玉米生长铜铅污染信息光谱辨别研究

为辨别农作物所受重金属胁迫种类，以受重金属铜(Cu)、铅(Pb)胁迫的玉米叶片为研究对象，利用ASD地物光谱仪获得叶片高光谱数据，通过分数阶微分(FD)对原始光谱数据进行处理，采用竞争性自适应重加权采样法(CARS)提取特征波段，最后通过多层感知机(MLP)、K-最近邻(KNN)、支持向量机(SVM) 3种模型对受胁迫的叶片光谱进行辨别，选择最优的MLP构建的FD-CARS-MLP模型，进行玉米生长铜铅污染信息光谱辨别。结果表明，FD-CARS-MLP模型对于受胁迫叶片光谱辨别的能力相较于传统方式有所提高，试验集辨别精度均可达到98%以上，0.1、0.2阶分数阶微分辨别精度可达到99%以上。选取苗期与抽穗期的玉米叶片，对其进行FD-CARS-MLP模型的可行性测试，经验证可得，FD-CARS-MLP模型辨别受重金属胁迫玉米叶片光谱数据的精度更高且更稳定，可为监测谷类作物不同重金属胁迫提供技术与方法。

Spectral Identification of Copper and Lead Pollution Information during Corn Growth

To identify the types of heavy metal stress on crops, corn leaves under heavy metal stress of copper (Cu) and plumbum (Pb) were selected as the research object. The hyperspectral data of corn leaves were obtained by ASD Field-Spectrometer. The original spectral data were processed by fractional differential (FD), and feature bands were extracted by competitive adaptive reweighted sampling method (CARS). Finally, multi-layer perceptron (MLP), K-nearest neighbor (KNN) and support vector machine (SVM) were used to distinguish the spectra of stressed leaves. The FD-CARS-MLP model constructed by the optimal MLP was selected to distinguish the spectral information of corn growth copper and plumbum pollution. The results showed that the FD-CARS-MLP model was better than the traditional methods in spectral discrimination of stressed leaves. The accuracy of the FD-CARS-MLP model could reach more than 98% in all test sets, and the accuracy of fractional differential discrimination of 0.1 and 0.2 orders could reach more than 99%. Corn leaves at the seedling stage and heading stage were selected for the feasibility test of the FD-CARS-MLP model. It was proved that the FD-CARS-MLP model had higher accuracy and more stability in identifying the spectral data of corn leaves under heavy metal stress, which could provide technology and methods for monitoring different heavy metal stresses of cereal crops.