Abstract:

This study aims to explore the variation pattern of leaf digital texture feature of Elaeagnus L.. The leaf texture parameters were extracted based on Gray-level co-occurrence matrix from the eight species of Elaeagnus L. from Zhejiang, Jiangsu, and Anhui Province. The variation pattern of leaf texture parameters was analyzed within and among species. KNN classification model was established. The results showed that all texture parameters of the same species from different geographical sources had highly significant differences, while only one texture parameter had significant difference between different species. The KNN classification recognition model was constructed by 132 random samples as train data, 35 random samples as test data. The correct recognition rate of this model was 93.75% at K=6. The leaf digital texture has the significance of classification recognition to some species of Elaeagnus L. in a certain distribution range, and it can be used to construct k-nearest neighbor classification model.

Key words: plant identification, Elaeagnus L., leaf, texture parameter, k-nearest neighbor classification (KNN)