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

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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.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160802577998

Affiliations: 1: Zhejiang University, Institute of Agricultural Remote Sensing and Information System Application, Hangzhou, China,Zhejiang University, Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Hangzhou, China 2: Zhejiang University, Institute of Agricultural Remote Sensing and Information System Application, Hangzhou, China,Zhejiang Meteorological Institute, Hangzhou, China 3: Zhejiang University, Institute of Agricultural Remote Sensing and Information System Application, Hangzhou, China,Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province 4: Zhejiang Meteorological Institute, Hangzhou, China,Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province

Publication date: January 1, 2009

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