基于叶片数字纹理特征自动识别胡颓子属植物

为了探索胡颓子属植物叶片的数字纹理特征变异规律,对苏、浙、皖地区常见的8种胡颓子属植物,提取了基于灰度共生矩阵的叶片纹理参数,分析了叶片纹理参数的种内、种间变异规律,并构建KNN分类模型。结果表明:同种不同地理来源的标本间全部纹理参数是极显著差异,不同种之间仅某一纹理参数有显著差异;随机取132个样本作为训练集,35个作为测试集,构建KNN分类模型,K=6时,正确识别率达到了93.75%。对于特定分布区内的几个胡颓子属植物,叶片数字纹理具有分类识别意义,可用于构建分类模型。

Automatic Identification of Elaeagnus L. Based on Leaf Digital Texture Feature

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.