Xiu Li-Na, Liu Xiang-Nan, Liu Mei-Ling
College of Information Engineering, China University of Geosciences, Beijing 100083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2011 Jan;31(1):192-6.
In order to detect the Cd stress levels of rice growing in natural environment fast and accurately, based on wavelet transform technology in the visible light and near-infrared region (NIR), a method of identifying stress levels of rice under Cd pollution was explored. The hyperspectral data, biochemical parameters and heavy metals concentration in folium were collected for the rice growing in natural farmlands. Wavelet transform of hyperspectral reflectance (350-1 300 nm) was performed by using Daubechies 5 mother function and wavelet energy coefficients of spectral reflectance were extracted. In addition, the model between wavelet energy coefficient and Cd content was established. The result showed that the wavelet coefficients of the fifth decomposition level (d5) proved successful for detecting Cd pollution of rice; the singularity range of rice located in the region around 550-810 nm of spectral signal under Cd pollution; and the singularity amplitude was 0.04; The centre of modulus maxima located at 700 nm. Regression model based on third level wavelet energy coefficient can estimate the Cd content of rice accurately with the coefficient of determination (R2) of 0.958, and root mean square error (RMSE) of 0.122. It can be concluded that the singularity analysis technology applying wavelet transform to reflectance has been shown to be very promising in detecting rice under Cd pollution effectively, and wavelet energy coefficients can estimate Cd content of rice, and provide important reference for detecting other metal-induced stress on crop.
为了快速、准确地检测自然环境中生长的水稻的镉胁迫水平，基于可见光和近红外区域（NIR）的小波变换技术，探索了一种识别镉污染下水稻胁迫水平的方法。收集了自然农田中生长的水稻的高光谱数据、生化参数和叶片中的重金属浓度。利用Daubechies 5母函数对高光谱反射率（350 - 1300 nm）进行小波变换，并提取光谱反射率的小波能量系数。此外，建立了小波能量系数与镉含量之间的模型。结果表明，第五分解层（d5）的小波系数在检测水稻镉污染方面取得成功；镉污染下水稻光谱信号的奇异区域位于550 - 810 nm附近；奇异幅度为0.04；模极大值中心位于700 nm。基于第三层小波能量系数的回归模型能够准确估计水稻的镉含量，决定系数（R2）为0.958，均方根误差（RMSE）为0.122。可以得出结论，将小波变换应用于反射率的奇异分析技术在有效检测镉污染下的水稻方面显示出很大的前景，小波能量系数能够估计水稻的镉含量，并为检测其他金属诱导的作物胁迫提供重要参考。
