[1]倪 超,张 云,高捍东.基于NIRS的马尾松苗木根部含水量预测模型[J].南京林业大学学报(自然科学版),2019,43(06):091-96.
 NI Chao,ZHANG Yun,GAO Handong.Prediction model of moisture content of masson pine seedling roots based on near infrared spectroscopy[J].Journal of Nanjing Forestry University(Natural Science Edition),2019,43(06):091-96.
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基于NIRS的马尾松苗木根部含水量预测模型
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《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

卷:
43
期数:
2019年06期
页码:
091-96
栏目:
研究论文
出版日期:
2019-11-25

文章信息/Info

Title:
Prediction model of moisture content of masson pine seedling roots based on near infrared spectroscopy
文章编号:
1000-2006(2019)06-0091-06
作者:
倪 超1张 云1高捍东2
(1.南京林业大学机械电子工程学院,江苏 南京 210037; 2.南京林业大学林学院, 国家林业和草原局南京林木种子检验中心,江苏 南京 210037)
Author(s):
NI Chao1 ZHANG Yun1 GAO Handong2
(1. College of Mechanical and Electronic Engineering,Nanjing Forestry University, Nanjing 210037, China; 2. Southern Tree Seed Inspection Center, State Forestry and Grassland Administration, College of Forestry, Nanjing Forestry University, Nanjing 210037, China)
关键词:
马尾松苗木 水分含量 近红外光谱 自动编码器 可变加权 支持向量回归
Keywords:
masson pine seedling moisture content near infrared spectroscopy autoencoder variable-wise weighted support vector regression
分类号:
S725
摘要:
【目的】马尾松是我国南方主要造林树种,其根部水分含量是评价树木活力的重要指标。本研究构建了一种基于近红外光谱(near infrared spectroscopy,NIRS)的马尾松苗木根部含水量预测模型。【方法】首先采集根部近红外光谱数据,然后利用可变加权堆叠自动编码器结合支持向量回归构建预测模型。可变加权堆叠自编码器用来逐层提取与输出相关的特征,支持向量回归根据自编码器生成的特征实现了含水量更精确预测。【结果】与其他常用模型的结果相比,提出的模型在马尾松苗木根部水分预测中可以达到最佳性能,校正集中决定系数达到0.970 8,均方根误差为0.635 8; 预测集中决定系数达到0.941 3,均方根误差为1.027 0。【结论】基于近红外光谱技术, 可变加权堆叠自动编码器与支持向量回归相结合可实现马尾松苗木根部含水量准确预测。
Abstract:
【Objective】 Masson pine is the main afforestation species in southern China. The moisture content of roots is an important indicator for evaluating the vigour of seedlings. In this study, the method for estimating moisture content of masson pine seedling roots based on near infrared spectroscopy is advanced. 【Method】 The spectrum of masson pine seedling root is obtained. The prediction model which combines variable-wise weighted stacked autoencoder with support vector regression, was proposed to estimate the moisture content in the root of masson pine seedlings. The variable-wise weighted stacked autoencoder is used to extract the features which relating to the output and the support vector regression further accurately predict the moisture contents by using the identified features. 【Result】 Compared with the other commonly used models, our model had the best performance in the root moisture prediction, with R2 value of 0.970 8 and RMSE of 0.635 8 in the training dataset and R2 value of 0.941 3 and RMSE of 1.027 0 in the validation dataset. 【Conclusion】 The combination of the variable-wise weighted stacked autoencoder and support vector regression is feasible to realize the accurate prediction of moisture content in masson pine seedlings based on near infrared spectroscopy.

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备注/Memo

备注/Memo:
收稿日期:2019-02-26 修回日期:2019-09-08 基金项目:国家自然科学基金面上项目(31570714); 江苏省高校优秀中青年教师和校长境外研修计划。 第一作者:倪超(chaoni@njfu.edu.cn),副教授,ORCID(0000-0002-3472-627X)。
更新日期/Last Update: 2019-11-30