浙江农业学报 ›› 2022, Vol. 34 ›› Issue (10): 2286-2295.DOI: 10.3969/j.issn.1004-1524.2022.10.23

• 生物系统工程 • 上一篇    下一篇

基于BiPLS-CARS-PLS的哈密瓜冠层叶片SPAD值反演建模

郭阳a(), 郭俊先a, 史勇a,*(), 李雪莲a, 黄华b   

  1. a 机电工程学院 新疆农业大学,新疆 乌鲁木齐 830052
    b 数理学院 新疆农业大学,新疆 乌鲁木齐 830052
  • 收稿日期:2021-05-25 出版日期:2022-10-25 发布日期:2022-10-26
  • 通讯作者: 史勇
  • 作者简介:*史勇,E-mail: 280974136@qq.com
    郭阳(1995—),男,辽宁沈阳人,硕士研究生,研究方向为农产品无损检测。E-mail: 2744103108@qq.com
  • 基金资助:
    新疆维吾尔自治区教育厅自然科学重点项目(XJEDU2020I009);国家自然科学基金(61367001)

SPAD inversion model of cantaloupe canopy leaf based on BiPLS-CARS-PLS

GUO Yanga(), GUO Junxiana, SHI Yonga,*(), LI Xueliana, HUANG Huab   

  1. a College of Electrical and Mechanical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    b College of Mathematics and Physics, Xinjiang Agricultural University, Urumqi 830052, China
  • Received:2021-05-25 Online:2022-10-25 Published:2022-10-26
  • Contact: SHI Yong

摘要:

利用光谱技术对大田哈密瓜冠层叶片叶绿素含量定量估测,可为田间水肥调控以及田间管理提供理论依据。本实验在剔除噪音后的378 nm到1 115 nm光谱的基础上采用多元散射校正、标准正态变量相交、标准化、Savitzky-Golay卷积平滑法、归一化、移动平均平滑等方法对原始光谱数据进行预处理,然后采用特征区间选择与特征波长选择相结合的方法实现数据降维和简化模型,并建立偏最小二乘和极限学习机的回归模型。结果表明,多元散射校正预处理效果最佳,在此基础上,利用反向区间偏最小二乘法(BiPLS)和竞争性自适应重加权采样算法(CARS)相结合共筛选出13个特征波长,将其作为模型的输入变量,由偏最小二乘法(PLS)建立的模型效果最优,其预测集的相关系数Rp和均方根误差RMSEP分别为0.942 4与1.006 2。因此,采用BiPLS与 CARS结合PLS建立的光谱定量分析模型,可实现对哈密瓜冠层叶片叶绿素含量的定量估测。

关键词: 哈密瓜冠层, SPAD值, 特征区间选择, 特征波长选择, 偏最小二乘法, 估算模型

Abstract:

The quantitative estimation of chlorophyll content in canopy leaves of Hami melon by spectral technology can provide a theoretical basis for water and fertilizer regulation and field management. On the basis of the original spectrum, this experiment used multivariate scattering correction, standard normal variable intersection, standardization, Savitzky-Golay convolution smoothing, normalization and moving average smoothing to preprocess the original spectrum data, and then combined the feature interval selection with the feature wavelength selection to achieve the purpose of data dimension reduction and model simplification. The regression models of partial least squares and extreme learning machine were established. The results showed that the preprocessing effect of multiple scattering correction was the best. On this basis, 13 characteristic wavelengths were selected by combining the inverse interval partial least squares (BiPLS) and the competitive adaptive reweighting sampling (CARS) algorithm, which were used as the input variables of the model. The model established by partial least squares (PLS) had the best effect. The correlation coefficient Rp and root mean square error (RMSEP) of the prediction set were 0.942 4 and 1.006 2, respectively. Therefore, the spectral quantitative analysis model established by BiPLS and CARS combined with PLS could realize the quantitative estimation of chlorophyll content in Hami melon canopy leaves.

Key words: canopy of Hami melon, SPAD value, feature interval selection, characteristic wavelength selection, partial least squares, estimation model

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