Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis

Multi-rotor unmanned aerial vehicles (UAVs) for plant protection are widely used in China's agricultural production. However, spray droplets often drift and distribute nonuniformly, thereby harming its utilization and the environment. A variable spray system is designed, discussed, and verified to solve this problem. The distribution characteristics of droplet deposition under different spray states (flight state, environment state, nozzle state) are obtained through computational fluid dynamics simulation. In the verification experiment, the wind velocity error of most sample points is less than 1 m/s, and the deposition ratio error is less than 10%, indicating that the simulation is reliable. A simulation data set is used to train support vector regression and back propagation neural network with multiple parameters. An optimal regression model with the root mean square error of 6.5% is selected. The UAV offset and nozzle flow of the variable spray system can be obtained in accordance with the current spray state by multi-sensor fusion and the predicted deposition distribution characteristics. The farmland experiment shows that the deposition volume error between the prediction and experiment is within 30%, thereby proving the effectiveness of the system. This article provides a reference for the improvement of UAV intelligent spray system.

Automated summary

This article introduces the design and verification process of multi-rotor unmanned aerial vehicles for plant protection widely used in agricultural production in China, discussing how to improve the uniform distribution of spray droplets through simulation and experimental verification methods. The study shows that the variable spray system can accurately obtain the UAV offset and nozzle flow under spray states through multi-sensor fusion and predicted deposition distribution characteristics, thereby enhancing the efficiency and precision of the system.