College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China.
Sichuan Key Laboratory of Agricultural Information Engineering, Ya'an 625000, China.
Sensors (Basel). 2021 Jan 18;21(2):638. doi: 10.3390/s21020638.
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.
多旋翼无人飞行器(UAV)在我国农业生产中被广泛应用于植物保护。然而,喷雾液滴常常漂移且分布不均匀,从而影响了其利用效率和对环境的危害。为了解决这个问题,设计、讨论并验证了一种变量喷雾系统。通过计算流体动力学模拟,获得了不同喷雾状态(飞行状态、环境状态、喷嘴状态)下液滴沉积的分布特性。在验证实验中,大多数采样点的风速误差小于 1m/s,沉积比误差小于 10%,表明模拟是可靠的。使用仿真数据集对具有多个参数的支持向量回归和反向传播神经网络进行训练,选择均方根误差为 6.5%的最优回归模型。通过多传感器融合和预测沉积分布特性,可以根据当前的喷雾状态获得变量喷雾系统的 UAV 偏移量和喷嘴流量。农田试验表明,预测与实验之间的沉积体积误差在 30%以内,从而证明了该系统的有效性。本文为改进 UAV 智能喷雾系统提供了参考。