BAS-ELM based UAV hyperspectral remote sensing inversion modeling of rice canopy nitrogen content

Abstract: The rapid, nondestructive, and accurate estimation of nitrogen content in rice can help to obtain the growth condition of rice in time, which is of great significance for guiding rice field management.  In order to improve the accuracy of high-spectral inversion of rice canopy nitrogen content, the discrete wavelet multiscale decomposition (DWMD) was used to downscale the high-spectral information in the range of 400 nm to 1000 nm by using the UAV hyperspectral image data and the synchronously measured rice canopy nitrogen content as the data source, from which the hyperspectral characteristic variables for rice nitrogen content inversion modeling were extracted.  And using the dimensionality reduction variables as the data base, three neural network inversion methods, including extreme learning machines (ELM), particle swarm optimization for extreme learning machines (PSO-ELM), and beetle antennae search algorithm for extreme learning machines (BAS-ELM), were used to establish the rice nitrogen content drone hyperspectral remote sensing inversion model, and the results showed that: (1) The hyperspectral range from 400 nm to 1000 nm was dimensionally reduced by DWMD, and finally the continuous hyperspectral reflectance information was dimensionally reduced to sixteen discrete hyperspectral features for subsequent inversion modelling of rice nitrogen content.(2) In the model adopted in this study, BAS-ELM has the highest accuracy, where the R 2 of training data, the R 2 of test data, the RMSE of training data, and the RMSE of test data is were 0.864, 0.863, 0.247, 0.254. Keywords: UAV, Nitrogen inversion, rice, Hyperspectral dimensionality reduction, ELM DOI:  10.33440/j.ijpaa.20200303.105 Citation: Yu F H, Feng S,Yao W X, Wang D K, Xing S M, Xu T Y.  BAS-ELM based UAV hyperspectral remote sensing inversion modeling of rice canopy nitrogen content.  Int J Precis Agric Aviat, 2020; 3(3): 59–64.