The aerial spraying of pesticides by plant protection unmanned aerial vehicles (UAV) is a process in which the spray droplet deposition on target sites occurs under the influence of the downwash wind field. The downwash wind field is the most important factor affecting droplet deposition distribution characteristics in an aerial spray. To understand the mechanism of the downwash wind field, spray tests were conducted at different flight heights by using a DJI UAV, and the downwash wind field in the three-dimensional direction (X-directional wind, Y-crosswind, and Z-vertical wind) was measured by using a wind speed measurement system for UAV. Combined with the droplet deposition of aerial spray, the distribution characteristics of the downwash wind field and the influence of the downwash wind field on droplet deposition were studied. The results showed that it had obvious differences in the distribution of the downwash wind field for UAV at different flight heights. As the flight height increases, the downwash wind field in X-direction and Z-direction showed a strong to weak trend, while the downwash wind field in Y-direction showed an opposite trend. In addition, it was found that the downwash wind field in Y-direction and Z-direction both have a significant influence on droplet deposition. With the increase of flight height, the change of the downwash wind field led to a gradual decrease in droplet deposition in the effective spray area, and droplets deposited more uniformly. For the DJI T16 plant protection UAV in this test, the optimal flight height was 2.0 m, and the downwash wind field had a better improvement effect on droplet deposition. Therefore, in order to make full use of the downwash wind field of UAV, the appropriate flight height should be selected to improve droplet deposition of liquid pesticide and achieve a better control effect for crop disease and pests when UAV is used for aerial spray operations in the field. This study revealed the influence mechanism of the downwash wind field on droplet deposition of aerial spray, and proposed appropriate operation parameters from the perspective of practical operation. It was expected to provide data support for improving the operation quality of aerial spraying and the formulation of field operation specifications.

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological innovation, and their development drives progress across the entire agricultural sector. This paper reviews the current state of research on fruit- and vegetable-picking robots, focusing on key aspects such as the vision system sensors, target detection, localization, and the design of end-effectors. Commonly used target recognition algorithms, including image segmentation and deep learning-based neural networks, are introduced. The challenges of target recognition and localization in complex environments, such as those caused by branch and leaf obstruction, fruit overlap, and oscillation in natural settings, are analyzed. Additionally, the characteristics of the three main types of end-effectors—clamping, suction, and cutting—are discussed, along with an analysis of the advantages and disadvantages of each design. The limitations of current agricultural picking robots are summarized, taking into account the complexity of operation, research and development costs, as well as the efficiency and speed of picking. Finally, the paper offers a perspective on the future of picking robots, addressing aspects such as environmental adaptability, functional diversity, innovation and technological convergence, as well as policy and farm management.

Design and test of one-rotor orchard horizontal rotary rake based on tine movement trajectory analysis