Non-destructive estimation of winter wheat leaf moisture content using near-ground hyperspectral imaging technology

ABSTRACT

Accurate monitoring of crop moisture content is very important for irrigation scheduling and yield increase. This study aims to construct an optimal estimation model of winter wheat leaf moisture content (LMC) through spectral data processing and feature band selection. LMC and spectral reflectance were measured in 2017-2018 to construct models using simple linear regression (SLR), principal components regression (PCR), and partial least square regression (PLSR); feature bands for modelling were selected through correlation analysis and the effects of feature band number on estimation accuracy were compared. The results showed that data transformation significantly enhanced the correlation between spectral features and LMC. However, the band position corresponding to the maximum correlation coefficient for each transformation was not fixed. The accuracy of PLSR models were significantly higher than that of PCR and SLR models. The comparison of relative percent deviation (RPD) values indicated that the RPD values increased rapidly and then tended to be stable with the increase of feature band number. The R′′ -PLSR model constructed with 28 feature bands (R²_c = 0.8517; RPD > 2.0) estimated the LMC more accurately than other models. This study provides a good method for non-destructive monitoring of crop moisture content.

KEYWORDS:

• Feature band
• model
• RPD

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributors

Zhen Zhu is a postgraduate at Key Lab of Oasis Ecology Agriculture of Xinjiang Production and Construction Group, Department of Resources and Environmental Science, Shihezi University, Shihezi, Xinjiang, People’s Republic of China. He is mostly interested in monitoring and inversing of crop growth status.

Tiansheng Li is a postgraduate at Key Lab of Oasis Ecology Agriculture of Xinjiang Production and Construction Group, Department of Resources and Environmental Science, Shihezi University, Shihezi, Xinjiang, People’s Republic of China. He is mostly interested in monitoring and inversing of crop nutrition status.

Jing Cui is an associate professor in Agronomy College, Shihezi University in Xinjiang, China. She is mostly interested in soil moisture monitoring and plant water physiology.

Xiaoyan Shi is a postgraduate at Key Lab of Oasis Ecology Agriculture of Xinjiang Production and Construction Group, Department of Resources and Environmental Science, Shihezi University, Shihezi, Xinjiang, People’s Republic of China. Presently she is involved in research in monitoring and identifying saline soils.

Jianhua Chen is a postgraduate at Key Lab of Oasis Ecology Agriculture of Xinjiang Production and Construction Group, Department of Resources and Environmental Science, Shihezi University, Shihezi, Xinjiang, People’s Republic of China. Presently he is involved in research in soil salinity and science.

Haijiang Wang has obtained a Ph.D. degree in the subject of soil science. Presently he is associate professor in Agronomy College, Shihezi University in Xinjiang, China. He is mostly interested in soil salinity, soil and water safety, improvement of soil salinity, and monitoring of crop water and nutrient status.

Additional information

Funding

This research was supported by Key Program for International S&T Cooperation Projects of China [grant number 2015DFA11660] and Major Scientific Research Projects of Xinjiang Production and Construction Corps [grant number 2018AA004, 2018AA005].