Evaluation of UAV spraying quality based on 1D-CNN model and wireless multi-sensors system

Abstract

The droplet deposition is a key index to evaluate the quality of unmanned aerial vehicle (UAV) spraying. The detection of the droplet deposition is time-consuming and costly, therefore, it is difficult to achieve large-scale and rapid acquisition in the field. To solve the above problems, a droplet deposition acquisition system (DDAS) was developed. It was composed of the multiple sensors, processing units, remote server database and Android-based software. A droplet deposition prediction model based on field experimental data was established by using a one-dimensional convolutional neural network (1D-CNN) algorithm, and the effects of different inputs on the prediction ability of the model were analyzed. The results showed that adding temperature and humidity data to the inputs can achieve higher prediction accuracy than only using UAV spraying operation parameters and wind speed data as the inputs to the model. In addition, the prediction accuracy of the 1D-CNN model was the highest when compared with other models such as back propagation neural network, multiple correlation vector machine, and multiple linear regression. The 1D-CNN model was embedded into the DDAS, and the evaluation experiments were carried out in the field. The correlation analysis was conducted between two datasets of the droplet deposition obtained by the DDAS and water sensitive paper (WSP), respectively. The R² was 0.924, and the RMSE was 0.026 μL/cm². It is proved that the droplet deposition values obtained by the DDAS and WSP have high consistency, and the DDAS developed can provide an auxiliary solution for the intelligent evaluation of UAV spraying quality.