The on-line modelling test was implemented with the L_os of 0.8, 1.5, 2, and 3 m. The lateral deviation increased in the same control cycle when the vehicle was reduced the heading deviation. A longer time was required for the vehicle to complete the on-line, with the increase of the L_os. The accuracy of vehicle tracking decreased gradually, but the number of turning counts was also reduced. An optimal L_os was obtained in the fuzzy control of adaptive preview tracking in real time, according to the current position and steering path angle of the tracked vehicle during path tracking. When tracking multi-angle planned paths, the turning count was 89 times, with an error area of 1.74 m². The field experiments were conducted at the Panax Notoginseng Planting Test Field of the Yunnan University Traditional Chinese Medicinal Materials Mechanization Engineering Research Center. The test field was measured as 40 m in length and 6 m in width. The accuracy of path tracking decreased for the tracked transport vehicle in the field, due to the unevenness of the terrain. However, the trends of tracking accuracy and turning count with the L_os were consistent with the simulation. Both the turning count and path tracking accuracy decreased gradually, as the driving speed increased, especially in the tracking fuzzy control with fixed L_os. When the vehicle tracked at the speeds of 0.14, 0.47, and 0.83 m/s, respectively, the fuzzy control of adaptive preview tracking reduced turning count by 13.59%, 9.87%, and 11.25%, respectively, and the error areas by 19.93%, 48.48%, and 54.59%, respectively. The field and simulation proved that the adaptive predictive tracking fuzzy control for tracked vehicles can be expected to improve the tracking accuracy of the vehicle and the number of steering control times in the field tracking. The tracking trajectory was closer to the planned path, indicating better performance. This study can provide innovative ideas and technical support to the automatic navigation of agricultural machinery in the single-sided brake-tracked vehicle.

Existing discrete element method-based simulation analysis of Panax notoginseng root soil separation still has the challenge to get the accurate and reliable basic parameters, which are necessary for discrete element simulation. In this paper, the P. notoginseng roots suitable for harvesting period were taken as the experimental object. Then using 3D scanning reverse modeling technology and EDEM software to establish the discrete element model of P. notoginseng, based on which, the physical and virtual tests were carried out to calibrate the simulation parameters. First, the basic physical parameters (density, triaxial geometric size, moisture content, shear modulus, and elastic modulus) and contact coefficients (static friction coefficient, rolling friction coefficient, and crash recovery coefficient between P. notoginseng roots and 65Mn steel) were measured by physical tests. Furthermore, treating the contact coefficients of P. notoginseng roots as the influence factor, the steepest uphill test, and four factors combing five levels of rotational virtual simulation are conducted. The measured relative error accumulation angle and simulation accumulation angle are set as the performance indices. The results show that the static friction coefficient, rolling friction coefficient, crash recovery coefficient, and surface energy coefficient of P. notoginseng roots are 0.55, 0.35, 0.16, and 19.5 J/m², respectively. Using calibration results as parameters of the vibration separation simulation test of P. notoginseng soil, the Box-Behnken vibration separation simulation tests were carried out, in which the vibration frequency, inclination angle, and vibration amplitude of separation device as factors, screening rate and damage rate of P. notoginseng soil complex are regarded as indices. The results show that the optimal operating parameters of the separation device are the vibration frequency of 10 Hz, the inclination angle of 5°, and the amplitude of 6 cm. Based on the optimal operation parameters, the discrete element simulation experiment and field experiment of P. notoginseng roots soil separation are also performed to compare the soil three-dimensional trajectory space coordinates of P. notoginseng roots. From the results, three axis coordinate error is less than 15%. This proves that the calibration results are reliable. It can also provide the theoretical basis and technical support for the further study of the P. notoginseng root soil separation platform.