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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 was collected in the experimental Farm of Jiangsu Academy of Agricultural Science in 2013 and 2014.The VI (Vegetation Indices) with the largest distance between the disease and control were screened out by transforming the reflectance of sensitive band.Then by comparing the various fuzzy cluster analysis methods to the disease samples, the best clustering method, K-Means, was chosen to be used in clustering the samples in each period.The dataset of rapeseed leukoplakia in 2013 and rapeseed virus in 2014 were used to test, and the results showed that under the single leaves with black background, these two diseases could be identified completely by R810/R650, R870/R650, R1080/R650, and R1200/R650, and R1200/R460 can identify rapeseed leukoplakia completely, while the recognition rate of the rapeseed virus is only 70%.R1200/R460 can distinguish these two diseases.Under the leaf with field background, the disease of rapeseed virus can be identified completely by R1200/R650, and the overall recognition rate was over 85%, but the recognition rate of rapeseed leukoplakia was low.While R1280/R460 could indetify leukoplakia completely, virus disease was low, which showed that R1200/R650 could be used to indetify the virus disease, and R1280/R460 could be used to indetify leukoplakia of Brassica napus L.in the field.In addition, the disease index (DI) was quantitied by the reflectance of 1540nm and 650nm.After tested, this model could be used to quantify the severity of rapeseed leukoplakia leaves in the field.It could provide reference for automation of accurately identifying rapeseed disease and its prevention and control in the future.