Abstract: To meet requirements of food supplies and the accompanying pollution problem on environment, precision fertilization is one of the most important technologies. Soil nutrient test and crop nutrition diagnosis are essential work for precision fertilization. With the current situation of the increasing agriculture scale management, it is urgent to develop fast, nondestructive and economic techniques for the nitrogen nutrition diagnosis of crops. Digital images technology has been widely applied in nutrition diagnosis of crops. In majority of such researches, digital cameras have already been successfully used. However, few researches were reported to use cellphone cameras to study nutrition diagnosis and precision fertilization of crops. Thinking of the advantages that cellphone cameras have, such as portability, universality and handleability, the application of cellphone cameras should be detailly studied in nutrition diagnosis. In this study, we used smart cellphones to photograph corn leaves at 6-leaf and 9-leaf stages. The color parameters of corn leaves images were extracted and processed. The differences in color parameters of leaves photographs during two growth stages and for four varieties of corn were evaluated. The correlations of parameters with traditional nitrogen nutrient indexes were determined. Appropriate color parameters were selected based on statistical analysis and nutrient diagnosis model established for the color parameters and nitrogen nutrition index. Then the model was fitted to establish indicator systems of diagnosis of nitrogen nutrient and recommendations for fertilization of corns. The results showed that correlations of color parameters and nitrogen nutrient indexes at 6-leaf stage were more significant than those at 9-leaf stage, suggesting that 6-leaf stage was suitable time for diagnosis of corn nitrogen nutrient using digital image processing technique. From the analysis of leaves photographs of four corn varieties, there was no statistically significant difference among the images. Furthermore, the consequences supported two color parameters, B/(R+G+B) and G/(R+G+B) as candidates for sensitive color parameters. These two color parameters both had strong correlations with leaf SPAD and vein nitrate concentration. Also based on multivariate analysis, B/(R+G+B) was the best and was selected as sensitive color parameter for diagnosis of corn nitrogen nutrient. The diagnosis model of vein nitrate concentration was 1.73×10¹⁰×B/(R+G+B)^9.43. Based on the equation, nitrogen application rates under different B/(R+G+B) values were calculated for certain yield targets of corn. The results were applied to nitrogen nutrient diagnosis and recommendation of fertilization of corn. In summary, it was possible and applicable to take photographs of corn leaves at 6-leaf stage with smart cellphone, extract B/(R+G+B) color parameter and use it to diagnose nitrogen nutrition status.