WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

Abstract

Unmanned Aerial Vehicles (UAVs) have been widely applied for pesticide spraying as they have high efficiency and operational flexibility. However, the pesticide droplet drift caused by wind may decrease the pesticide spraying efficiency and pollute the environment. A precision spraying system based on an airborne meteorological monitoring platform on manned agricultural aircrafts is not adaptable for. So far, there is no better solution for controlling droplet drift outside the target area caused by wind, especially by wind gusts. In this regard, a UAV trajectory adjustment system based on Wireless Sensor Network (WSN) for pesticide drift control was proposed in this research. By collecting data from ground WSN, the UAV utilizes the wind speed and wind direction as inputs to autonomously adjust its trajectory for keeping droplet deposition in the target spraying area. Two optimized algorithms, namely deep reinforcement learning and particle swarm optimization, were applied to generate the newly modified flight route. At the same time, a simplified pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplets. Moreover, an LSTM-based wind speed prediction model and a RNN-based wind direction prediction model were established, so as to address the problem of missing the latest wind data caused by communication latency or a lack of connection with the ground nodes. Finally, experiments were carried out to test the communication latency between UAV and ground WSN, and to evaluate the proposed scheme with embedded Raspberry Pi boards in UAV for feasibility verification. Results show that the WSN-assisted UAV trajectory adjustment system is capable of providing a better performance of on-target droplet deposition for real time pesticide spraying with UAV.

Research on Methods Decreasing Pesticide Waste Based on Plant Protection Unmanned Aerial Vehicles: A Review

“…As the altitude increases, the drifting resistance of the UAV’s downwash airflow gradually increases ( Wu et al, 2019 ). Hu et al (2020) used the DQN and PSO algorithms to adjust the flight trajectory according to the environmental wind conditions so that the drift of the droplets was reduced by 50%, and the amount of deposited droplets was increased. Faiçal et al (2017) used Adaptation to the Environment (AdEn), a system that adjusts the flight according to different wind levels.…”

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

“…Drones are rapidly evolving in the field of agriculture and can perform numerous tasks, such as weed mapping [ 14 ], soil and crop status monitoring [ 15 ], pesticide spraying [ 16 ], diagnosis of insect pests [ 17 ], and artificial pollination [ 18 ].…”

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“…An interesting read for the researchers working on optimal UAV trajectory with multi-parameter optimization is given in [19]. A UAV trajectory is proposed in [20] for WSN-based pesticide controlling. This research focuses on energy depletion through power consumption, harvesting solar power, energy storage limitation, and various QoS parameters.…”