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林业科学 ›› 2020, Vol. 56 ›› Issue (8): 47-54.doi: 10.11707/j.1001-7488.20200806

• 论文与研究报告 • 上一篇    下一篇

基于叶片形态的毛竹单叶叶面积模型

巫娟1,2,胡姝珍1,茅思雨1,2,邹凯1,2,郑淇元1,邱啟璜1,施建敏1,2,*   

  1. 1. 江西农业大学林学院 南昌 330045
    2. 江西省竹子种质资源与利用重点实验室 南昌 330045
  • 收稿日期:2019-06-19 出版日期:2020-08-25 发布日期:2020-09-15
  • 通讯作者: 施建敏
  • 基金资助:
    国家自然科学基金项目(31660198);国家自然科学基金项目(31260174);江西省重点研发计划项目(20161BBF60082);国家大学生创新基金训练项目

Single Leaf area Model of Phyllostachys edulis Based on Leaf Morphology

Juan Wu1,2,Shuzhen Hu1,Siyu Mao1,2,Kai Zou1,2,Qiyuan Zheng1,Qihuang Qiu1,Jianmin Shi1,2,*   

  1. 1. College of Forestry, Jiangxi Agricultural University Nanchang 330045
    2. Jiangxi Provincial Key Laboratory for Bamboo Germplasm Resources and Utilization Nanchang 330045
  • Received:2019-06-19 Online:2020-08-25 Published:2020-09-15
  • Contact: Jianmin Shi

摘要:

目的: 毛竹叶片小、易卷曲的特点增加了叶面积测量的难度和测量误差,本研究旨在建立快速、便捷、准确测量毛竹叶片面积的模型。方法: 采集7个省份的毛竹叶片,对毛竹叶片形态进行分类,利用叶长、叶宽数据和扫描所得实际叶面积进行建模,并采用均方根误差、卡方、赤池信息量准则和预测精度检验模型的精度,同时与叶面积仪测定结果进行精度比较。结果: 1)根据长宽比,毛竹叶片可以分为3类,分别是类三角形叶片(长宽比≤7.2)、长椭圆叶片(7.2 <长宽比≤ 8.3)和细长条叶片(长宽比>8.3);2)对毛竹叶面积和叶形态学指标的相关分析显示,叶片长度与宽度的积对叶面积影响最大,相关系数为0.993;3)建立以叶片长宽积为自变量的叶形分类拟合模型,其决定系数为0.990 1、均方根误差为0.159 6、卡方值为10.368 1、赤池信息量准则为-6 317.10、预测精度为97.73%,其预测结果最佳,优于叶形不分类的整体拟合和叶面积仪测量结果。结论: 基于叶片形态分类的毛竹叶面积拟合模型仅需测定叶片的长和宽,便可准确预测叶片面积,其精度不但优于叶面积仪与整体拟合结果,而且测量过程快速、便捷。该模型可解决长期困扰的毛竹叶片面积测量难题。

关键词: 分类拟合, 叶面积仪, 扫描, 叶形, 长宽比

Abstract:

Objective: Phyllostachys edulis leaves are small and easy to be curled, which increases the difficulty and error of area measurement. Our aim of this study is to build a quick, convenient and accurate measurement of leaf area for P. edulis. Method: We collected leaves from 7 provinces and classified the leaf shapes. The data of leaf length and leaf width, as well as leaf area measure by the scanner were used to build the leaf models. The root mean square error, chi-square test, chi information criterion and predicted precision were used to test the errors and the goodness of the models. Result: 1) According to the length-width ratio, P. edulis leaves can be divided into three categories, which are triangular leaves (length-width ratio ≤ 7.2), elliptic leaves (7.2 < length-width ratio ≤ 8.3) and slender leaves (length-width ratio ≥ 8.3), respectively; 2) Morphological correlation of P. edulis showed that the product of leaf width and length was the closest factor related with leaf area, with ahigh correlation coefficient of 0.993; 3) The Classified fitting model of P. edulis leaf shape was based on the product of leaf length and width, with the determination coefficient of 0.990 1, the root-mean-square error value of 0.159 6, the chi-square value of 10.368 1, the Chi information criterion value of -6 317.10, and the prediction accuracy of 97.73%, respectively. The prediction results were better than that of area measured bya leaf area meter and predicted by whole fitting. Conclusion: The leaf area fitting model based on leaf morphology classification can accurately predict the leaf area by measuring the length and width of P. edulis leaves. In this way, it can not only measure the leaf area more accurate than leaf area meter and the whole fitting method, but also measures fast and conveniently. This model solves the difficulty in measuring the leaf area of P. edulis.

Key words: classified fitting, leaf area meter, scanning, leaf shape, length-width ratio

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