Evaluation of Cotton Defoliation Rate and Establishment of Spray Prescription Map Using Remote Sensing Imagery

Abstract

The site-specific management of cotton fields is necessary for evaluating the growth status of cotton and generating a defoliation prescription map. The traditional assessment method of pests and diseases is based on spot surveys and manual participation, which is time-consuming, labor-intensive, and lacks high-quality results. The RGB and multispectral images acquired by drones equipped with sensors provide the possibility to quickly and accurately obtain the overall data for a field. In this study, we obtained RGB and multispectral remote sensing images to calculate the spectral index of the target area. At the same time, ground survey data were obtained by tracking and investigating the defoliation rate of cotton after spraying. With the help of data analysis methods, such as univariate linear regression, multiple linear regression models, neural network models, etc., a cotton defoliation effect monitoring model based on UAV remote sensing images was constructed. The results show that the BP neural network based on the VARI, VDVI, RSI, NGRDI, NDVI index has an R2 value of 0.945 and RMSE value of 0.006. The R2 values of the multiple linear regression model are 0.844 based on the RSI and NGRDI indexes and RSI and VARI indexes. Additionally, based on the model, the cotton defoliation of the whole farmland was evaluated, and the spray prescription map of the UAV sprayer was obtained.

Keywords

• unmanned aerial vehicle (UAV)
• cotton defoliation
• remote sensing images
• defoliation efficiency
• spray prescription map