Determining application volume of unmanned aerial spraying systems for cotton defoliation using remote sensing images

Abstract

The Unmanned aerial spraying systems (UASS), with a precise positioning system and convenient control system, have attracted more attention from researchers and the market. However, the UASS operation parameter setting still depends on the operator, and there is still a gap between the intelligent spraying. The upgrade of UASS from automation to intelligence requires a “scientific brain” to make spraying decisions. In this study, the UASS equipped with centrifugal nozzles was used to simulate defoliant spraying, combined with RGB and multi-spectral cameras to collect remote sensing images of the target area. The droplet distribution data were obtained through two years of field trials in two places. A droplet distribution prediction model based on the spray volume of UASS and the remote sensing spectral index that characterizes the cotton canopy structure is established by the BP neural network and Bayesian regularization training algorithm. The cotton defoliant was verified using this model and combined with NY/T 3213–2018 standard. The research results show that it is feasible to use remote sensing images to determine the application volume of UASSs for cotton defoliant. Compared with the conventional application rate and overspray, the defoliant spray based on the decision model can achieve the expected cotton defoliation effect and reduce the application volume.