Abstract: 【Objective】The purpose of the study was to create database of characteristics from maize leaf transmission images, analyze the rules of characteristics variation with maize varieties and the recognition results of different types of characteristics in order to provide a basis for further research of identifying maize varieties from leaf transmission image of different growth periods based on machine vision. 【Method】 Twenty-one common varieties of maize were selected as the research materials. The maize leaves at jointing stage, small bell stage, large bell stage and tasselling stage were collected. A total of 420 high quality transmission images of maize leaves were taken in lamp box. The software for characteristic extraction and recognition of maize leaves was designed and developed based on Matlab R2009a, which included image preprocessing module, characteristic extraction module, neural network recognition module and threshold selection module. The transmission images of maize leaves at jointing stage, small bell stage, large bell stage and tasselling stage were pre-processed by the software. Then 48 characteristics of color group, shape group and texture group were extracted from transmission images of maize leaf, and a total of 20 160 characteristic data. In order to study the rules of characteinristics variation with maize varieties, the coefficient of variation of 48 characteristics of leaf transmission image among different maize varieties were analyzed. In order to search the important characteristics with strong ability of identifying maize varieties from transmission images of leaves, the Artificial Neural Network was built and the recognition rate of single characteristics from different time were analyzed. In order to study the recognition results, the recognition rates of the three groups of characteristics and the group combinations of characteristics from different time were further analyzed. 【Result】 The results in 4 stages indicated that there were significant differences in the coefficient of variation of 3 groups of characteristics among different maize varieties. The differences were stable with the growth of maize. The coefficient of variation of color group was the highest, then the texture group and the third was the shape group. The results at 4 stages also indicated that there were significant differences among the recognition rates of 48 single characteristics. The recognition rates were between 9.52% and 29.33%. According to the recognition rates, the important characteristics were in the following order: the standard deviation of R, the minor axis length, the standard deviation of H, the diameter, the average value of H, the standard deviation of V, the standard deviation of B, the invariant moment 6, the eccentricity, the average value of S, the external convex polygon area, the average value of B, the smoothness, the kurtosis of V, and the standard deviation of S. Those average recognition rates was over 18%. The average recognition rate of the color group was the highest which was 86.76%, then the texture group which was 78.05%, and the third was the shape group which was 68.67%. The stability of recognition rates of the texture group was the highest, then the color group and the third was the shape group. The average recognition rate of combinations of shape group and color group was the highest which was 92.29%, then the combinations of color group and texture group which was 90.29%, and the third was the combinations of shape group and texture group which was 87.43%. At jointing stage, there was no obvious difference among the recognition rates of combinations of 3 groups. At small bell stage, the recognition rate of the combinations of color group and texture group rose sharply, while the recognition rate of the combinations of shape group and color group fell slightly, and the recognition rate of the combinations of shape group and texture group showed a marked decline, which still remained above 82%. There was no significant difference between recognition rates of the shape group and texture group and the color group and texture group at jointing stage, large bell stage and tasselling stage. And the recognition rate of combinations of the shape group and color group was always the highest. 【Conclusion】 This study has provided rich primary data on characteristics from maize leaf transmission images, and has provided a quantitative basis and a new method for further research of identifying maize varieties in different growth periods, which will play a good reference role in crop varieties identification.

Key words: maize , transmission image , machine vision , artificial neural network (ANN) , variety identification