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.

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.

The environmental temperature and humidity are crucial factors for the normal growth and development of arbor tree cuttings by altering their hormone levels and influencing their physiological activities. Developing a temperature and humidity environmental model for arbor tree cuttings serves as a key technique to improve the adjustment performance of environmental parameters in facility agriculture systems and enhance the rooting rate of cuttings. This paper provides a comprehensive summary of current research on the inherent characteristics of cuttings and the factors influencing environmental temperature and humidity. It explores the mechanisms of interaction between the inherent characteristics of cuttings and the factors influencing environmental temperature and humidity. This paper investigates the interactive relationships among the factors affecting environmental temperature and humidity. It analyzes methods to improve the efficiency of constructing temperature and humidity environmental models for arbor tree cuttings. To enhance the transferability of the environmental model, the necessary physiological activities under the influence of plant hormones are generalized as common physiological traits in the growth and development of cuttings. In addition, this paper explores the factors influencing the air and substrate temperature and the humidity in facility agriculture systems as well as two types of facilities for controlling environmental temperature and humidity. Furthermore, it reviews the research progress in environmental models from both mechanistic and data-driven perspectives. This paper provides a comparative analysis of the characteristics associated with these two model categories. Building upon this, the paper summarizes and discusses methods employed in constructing temperature and humidity environmental models for arbor tree cuttings. In addition, it also anticipates the application of deep learning techniques in the construction of temperature and humidity environmental models for arbor cuttings, including utilizing machine vision technology to monitor their growth status. Finally, it proposes suggestions for building physiological models of fruit tree-like arbor cuttings at different growth stages. To enhance the transferability of environmental models, the integration of physiological models of cuttings, environmental models, and control system performance are suggested to create an environmental identification model. This paper aims to achieve control of the common physiological activities of cuttings.

This study addresses inefficiencies in manual apple harvesting and high damage rates in mechanical methods by developing an airflow-cushioned conveyor to minimize postharvest losses. Analyzing apple dynamics in pipelines and collision mechanics identified three key damage factors: fruit size, conveyor linear velocity, and airflow speed. A Box–Behnken-designed response surface model linked these parameters to damage area and collision force. The results showed optimal settings for small (grade III: 11 m/min, 18.2 m/s; 34.24 mm², 8.7 N), medium (grade II: 11 m/min, 19.01 m/s; 48.62 mm², 9.52 N), and large apples (grade I: 11 m/min, 19.3 m/s; 67.01 mm², 10.34 N). Under the optimal parameters, the damage rate for grade I apples was only 12%, while grade II apples had a 0% damage rate, fully meeting the grade II standards. This damage rate was significantly lower than the over 50% damage rate observed in vibration harvesting. Additionally, the harvesting speed using the optimized device increased by more than twice compared to traditional manual harvesting. The findings provide an engineering case for balancing fruit quality maintenance and harvesting speed improvement.