基于小波变换和BP神经网络的水稻冠层重金属含量反演

Inversion of Heavy Metal Content in Rice Canopy Based on Wavelet Transform and BP Neural Network

摘要:

自然农田生态系统中，农作物的各种生化参数受重金属污染胁迫后虽表现异常，但其特征往往极为微弱，极不稳定。利用处理非稳定信号方法中常用的信号处理方法——小波分析法（Db-5），对水稻的光谱反射率数据进行处理，有效提取光谱信号中受重金属污染胁迫而潜藏的一些“突变”弱信息。利用Db-5小波基进行小波变换，从中选取具有异常光谱特征的奇异点，利用奇异点对应波段（716、745、766nm）的光谱反射率构建反向传播（BP）神经网络模型，对水稻冠层4种重金属含量进行反演。将利用模型得到的预测值与实测值进行相关性分析，结果表明，基于BP神经网络的水稻冠层重金属含量反演模型对于实验区镉、铅、汞、砷4种重金属胁迫，具有良好的反演效果。

Abstract:

In the “natural farmland ecosystem”, although the biochemical parameters of crops are abnormal under the stress of heavy metal pollution, their characteristics are often very weak, with small changes and extreme unstability. Wavelet analysis, a common signal processing method in unstable signal processing, was used to process spectral reflectance data of crops (rice) and effectively extract weak information of “mutation” hidden in spectral signals under the stress of heavy metal pollution. Wavelet transform was carried out by using Db-5 wavelet basis, and singular points with abnormal spectral characteristics were selected. Back propagation neural network model (BPNN) was constructed by using spectral reflectance of corresponding bands of singular points (716nm, 745nm and 766nm) to invert the contents of four heavy metals in rice canopy. Correlation analysis was conducted between the predicted and measured values of the model, and the results showed that the inversion model of heavy metal content in rice canopy based on BP neural network had a good inversion effect on the stress of cadmium, lead, mercury and arsenic in the experimental area.