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Measurements and analysis of water content in winter wheat leaf based on terahertz spectroscopy

文献类型： 外文期刊

作者： Li, Bin 1 ; Long, Yuan 1 ; Yang, Hao 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

3.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

4.Minist Agr, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China

关键词： terahertz spectroscopy; winter wheat; gravimetric water content (GWC); partial least squares method

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.032； 五年影响因子：2.137 ）

ISSN： 1934-6344

年卷期： 2018 年 11 卷 3 期

页码：

收录情况： SCI

摘要： Wheat is a major grain crop in China. Water is one of the most important factors which influence the lifecycle and yield of wheat. It is of great significance to study the water content at key stages of wheat growth in order to make irrigation decision to raise its yield. As terahertz (THz) spectroscopy is a brand new sensing technology and sensitive to water absorption, the relationship between terahertz spectra and water content in winter wheat leaf was investigated and a preliminary result was presented in this paper. Forty winter wheat leaves samples with diverse range of water content (42.8%-72.5%) were collected. The Terahertz time domain spectra (THz-TDS) were first obtained and then transformed into Frequency-domain amplitude with the Fast Fourier Transformation (FFT) method. The absorption and refractive index spectra were then calculated. The spectra were linearly fitted to get the slope and intercept used for building a calibration model. The partial least squares (PLS) method and linear regression were employed to establish models to determine leaf water content in the winter wheat. The predicted correlation coefficient and the root mean square error of the optimal model established with the Frequency-domain amplitude parameter at 0.3 THz by linear regression were 0.812% and 4.4%, respectively. The results showed that terahertz spectroscopy performed well in water content prediction and could be an effective and potential method for leaf water content measurement in winter wheat.

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