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.