A two-dimensional method for the detection of copper-lead stress in maize leaves based on hyperspectrum was proposed. Multi-order red-edged copper-lead sensitivity index (RECLSI) cluster of maize leaves was constructed by using the spectral values of the red edge position and two wavelengths in the 0.1~2.0 fractional order derivative (FOD) spectrum. The correlation coefficient between index and stress type in each cluster was calculated. The copper-lead identification features (CLIF) were constructed with the maximum and minimum correlation coefficients. The stress identification boundary (SIB) was established when clustering related to stress type appeared in the two-dimensional distribution of CLIF, enabling copper-lead stress identification. It was found that the maximum and minimum values of the correlation coefficient between the index and the stress type in each RECLSI cluster showed a trend of firstly rising and then falling, or firstly falling and then rising with the increase of FOD spectrum order. The poles appeared in the RECLSI clusters corresponding to the 1.3 order and 1.4 order FOD spectra, respectively. The two-dimensional CLIF distribution of 0.7~1.5 order FOD spectra showed clustering in relation to the type of stress, and the identification of copper-lead stresses could achieve different degrees according to CLIF-SIB. In the test set, the identification effect of CLIF-SIB in 1.2 order FOD spectrum was the best, with the accuracy (A) of 100%, and the 0.9 order, 1.0 order and 1.3 order FOD spectra corresponded to value of A of more than 90%. In the verification set, the identification effect of CLIF-SIB in 1.4 order FOD spectrum was the best, A was 87.5%, and the A was 81.25% at the 1.2 order FOD spectrum. The CLIF-SIB maize leaf copper-lead stress discrimination method based on FOD spectrum can effectively discriminate the stress types and it was stable.