Heavy metal pollution of agricultural products has attracted much attention along with the improved human quality of life. The heavy metal elements in crops will harm human health through the food chain, and different heavy metal elements have a large difference in toxicity to the human body. Therefore, it is crucial to distinguish the types of heavy metal elements in crops. There are many shortcomings in the traditional methods of detecting heavy metals such as many links, long time, and high cost. However, hyperspectral remote sensing technology has the advantages of abundant information usage, strong physical and chemical inversion capabilities, fast analysis speed, non-destructive monitoring and so on. It has gradually become one of the important methods for analysing heavy metal pollution in crops.Taking the leaf spectra of a typical corn crop growing under soil stressed by different CuSO₄·5H₂O and Pb(NO₃)₂ concentration gradients as the research object, the copper (Cu) and lead (Pb) identification index (CLI) was builtbased on spectral processing results of continuum removal (CR), spectral ratio (SR)and fractional-order derivative (FOD) combining with modified red edge simple ratio index (MSR). Then the Cu and Pb element discrimination feature points (CLDFP) were established by selecting the three CLI values of fractional differential orders that have the strongest correlation with the types of Cu and Pb elements. And then, the Cu and Pb elements discriminant rule line (CLDRL) under the two-dimensional coordinate system (2D) and the discriminant rule plane (CLDRP) under the three-dimensional coordinate system (3D) were structured to identify the types of Cu and Pb elements. Based on the Euclidean cluster (EC)- the perpendicular bisector (PB) by using the EC to divide the training samples into two sets of Cu pollution and Pb pollution and combining with the PB to connect the circle enters the sets so that the types could be accurately identified on the heavy metal Cu and Pb elements in the spectral information of corn leaves. The results showed that the correlation between the spectral information of corn leaves and the types of Cu and Pb elements was enhanced because of the CR-SR-FOD spectral transformation processing. The correlation coefficients of the CLI corresponding to each order of FOD and the types of Cu and Pb elements were different. With the increase of orders, the correlation showed a trend of increasing first and then decreasing. Among them, the three values of orders of the highest correlation coefficients were 1.2, 0.7, and 1.0 respectively. The accuracy rate of the training set samples was 78.95% andthe accuracy rate of the verification set samples was 75.0% when discriminated under the 2D, and the accuracy rate of the training set samples was 76.32% and the accuracy rate of the verification set samples was 75.0% when discriminated under the 3D, it is proved that the spectral discriminant rulesof 2D CLDRL and 3D CLDRP based on EC-PB could effectively identify the types of Cu and Pb pollution elements when they polluted the corn leaves.