Environmental Science and Pollution Research

, Volume 26, Issue 2, pp 1848–1856 | Cite as

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

  • Weihong Zhou
  • Jingjing Zhang
  • Mengmeng Zou
  • Xiaoqing Liu
  • Xiaolong Du
  • Qian Wang
  • Yangyang Liu
  • Ying Liu
  • Jianlong LiEmail author
Research Article


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.


Hyperspectral Brown rice Cd concentration Before harvest Booting stage Derivative transformation 



We are grateful to the editor and anonymous reviewers.

Funding information

This research was mainly supported by the “National key R & D project (No. 2018YFD0800201),” “Key Project of Chinese National Programs for Fundamental Research and Development (973 Program, No. 2010CB950702),” “APN Global Change Fund Project (No. ARCP2015-03CMY-Li),” and “Suzhou Science and Technology Project of China (SNG201447).”


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Life SciencesNanjing UniversityNanjingPeople’s Republic of China
  2. 2.Suzhou Institute of TechnologyJiangsu University of Science and TechnologyZhangjiagangChina

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