Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm

Jingang Wang; Tian Tian; Haijiang Wang; Jing Cui; Xiaoyan Shi; Jianghui Song; Tiansheng Li; Weidi Li; Mingtao Zhong; Wenxu Zhang

doi:10.3389/fpls.2023.1284172

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm

Front Plant Sci. 2023 Nov 14:14:1284172. doi: 10.3389/fpls.2023.1284172. eCollection 2023.

Authors

Jingang Wang^{1

2}, Tian Tian^{1

2}, Haijiang Wang^{1

2}, Jing Cui^{1

2}, Xiaoyan Shi^{1

2}, Jianghui Song^{1

2}, Tiansheng Li^{1

2}, Weidi Li^{1

2}, Mingtao Zhong^{1

2}, Wenxu Zhang^{1

2}

Affiliations

¹ College of Agriculture, Shihezi University, Shihezi, China.
² Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China.

Abstract

Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, blue-green (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R²c, R²p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R²c, R²p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas.

Keywords: brassic anapus; continuous wavelet transformation; partial least squares regression; photosynthesis; support vector machines.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (42161042), the Shihezi University Project (RCZK20208), Leading Talents Project of Science and Technology Innovation of Xinjiang Production and Construction Corps [2023CB008-10].