Abstract: Abstract: This paper explored the digital image evaluation index of the monitoring of winter wheat nitrogen nutrition and the establishment method based on digital image processing technology. The images of winter wheat canopy at different stages under different planting schemes (2 varieties, 2 planting density levels and 3 nitrogen fertilizer application rates) were taken from 2012 to 2014, and the basic color characteristic value of canopy image samples was extracted using the algorithm based on the normalized k-means clustering segmentation for the H component. Firstly, it chose 9 image feature parameters, including 3 monochromatic components (r, g and b), 3 linear combination parameters (r-g-b, r-g and r-b), and 3 linear combination parameters by standardized processing ((r-g-b)/(r+g+b), (r-g)/(r+g+b) and (r-b)/(r+g+b)), and analyzed contrastively the correlation between the above parameters and the monitoring evaluation index of nitrogen nutrition under different schemes. It was found that the characterization ability of 3 monochromatic components for wheat nitrogen nutrition level was not the same, but for the combination characteristic parameters by the 3 monochromatic components, the degree of correlation was better than the 3 monochromatic components, and at the same time, the increase of the level of correlation was more obvious after further normalized. So, to establish the image evaluation index of the monitoring of wheat nitrogen nutrition, the first step was to choose the base color component which had a stronger representation ability of leaf nitrogen content and increase its proportion, the second step was to adjust and optimize the combination weights of the other remaining monochromatic components, and the final step was to standardize it. A linear fitting was carried out at different sampling periods and under different cultivation schemes, which made use of basal image color characteristic values and leaf nitrogen concentration (LNC) measurement during the same period in 2013, determined the best fitting content, and put forward the image normalized color mix index (NCMI) under the RGB (red, green, blue) color space. To study the feasibility of NCMI on monitoring nitrogen nutrition state, the experimental data in the year of 2014 were divided according to different periods and different cultivation schemes, and the correlation between NCMI and LNC, as well as that between other 3 typical image evaluation parameters, i.e. normalized red index (NRI), dark green color index (DGCI) and ratio of green light to red light (G/R) and LNC was analyzed quantitatively. The results showed that the correlation and the fitting of NCMI as winter wheat nitrogen nutrition evaluation index kept a good suitability, accuracy and stability, and meanwhile NCMI had a consistent change law with other 3 typical image characteristic indices (DGCI, G/R, NRI) under each scheme in 2014. Among them, the values of R2 between established NCMI and LNC during 3 sampling periods (March 8th, March 31st and April 15th) were respectively 0.77, 0.79 and 0.94, higher than 3 contrast indicators at different degree, and the root mean square error (RMSE) reduced by 0.18%, 0.37% and 1.67% respectively compared with the best RMSE of other 3 indices. Under the certain vegetation canopy coverage condition, the correlation between NCMI and LNC for the 2 cultivars (Shengxuan No.6 and Yangmai No.18) on March 31th and April 15th was better than that of the 3 comparative indices, and the lowest RMSE were 0.1833 and 0.2230, respectively; the related degree between NCMI and LNC under the planting density of D2 (3.0×106 plant/hm2) in the 3 periods was higher, and the values of R2 and RMSE were superior to the 3 indices on March 8th, while they were consistent with those between DGCI and NRI at the other 2 stages; the RMSE between NCMI and LNC on April 15th was 0.1299, which was reduced by 5.18% compared with the lowest RMSE of the other 3 indicators; NCMI was also better than other indices under the N1 treatment (pure nitrogen 150 kg/hm2) of the D2 planting density, and the R2 increased by 7.69% and the RMSE improved by 4.11% compared with the best performer NRI. Therefore, it's more suitable to choose NCMI to be the digital image evaluation index of winter wheat nitrogen nutrition compared with other parameters under certain canopy coverage.