Abstract：Heavy metals can enter the human body through the food chain after the crops had been polluted by them and can seriously harm the body health. Therefore, how to quickly and accurately monitor the content of heavy metals in crops has become important research in the fields of ecology and food security. The conventional biochemical monitoring methods have the disadvantages of cumbersome operation, long implementation process and destructiveness, while the hyperspectral remote sensing has the advantages of high spectral resolution, a large amount of information, strong biochemical inversion ability, convenience and fast, and no damage to the monitored object, so using hyperspectral remote sensing to monitor of heavy metal content in crops has become one of the hotspots in the field of remote sensing research. The potted corn plants stressed by different concentrations of Pb(NO3)2 solution were used as the research object in the paper, based on the data of the reflectance spectra of corn leaves under different lead ion (Pb2+) stress gradients and the measured Pb2+ contents in the leaves and combined with the Singular Value Decomposition (SVD) theory and Adaptive Network-based Fuzzy Inference System (ANFIS) structure, an SVD-ANFIS model was established for predicting the Pb2+ content in corn leaf. Firstly, SVD was used to process the reflectance spectra of Old leaves (O), Middle leaves (M), New leaves (N) under different stress gradients so that the singular values of the original spectral information were obtained. Then, the singular values corresponding to O, M, N leaves were selected to seek the optimal input combination of the ANFIS structure. Finally, the singular values of the spectra of the O-M (double-input) combination were selected as the input quantity of the ANFIS structure. After obtaining the optimal fuzzy rule base through training and learning, the output quantity of ANFIS structure was the content of Pb2+ in the leaves. Thus the SVD-ANFIS model achieved its predictive performance. The results showed that the model’s output error value was small and the prediction accuracy was high, and the prediction effect was best when the membership function was chosen as bell function in the fuzzy training process. When the multi-parameter Back Propagation (BP) neural network prediction model was used to verify the superiority of the prediction of the SVD-ANFIS model, the determination coefficient (R2) of the BP model and SVD-ANFIS model were 0.977 6 and 0.988 7, and the root means square error (RMSE) were 2.455 9 and 0.601 3 respectively, so the SVD-ANFIS model was shown to has a higher fit degree and better prediction effect. At the same time, spectral data of the corn leaves polluted by Pb2+ in different years were selected to test the feasibility of the SVD-ANFIS model, and its R2 and RMSE were 0.986 4 and 0.887 4, respectively, it indicated that the SVD-ANFIS model could be better used to predict the content of Pb2+ in corn leaves with high robustness and could be used as a method to predict the content of heavy metals in corn leaves.
Key words：Spectral analysis; Corn leaves; Singular value decomposition; Adaptive network-based fuzzy inference system; Heavy metal pollution; Prediction model
韩倩倩，杨可明，李艳茹，高 伟，张建红. 玉米叶片重金属铅含量的SVD-ANFIS高光谱预测模型[J]. 光谱学与光谱分析, 2021, 41(06): 1930-1935.
HAN Qian-qian, YANG Ke-ming, LI Yan-ru, GAO Wei, ZHANG Jian-hong. SVD-ANFIS Model for Predicting the Content of Heavy Metal Lead in Corn Leaves Using Hyperspectral Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1930-1935.
