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中国农学通报 ›› 2020, Vol. 36 ›› Issue (11): 20-25.doi: 10.11924/j.issn.1000-6850.casb18110086

• 生物科学 • 上一篇    下一篇

基于叶片数字纹理特征自动识别胡颓子属植物

王雷宏1, 陈永生1(), 郑玉红2   

  1. 1安徽农业大学林学与园林学院,合肥 230036
    2江苏省中国科学院植物研究所,南京 210014
  • 收稿日期:2018-11-16 修回日期:2019-01-27 出版日期:2020-04-15 发布日期:2020-04-28
  • 通讯作者: 陈永生
  • 作者简介:王雷宏,男,1977年出生,山西五台人,副教授,博士,主要从事园林植物种质资源研究。通信地址:230036 安徽省合肥长江西路130号 安徽农业大学林学与园林学院,E-mail: wangleihong208010@126.com
  • 基金资助:
    国家自然科学基金资助项目“不同林龄序列亚热带常绿阔叶林地下碳氮耦合循环特点”(31370626)

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

Wang Leihong1, Cheng Yongsheng1(), Zheng Yuhong2   

  1. 1School of Forestry and Landscape of Architecture, Anhui Agricultural University, Hefei 230036
    2Institute of Botany, Jiangsu Province and the Chinese Academy of Sciences, Nanjing 210014
  • Received:2018-11-16 Revised:2019-01-27 Online:2020-04-15 Published:2020-04-28
  • Contact: Cheng Yongsheng

摘要:

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。

关键词: 植物识别, 胡颓子属, 叶片, 纹理参数, 最近邻分类器

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)

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