Prediction of cadmium concentration in brown rice before harvest by hyperspectral remote sensing.

Abstract

Cadmium (Cd) contaminated rice has become a global food security issue. Hyperspectral remote sensing can do rapid and nondestructive monitoring of environmental stress in plant. To realize the nondestructive detection of Cd in brown rice before harvest, the leaf spectral reflectance of rice exposed to six different levels of Cd stress was measured during the whole life stages. In addition, the dry weight of rice grain and Cd concentrations in brown rice were measured after harvest. The impact of Cd stress on the quantity and the quality of rice grain and on the leaf reflectance of rice was analyzed, and hyperspectral estimation models for predicting the Cd content in brown rice during three growth stages were established. The results showed that rice plants can impact the quality of the brown rice seriously, even if the impact on the quantity was not significant. All the established models had the capability to estimate Cd concentrations in brown rice (R2 > 0.598), and the best performance model, with the R2 value of 0.873, was use first derivative spectrum of booting stage as variable. It was concluded that the hyperspectral of rice leaves provides a new insight to predict Cd concentration in brown rice before harvest.