Dynamic Fuzzy Neural-Network Model for Estimating Heavy Metal Concentration in Rice Using Spectral Indices and Environmental Parameters

Abstract

1.1 Predictors of heavy metal concentrations in rice Several spectral indices and environmental parameters were analyzed in order to determine heavy metal concentrations in rice. Excessive heavy metal concentrations in rice affect its chlorophyll content and cell structure(Huang et al., 2007;Liu et al., 2010b), which can be reflected by hyperspectral reflectance(Yoder and Pettigrew-Crosby 1995; Blackburn 1998; Curran et al., 2001). So, it is feasible to estimate the heavy metal concentrations in plants using hyperspectral data. That is to say, spectral indices deriving from hyperspectral reflectance were utilized to examine rice’s physiological responses to heavy metal contamination in paddy fields. Whereas environmental parameters including those relating to soil and weather were important factors for determining heavy metal diffusion in rice. They were selected as input variables on the basis of two important reasons. On the one hand, the involvement of environmental parameters facilitates the application of GDFNN model in different environmental conditions and thus increases the ability of model to be used extensively. On the other hand, the involvement of environmental parameters can improve the accuracy of prediction of heavy metal concentrations in rice leaves. Therefore, in this research, predictors of heavy metal concentrations in rice are composed of spectral indices and environmental parameters.