Current developments in agricultural aviation technology have gradually increased the requirements for the endurance of agricultural unmanned aerial vehicles (UAVs). It is significant to establish an endurance evaluation model for different types of UAVs and rationalize the battery and operating load parameters on this basis, which play an important role in improving the operational efficiency of the whole UAV. Taking the quad-rotor UAV as the research object, firstly, the structural composition of the whole UAV is studied in this paper, and then the main influencing parameters of the UAV endurance evaluation model are determined, combined with the mass distribution data of specific models. Based on the above, the hovering output power of the UAV is modularly divided, and a fast measurement method is innovatively proposed to sample and fit the power data of each module. Finally, in combination with the variation law of the lithium battery discharge time, a rapid evaluation method of the endurance time of agricultural UAVs is derived and validated by the hovering test data of the agri-drone with a standard operating load of 5 kg. The results show that the mass distribution system of the UAV has the highest percentage of operational load mass (40.45% under the standard configuration); the power distribution system has the highest percentage of motor pull curve output power (91.12% under the standard configuration); the maximum error of the surface verification point in the validation experiment is 2.73%, and the model is of relatively high accuracy. The modeling idea and the supporting module power rapid measurement method can quickly model and evaluate the endurance of common agricultural drones, guide reasonable matching between the battery and operating load, and are of great significance in improving UAVs’ endurance and operational efficiency.

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.

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.

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.

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.