The feasibility of non-destructive assessment and prediction of tobacco nitrogen nutrition based on visible spectrum diagnostic technology on UAV platform was analyzed by field experiments with different nitrogen dosage, and the optimum color parameters and model of the technology were defined. Field experiments were carried out in Anfu, Jiangxi Province in 2018. Different nitrogen dosages were set at 0, 45, 90, 135, 180 and 300 kg N·ha^-1. At 47 days after transplantation (cluster stage), 83 days after transplantation (flourishing late stage) and 116 days after transplantation (mature stage of lower leaves), digital images of canopy RGB color were obtained by UAV. At the same time, plant samples were collected to analyze aboveground biomass and leaf biomass. Digital analysis of canopy images was carried out to obtain the value of color indices. Through correlation analysis between color indices and tobacco nitrogen nutrition indices, appropriate color indices were screened and nitrogen nutrition diagnosis equation was established. The fitting accuracy of the diagnostic equation of nitrogen nutrition was validated by the experiment of nitrogen consumption in different plots. The results showed that the standard color values of canopy images were significantly different between different treatments in the later stage of flourishing growth, and there was no significant difference between the clustering stage and the maturity stage of lower leaves. Among the 15 color indexes, NRI, NGI, G/R, G/(R+B), (G-R)/(R+G+B) and ExG were significantly correlated with nitrogen nutrition indexes of 5 flue-cured tobacco varieties (p<0.01). According to the screening method of canopy color index, NGI, G/R and ExG are the potential optimal color parameters in the normalized color index system, the ratio color index system and the normalized difference color index system. According to different types of regression analysis results, exponential regression was determined as the prediction model of aboveground biomass and leaf biomass, and linear regression as the prediction model of aboveground nitrogen concentration, leaf nitrogen concentration and leaf SPAD value. The RMSE values of G/R for aboveground nitrogen concentration and leaf nitrogen concentration were 0.375 1% and 0.249 1%, respectively, significantly lower than NGI and ExG, with the highest prediction accuracy. The prediction equations of above-ground biomass, leaf biomass, above-ground nitrogen concentration, leaf nitrogen concentration and SPAD value expressed by G/R value are Y=21.785e^{1.350 2G/R}, Y=4.057 9e^{1.937 3G/R}, Y=5.039 9G/R-3.333 2, Y=4.281 4G/R-3.802 9, Y=40.168G/R-28.188. Therefore, the UVAs platform-based visible spectrum diagnosis technology has application potential in the nitrogen nutrition diagnosis of flue-cured tobacco. The best evaluation period is the flourishing later stage, and the best prediction parameter is G/R value.