The leaves of rice were captured by scanner. Integrated method combining digital image processing, parameter optimization and classification was used to explore leaves spectral and shape characteristics which were adopted to diagnose and recognize rice nitrogen nutrition. Proportion of etiolated area in the tip of leaf was extracted by method of object-oriented classification. The results of exponential regression analysis showed high correlation between tip etiolated area proportion and leaf nitrogen concentration (R2=0.863). The color indices of tip as well as whole leaves were extracted and exponential regression analysis with leaf nitrogen concentration was made, which illustrated the better performance of representation of rice nitrogen nutrition with tip information. Optimal selection of subset by means of CfsSubsetEval and Scatter search combined with support vector machine were used for pattern recognition. The result of accuracy assessment indicated that nitrogen deficiency and healthy leaves could be easily recognized and the accuracy descended with the improvement of nitrogen treatment. The accuracy of excessive nitrogen nutrition status was low. The leaf area could be a favorable assistant for recognition under deficient and healthy status. 