Validation of artificial neural network techniques in the estimation of nitrogen concentration in rape using canopy hyperspectral reflectance data

Abstract

A systematic comparison of two types of method for estimating the nitrogen concentration of rape is presented: the traditional statistical method based on linear regression and the emerging computationally powerful technique based on artificial neural networks (ANN). Five optimum bands were selected using stepwise regression. Comparison between the two methods was based primarily on analysis of the statistic parameters. The rms. error for the back-propagation network (BPN) was significantly lower than that for the stepwise regression method, and the T-value was higher for BPN. In particular, for the first-difference of inverse-log spectra (log 1/R)′, T-values performed with a 127.71% success rate using BPN. The results show that the neural network is more robust to training and estimating rape nitrogen concentrations using canopy hyperspectral reflectance data.

Acknowledgements

This project was supported by the National Natural Science Foundation of China (Nos. 40271078). The authors gratefully acknowledge the data providers, including Junfeng Xu, La Chen, Qiuxiang Yi, Xiaohua Yang, of Institute of Agricultural Remote Sensing and Information Application, Huajiachi Campus, Zhejiang University, Hangzhou, China.