The hyperspectral data on total 927 leaves of different genotypes, which come from 4 ramie varieties, were collected under the field cultivation conditions to explore the identification and classification of ramie varieties with the hyperspectral as the basis. According to the hyperspectral reflection curve of ramie leaves, two groups of feature parameters were extracted, namely, the hyperspectral wave-valley reflectance and position parameters(the group V1) as well as the skewness and kurtosis parameters (the group V2). Then, by adopting the stepwise discriminant approach to screen different number of variables under different F-value settings, multiple Fisher linear discriminant functions based on these two groups of feature parameters were created respectively, and further, the created discriminant functions were comparatively analyzed from the computational complexity, the accuracy and the stability. So, we can come to the following conclusions: (1) For discriminant functions under all the combinations, the overall average accuracy was 91. 1 A and the overall standard deviation mean was 1. 2%; (2) From the comprehensive trade-off perspective, when the number of variables was between 8 and 14, the discriminant effect of the group V2 was the best among all the combinations, namely, the computational complexity was in the middle level, and both the accuracy and the stability were over their corresponding average values; among them, the discriminant functions with 13 variables had the highest average accuracy and the lowest standard deviation, which were 94. 2% and 0% respectively; (3) When the accuracy was considered preferentially and the number of variables changed between 15 and 22, the group V1 had the highest accuracy which was 95. 5 %, however, the computational complexity under this case was higher, the stability was in the middle level and the lowest standard deviation was 0. 9%. Above results showed that it was feasible to utilize the hyperspectral parameters together with the stepwise discriminant approach.
