The downwash airflow generated by the rotors can enhance the penetration of pesticide droplets, allowing them to penetrate deeper into the canopy and more comprehensively control pests. Downwash air will produce a “wind vortex” in plant canopy, and the parameters of a “wind vortex” represent the effect of pesticide deposition to a certain extent. To obtain the corresponding relationship between wind vortices and flight parameters and control the effect of pesticide spraying, this paper carried out unmanned aerial vehicle (UAV) flight experiments in the field. Wind vortices were generated in rice canopy by downwash airflow, and the parameters of wind vortices were obtained by identifying wind-vortex images using the inter-frame difference method. The wind-vortex parameter control model was established, which can calculate the altitude and speed of the UAV when applying pesticide according to the target wind-vortex parameter. The deviations in the altitude were determined to be 0.67 and 0.43 m, and the deviations in the speed were 0.29 and 0.35 m/s during downwind and headwind UAV operations, respectively. The model relation functions were established, and their accuracies were found to be 97.1%, 92.3%, 69%, and 58% (downwind), and 97%, 78.4%, 62%, and 57% (upwind), respectively, indicating that downwind UAV operation leads to a clear relation between the wind-vortex parameters and the UAV-flight parameters. The model establishes the corresponding relationship between the wind-vortex parameters and flight parameters, which provides a theoretical basis for studying the precise application control method of UAV.

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.

Plant protection drones are commonly encountered in agricultural fields. Their downwash airflow can agitate flexible crops (e.g., rice and wheat) or even cause wind-induced losses. To predict the wind-induced responses of rice under wind fields, herein, a wind-induced rice response model (RWRM) was proposed using the finite element method. With the RWRM, the rice displacement and critical wind speed (CWS) were calculated at different wind speeds, considering the morphological and mechanical properties of rice, and the accuracy was experimentally verified and compared to that of an existing model. The results indicated that the mean paired difference and mean error in rice displacement amplitude prediction under 2~5 m/s wind speeds were 13.48 mm and 42.46 mm, respectively, and the predicted and measured values were highly correlated at the 1% significance level. Moreover, the CWS values for four rice species could be calculated with the model with an average of 3.57 m/s, and the difference between the simulated and theoretical values was 0.368. The strength of the wind-induced rice responses was primarily correlated with the mechanical properties, and to a lesser extent the morphology. The rice yield has a negative correlation with rice responses. Within a certain range, a bigger displacement and lower CWS could result in a higher rice yield. The RWRM achieved favorable modeling accuracy for the wind-induced responses of rice and could provide a simulation reference for balancing the wind-induced loss and rice yield.

At present, all the flight controllers of agricultural UAVs cannot accurately and quickly control the influencing factors of the UAV coupled rotor airflow backward tilt angle during the application process. To solve the above problem, a Rotor Airflow Backward Tilt Angle (RABTA) controller is established in this paper. The RABTA controller integrates advanced sensor technology with a novel algorithmic approach, utilizing real-time data acquisition and state–space analysis to dynamically adjust the UAV’s rotor airflow, ensuring precise control of the backward tilt angle. The control effect of the traditional flight controller and RABTA controller in the process of pesticide application and the corresponding operation effect are compared and analyzed. The comparison results show that the RABTA controller reduces the control error to less than 1 degree, achieving a 48.3% improvement in the uniformity of the distribution of pesticides droplets across the crop canopy, which means that the UAV field application effect is implemented and the innovation of the UAV field application control mode is realized.