In orchard plant protection application, an anti-drift strategy can effectively reduce drift in the non-target area, reduce spray drift in the environment, and avoid spray leakage and overspraying. To clarify the future development direction of orchard plant protection mechanization technology, this review introduces the development status of an anti-drift spray nozzle and the impact of different types of spray nozzles on the potential of drift, and then, it analyzes the research progress on air-assisted spraying, recycling spraying, profiling spraying, target variable spraying technologies, and plant protection UAVs. It also provides a general analysis of the above spraying technologies on the amount of drift and the impact of pesticide deposition. Finally, combined with the characteristics of orchard plant protection, the paper presents the research and development of anti-drift nozzles, pesticide adjuvant, air-assisted spraying technology, electrostatic, recycling spraying technology, profiling and target variable spraying technology, and plant protection UAVs. The review provides a reference for the development of an anti-drift strategy for orchard plant protection production.

In order to improve the efficiency of agricultural machinery operations and reduce production costs, this article proposes a path planning algorithm based on the improved A* algorithm (IA*) and a tracking controller based on fuzzy sliding mode variable structure control (F-SMC) to meet the operation requirements of tracked agricultural machinery. Firstly, we introduce a heuristic function with variable weights, a penalty, and a fifth-order Bezier curve to make the generated path smoother. On this basis, the ant colony algorithm is introduced to further optimize the obtained path. Subsequently, based on fuzzy control theory and sliding mode variable structure control theory, we established a kinematic model for tracked agricultural machinery as the control object, designed a fuzzy sliding mode approaching law, and preprocessed it to reduce the time required for sliding mode control to reach the chosen stage. The simulation experiment of path planning shows that compared with A*, the average reduction rate of the path length for IA* is 5.51%, and the average reduction rate of the number of turning points is 39.01%. The path tracking simulation experiment shows that when the driving speed is set to 0.2 m/s, the adjustment time of the F-SMC controller is reduced by 0.99 s and 1.42 s compared to the FUZZY controller and PID controller, respectively. The variance analysis of the adjustment angle shows that the minimum variance of the F-SMC controller is 0.086, and the error converges to 0, proving that the vehicle trajectory is smoother and ultimately achieves path tracking. The field test results indicate that the path generated by the IA* algorithm can be tracked by the F-SMC controller in the actual environment. Compared to the A* algorithm and FUZZY controller, the path tracking time reduction rate of IA* and F-SMC is 29.34%, and the fuel consumption rate is reduced by 2.75%. This study is aimed at providing a feasible approach for improving the efficiency of tracked agricultural machinery operations, reducing emissions and operating costs.

As a result of the continuous global warming in recent years, the average annual number of rain days in China has been on the decline, while the number of rainstorm days has gradually increased. These conditions make it extremely easy to form a waterlogging environment, which has an adverse impact on plant growth and development. In many apple-producing areas in China, apples are subject to severe flooding during planting. In this study, two-year-old apple rootstock M9T337 was used to explore the effects of interval water stress on the morphological and physiological parameters of apple leaves. The purpose was to determine the plant’s adaptability to waterlogged environments and provide theoretical reference for management and maintenance after waterlogging. The results showed that the effect on flooded (T2) on apple stock was greater than that of waterlogged (T1), Short-term (7 d) waterlogging (T1) did not affect the growth of seedlings but was conducive to the accumulation of dry matter. Furthermore, the initial stress was be imprinted on the plants, which could directly affect their response to later stress. The results of principal component analysis (PCA) revealed that PC1, PC2, and PC3 explained 26.92%, 17.46%, and 13.03% of the physiological changes under water stress, respectively. By calculating the weight of each indicator, we concluded that high-frequency resistance r, relative chlorophyll content (SPAD) and maximum photochemical efficiency Fv/Fm are important parameters for apple rootstocks affected by water stress.

In order to optimize the operating path of orchard mowers and improve their efficiency, we propose an MI-DBO (multi-strategy improved dung beetle optimization algorithm) to solve the problem of full-coverage path planning for mowers in standardized quadrilateral orchard environments. First, we analyzed the operation scenario of lawn mowers in standardized orchards, transformed the full-coverage path planning problem into a TSP (traveling salesman problem), and mathematically modeled the U-turn and T-turn strategies based on the characteristics of lawn mowers in orchards. Furthermore, in order to overcome the issue of uneven distribution of individual positions in the DBO (dung beetle optimization) algorithm and the tendency to fall into local optimal solutions, we incorporated Bernoulli mapping and the convex lens reverse-learning strategy in the initialization stage of DBO to ensure a uniform distribution of the initial population. During the algorithm iteration stage, we incorporated the Levy flight strategy into the position update formulas of breeding beetles, foraging beetles, and stealing beetles in the DBO algorithm, allowing them to escape from local optimal solutions. Simulation experiments show that for 18 types of orchards with different parameters, MI-DBO can find the mowing machine’s operation paths. Compared with other common swarm intelligence algorithms, MI-DBO has the shortest average path length of 456.36 m and can ensure faster optimization efficiency. Field experiments indicate that the algorithm-optimized paths do not effectively reduce the mowing machine’s missed mowing rate, but the overall missed mowing rate is controlled below 0.8%, allowing for the completion of mowing operations effectively. Compared with other algorithms, MI-DBO has the least time and fuel consumption for operations. Compared to the row-by-row operation method, using paths generated by MI-DBO reduces the operation time by an average of 1193.67 s and the fuel consumption rate by an average of 9.99%. Compared to paths generated by DBO, the operation time is reduced by an average of 314.33 s and the fuel consumption rate by an average of 2.79%.

Path planning and tracking is the most basic technology for mowing robots, among which the performance of algorithms has a great impact on their intelligence and efficiency. Based on the research of relevant references on mower robots, it mainly focuses on complete coverage path planning, path tracking control, and obstacle avoidance path planning. In complete coverage path planning, three methods were introduced, including simple complete coverage planning, optimal complete coverage planning, and hybrid complete coverage planning. In the path tracking control section, the control methods are divided into three types based on whether the control method depends on the robot model and the type of model, namely model free control method, kinematic model-based control method, and dynamic model-based control method. In obstacle avoidance path planning, we introduce the environment detection device and obstacle avoidance planning algorithm. Then the relevant research papers are analyzed in classification, comparing the research and validation methods adopted by the researchers in the form of charts. Finally, we pointed out the limitations of path planning technology in the application of mower robots. Meanwhile, future development trends are predicted.

To improve the crashworthiness and energy absorption of thin-walled tube structures, a bionic thin-walled tube was designed based on the structural characteristics of antler osteon and the principle of structural bionics and had the same inner and outer diameters and the same gradient thickness as antler osteon. A nonlinear finite element method is used to simulate the crashworthiness of a thin-walled tube with equal gradient thickness variation (EGTTS) under axial and oblique loads. The crashworthiness of EGTTS-7 (Egtts with 7 layers) was evaluated using the complex proportional assessment(COPRAS). A multi-objective particle swarm optimization (MOPSO) algorithm was used to optimize the EGTTS-7 and the Pareto boundary was used to obtain the optimal structure parameters of the EGTTS-7 by using the loading angles of 0°, 10°, 20°, and 30°. It is found that the crashworthiness of the EGTTS is best when the axial load weight factor of the case is large. Compared with EGTTS and circular tubes(CT), F max can be reduced by up to 50.1% and EA can be increased by up to 22.7%.