The non-destructive, rapid and accurate monitoring diagnosis of rapeseed diseases is of significance for sustainable development of rapeseed production and environment protection. The spectrum data of rapeseed leaf leukoplakia were collected in the experimental Farm of Jiangsu Academy of Agricultural Science in 2013 and 2014. Firstly, the common distinctive bands of the disease and the health were found by comparing reflectance spectrum of leaves in the field and under black background. The results showed that with the progress of the growth period, reflectance of disease leaves decreased earlier than healthy leaves. It was the best period to identify rapeseed leukoplakia from 11 days after early anthesis to 9 days after finish flowering to identify rapeseed leukoplakia in the field due to during this period the reflectance of healthy leaves remained at 35 % while the disease diseased to 30 %. The sensitive band was in the range of 760–1080 nm. The correlation among disease index (DI), agronomic parameters, and the reflectance of the disease samples were analysed, and the results showed that there were high correlations between DI, and agronomic parameters and reflectance, e.g., the correlation between the leaf moisture content and the reflectance in 460 nm, 550 nm, 650 nm, 710 nm, 760 nm, 1480 nm, and 1600 nm, between the leaf nitrogen content and the reflectance in 810 nm, 870 nm, 1080 nm, 1280 nm, 1320 nm, 1540 nm, 1600 nm, 1650 nm, and 1700 nm, and between the SPAD value and the reflectance in 1200 nm, 1280 nm, and 1540 nm had significance with p < 0.01. The quantitative models of agronomic parameters based on reflectance were developed by stepwise regression, principal component analysis, and curve fitting. The data of rapeseed leukoplakia in 2013 and rapeseed virus in 2014 were used to test. The results showed that in the same disease test, the quantitative models of moisture content based on reflectance were fit well. In the different disease test, the quantitative models were fit badly except the model of moisture content. The model on moisture content performed reasonably well, though performance of precision could probably be improved by further analysis, and the paper would provide a basis for spectrum-based identifying of rapeseed leaf leukoplakia.