Design and research of residual film pollution monitoring system based on UAV

Abstract

To evaluate residual film recovery quality and rapidly monitor residual film pollution in agricultural fields, a residual film pollution monitoring system based on UAV, ground station, and cloud server was proposed in this research. It integrated a random sampling point generation (RSPG) algorithm and a key-lock pairing (KLP) algorithm for fast planning of flight paths, and used the U-Net model to rapidly segment residual films from images and calculate residual film coverage. The results showed that the average deviation between the actual distance and the ideal distance of the randomly generated sampling points using the RSPG algorithm was 0.0733 m, and the average deviation of the azimuth angles was 0.2°. The KLP algorithm significantly reduced path planning time, while maintaining a high shortest path rate and reducing memory consumption. The U-Net model achieved an optimal mean intersection over union (MIOU) of 85.63 % on the test set. The simulation and field verification of the system found that automatic UAV path planning and rapid residual film image segmentation were achieved. The coefficient of determination (R2) between the predicted values and the manually labelled values was 0.966, and the root-mean-square error (RMSE) was 0.1173 %. The average relative error between the predicted residual film coverage and the manually labelled coverage was 11.55 %, and the average detection time of the monitoring system was 422.6 s. Therefore, the UAV-based residual film pollution monitoring system can realize integrated automatic sampling and rapid residual film pollution assessment. This study provides technical support for accurate assessment and control of residual film pollution.