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In order to rapidly and accurately acquire winter wheat growing information and nitrogen content, a non-destructive testing method was developed combined with multi-spectral imaging technique and remote sensing technology to research wheat growing and nutrition status. Firstly, a 2-CCD multi-spectral image collecting platform was developed to acquire visible image and NIR image synchronously, meanwhile, the canopy spectral reflectance and the nitrogen content of wheat leaves were measured and analyzed to research the characteristics of the canopy spectral reflectance. Secondly, using calibration panels the experiential linear calibration model was established between image gray value and spectral reflectance. Thirdly, NIR image was processed to segment wheat canopy from soil and then gray value of wheat leaves was achieved by image processing of Red, Green, and Blue channels. Finally, the gray value of wheat leaves was transformed into spectral reflectance by aforementioned experiential linear model, and the vegetation index were calculated and analyzed to research the winter wheat growing and nitrogen content status. Experiment results showed that it was reasonable to diagnose nitrogen content of winter wheat based on multi-spectral imaging system and experiential linear model. There existed remarkable correlation between vegetation index (NDVI, GNDVI) and nitrogen content of winter wheat, and the correlation coefficients (R2 ) were 0.633 and 0.6.