基于CWT-HHT的玉米叶片铜离子污染信息探测

为了准确探测农作物在不同浓度重金属污染下叶片光谱间微弱的畸变信息，本研究通过设置不同浓度铜离子（Cu2+）胁迫下的玉米盆栽实验，在采集了不同梯度下玉米叶片光谱并测定同期叶片Cu2+含量的基础上，采用连续小波变换（CWT）结合希尔伯特-黄变换（HHT）的方法，构建CWT-HHT算法以探测玉米叶片光谱重金属污染信息，同时与红边位置（REP）、红边归一化指数（NDVI705）和红边植被胁迫指数（RVSI）等常规的植被指数监测方法进行对比分析。结果表明：基于CWT-HHT探测方法提取的瞬时能量峰值呈现先升高、后降低的趋势，与玉米叶片Cu2+含量变化趋势一致。而且通过与植被指数监测农作物重金属污染的方法对比，证明CWT-HHT探测结果最优，表明CWT-HHT方法在玉米叶片重金属Cu2+污染信息探测方面具有可行性。

Detection of copper ion pollution in corn leaves based on continuous wavelet transform-Hilbert-Huang transform

To accurately detect weak spectral distortion information for crops under different concentrations of heavy metal pollution, a corn pot experiment with different Cu²⁺ stress gradients was performed. The spectra of corn leaves under different gradients were collected and the Cu²⁺ content of the leaves was measuring at the same time. Continuous wavelet transform(CWT) combined with Hilbert-Huang transform(HHT)was used to construct a CWT-HHT algorithm to detect spectral copper pollution information from the corn leaves. This method was compared with other conventional vegetation index monitoring methods, such as the red edge position, the red edge normalization index, and the red edge vegetation stress index. The results showed that the instantaneous energy peak extracted using the CWT-HHT detection method had a trend of first increasing and then decreasing, which was consistent with the trend in the Cu²⁺ content of the corn leaves. Moreover, the CWT-HHT method was found to be better than the vegetation index monitoring method for detecting heavy metal pollution in crops, indicating that the CWT-HHT method is feasible for the detection of heavy metal copper pollution in corn leaves.