Unmanned aerial vehicles for plant protection and precision agriculture: a study on low-altitude route planning method of unmanned aerial vehicles

In recent years, Unmanned Aerial Vehicles (UAVs) are widely utilized in precision agriculture, such as crops plantations and plant protection. Recently, UAVs have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Due to limited intelligence, these UAVs can only operate at high altitudes, leading to the use of expensive and heavy sensors for obtaining important health information of the plants. This paper presents an in-depth study and analysis of low -altitude flight path planning for plant protection UAVs using an improved deep neural network. The simulation results show that the average conflict probability based on the predicted trajectory is lower compared with the average conflict probability obtained using the actual trajectory. When using the predicted trajectory, its RMSE score is 0.893. There may be errors in the trajectory information acquired by the ADS-B receiver, but the errors are usually around 100 m and have little impact on the trajectory prediction task. If the transmission process of the error is further considered, the RMSE score can be improved from 0.893 to about 1 at most, which is still within the error range allowed by the conflict detection method. This indicates that the predicted number of conflicts per second differs from the actual number of conflicts by less than one vehicle (0.893 < 1). Therefore, the conflict prediction method based on predicted trajectories designed in this paper can detect the presence of conflicts at future moments.