Abstract: Abstract: To explore the feasibility of monitoring nitrogen elements in beet canopy leaves by digital camera, field experiment with different planting schemes was carried out in Chifeng City, Inner Mongolia in 2014. Canopy digital images of beet grown under different nitrogen application rates were captured several times during the whole growth stage. The change trend of canopy LNC (leaf nitrogen content) under different nitrogen levels was analyzed. It was found that canopy LNC is relatively high in the middle period of rapid growth and later stage of sugar growth, and the canopy grow trend is from high to low during the whole stage. Canon EOS7D digital camera with the resolution of 5184×3456 was used for image acquisition. In order to keep the light source consistent and improve the comparability of the images captured in different stages, collection time was set at noon from 12:00 to 14:00, when the weather was clear and calm. The camera was 1.50 m above the beet canopy and had an included angle of 60° with the ground. Complete beet canopies of adjacent 2×2 plants were selected and the image was stored in JPEG format. In view of the difference of gray value between leaves and background, threshold segmentation method based on gray value was used to segment the soil, shading leaves, and numbered signs. After that, the color image with green leaves only was obtained, and the R (redness intensity), G (greenness intensity), and B (blueness intensity) values were extracted. Ten image feature parameters were chosen to analyze their correlation with monitoring evaluation index of canopy nutrition under different schemes, including 3 single color characteristic values (R, G and B), 4 linear combination parameters (G/R, G+B, R/B and R-B), and 3 linear combination parameters by standardized processing (R/R+G+B, G/R+G+B and B/R+G+B). It was found that different characterization ability exists among 3 single color parameters, and the correlation between the composite characteristic parameters and the canopy LNC has significant improvement compared with that between the single color parameter and the canopy LNC. Interactive tuning R, G and B tricolor component coefficients, and the method of primary color weight optimization was proposed. The BOP (basic optimal parameter) and NOP (normalized optimal parameter) were extracted to characterize nitrogen elements in beet canopy leaves. The results show that the 2 tuning parameters have great effect on correlation and fitting accuracy compared with color characteristic parameters G/R and NRI (normalized redness intensity) obtained by conventional methods. The correlation coefficients between G/R, NRI and canopy LNC were 0.80 and 0.79, respectively, and those between BOP, NOP and canopy LNC were 0.84 and 0.83, respectively. Comparing prediction accuracy, the R2 values of the model based on the tuning parameters BOP and NOP were 0.69 and 0.68, respectively, which were higher than the contrast indices G/R and NRI. The RMSE (root mean square error) values of the model based on the tuning parameters were 2.65 and 2.73, respectively, which were lower than the contrast indices. It can be seen that the method of primary color weight optimization is more efficient than the conventional color characteristic parameter selection method. The sensitive parameters affect the accuracy of crop nutrition monitoring, however, most studies choose sensitive parameters from the common, and researches are few in the constructing parameters method. This paper analyzed R, G and B primary weights, and proposed a general method for constructing color sensitive parameters. The optimization parameters BOP, NOP and coefficient optimization model were constructed, and the parameters weight range was analyzed and regulated, and then BOP and NOP were optimized. This study can provide a basis for the nutritional diagnosis of other crops, and also show it is possible to estimate the nutrition deficiency using digital camera under the conditions of field natural light. This indicates that conventional low-cost digital cameras can be used for determining nitrogen content in beet canopy leaves, and also offers a potentially inexpensive, fast, accurate and suitable tool for small farms.