Agricultural unmanned aerial vehicles (UAVs), increasingly integral to crop protection through spraying operations, are significantly influenced by their downwash fields, which in turn affect the distribution of spray droplets. The key parameters impacting spray deposition patterns are the velocity of the downwash airflow and its spatial extent. Understanding the interplay of these parameters can enhance the efficacy of UAV applications in agriculture. Previous research has predominantly focused on downwash airflow velocity, often neglecting the spatial scope of the downwash. This paper presents an applied foundational study grounded in the compressible Reynolds-averaged Navier–Stokes (RANS) equations. Leveraging a dependable $k$-$\epsilon$ turbulence model and dynamic mesh technology, it develops an effective three-dimensional computational fluid dynamics (CFD) approach to analyze the downwash field’s distribution characteristics during UAV hover. To validate the CFD method, a visualization test was conducted using EPS (expanded polystyrene foam) balls dispersed in the airspace beneath the UAV, illustrating the airflow’s spatial distribution. Additionally, a parameter η was introduced to quantify changes in the wind field’s range, enabling the mapping of the cross-sectional area of the downwash airflow at various velocities within the UAV’s airspace. The study reveals that the downwash field’s overall shape evolves from a “four-point type” to a “square-like” and then to an “ellipse-like” configuration. Lower downwash airflow velocities exhibit a more rapid expansion of the wind field area. High-velocity downwash areas are concentrated beneath each rotor, while lower-velocity zones coalesce under each rotor and extend downward, forming a continuous expanse. Within the UAV’s downwash area, the deposition of droplets is more pronounced. At a given nozzle position, an increase in downwash airflow velocity correlates with greater droplet deposition within the downwash field. This research bridges a gap in downwash field studies, offering a solid theoretical foundation for the development of future UAV downwash field models.

In the context of agricultural unmanned helicopters, the complex wind disturbances over crop fields and structural perturbations due to variations in pesticide container weights present substantial challenges to flight safety. To address these issues, this paper proposes an innovative fuzzy extended state observer-based sliding mode control (FESO-SMC) methodology aimed at enhancing the aircraft’s resilience against such disturbances. Initially, this study adopts a state expansion strategy to integrate both wind and structural disturbances into a comprehensive disturbance model applicable to the agricultural unmanned helicopter. Following this, a sliding mode control law is formulated with consideration for unknown total disturbances, employing specific sliding mode functions alongside exponential reaching laws. An extended state observer is simultaneously implemented within the sliding mode control framework to estimate and mitigate these disturbances, thereby augmenting the disturbance rejection capabilities of the flight control system. Additionally, the integration of fuzzy logic facilitates adaptive parameter adjustment for the extended state observer, leading to more accurate disturbance estimation. Consequently, a trajectory tracking control system tailored specifically for the agricultural unmanned helicopter has been developed, and its performance was evaluated through simulation experiments. The results indicate that, under certain disturbances, the attitude control error of the FESO-SMC controller is reduced to one-fifth that of traditional sliding mode controllers, while position control accuracy is enhanced more than twofold, thus demonstrating that the proposed FESO-SMC controller not only exhibits superior anti-disturbance capability and robustness but also achieves higher tracking accuracy compared to conventional sliding mode controller.

The strong airflow beneath a rotary drone generates a wind vortex within the rice canopy; precise control of the wind vortex distance and wind vortex area can improve pesticide utilization efficiency. This paper calculates the flight parameter curve based on the wind vortex target from the wind vortex target parameter control model of the four-rotor plant protection drone, designs a flight control system using a Cube Orange flight controller and a Jetson AGX Xavier onboard computer, and implements flight parameter control using both PID control and fuzzy control algorithms. Experimental results indicate that when using PID control and fuzzy control, the average deviation values of UAV flight altitude and speed are 0.08 m, 0.08 m/s, 0.06 m, and 0.08 m/s, respectively. When using PID control, the average distance and area errors of the target downwind and upwind are 0.17 m and 0.37 m² and 0.20 m and 0.46 m², respectively. The corresponding values for fuzzy control are 0.12 m, 0.38 m², 0.09 m, and 0.31 m². In the twelve voyage experiments, the target parameter variance using fuzzy control was relatively smaller for eight voyages compared to PID control, which had a smaller variance for four voyages. On the whole, the effect of fuzzy control is superior. The wind vortex control method proposed in this paper can effectively enable precise pesticide spraying by drones. This has significant implications for reducing agricultural production costs and safeguarding the natural environment.