Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

Jiaxing Xie; Yufeng Chen; Zhenbang Yu; Jiaxin Wang; Gaotian Liang; Peng Gao; Daozong Sun; Weixing Wang; Zuna Shu; Dongxiao Yin; Jun Li

doi:10.3389/fpls.2023.1054587

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

Front Plant Sci. 2023 Feb 10:14:1054587. doi: 10.3389/fpls.2023.1054587. eCollection 2023.

Authors

Jiaxing Xie^{1

2}, Yufeng Chen¹, Zhenbang Yu¹, Jiaxin Wang¹, Gaotian Liang¹, Peng Gao¹, Daozong Sun^{1

2}, Weixing Wang¹, Zuna Shu¹, Dongxiao Yin³, Jun Li⁴

Affiliations

¹ College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou, China.
² Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou, China.
³ Department of Mechanical and Electrical Engineering, Luoding Polytechnic, Yunfu, China.
⁴ College of Engineering, South China Agricultural University, Guangzhou, China.

Abstract

Introduction: Canopy stomatal conductance (Sc) indicates the strength of photosynthesis and transpiration of plants. In addition, Sc is a physiological indicator that is widely employed to detect crop water stress. Unfortunately, existing methods for measuring canopy Sc are time-consuming, laborious, and poorly representative.

Methods: To solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to predict the Sc values and used citrus trees in the fruit growth period as the research object. To achieve this, VI and texture feature data of the experimental area were obtained using a multispectral camera. The H (Hue), S (Saturation) and V (Value) segmentation algorithm and the determined threshold of VI were used to obtain the canopy area images, and the accuracy of the extraction results was evaluated. Subsequently, the gray level co-occurrence matrix (GLCM) was used to calculate the eight texture features of the image, and then the full subset filter was used to obtain the sensitive image texture features and VI. Support vector regression, random forest regression, and k-nearest neighbor regression (KNR) Sc prediction models were constructed, which were based on single and combined variables.

Results: The analysis revealed the following: 1) the accuracy of the HSV segmentation algorithm was the highest, achieving more than 80%. The accuracy of the VI threshold algorithm using excess green was approximately 80%, which achieved accurate segmentation. 2) The citrus tree photosynthetic parameters were all affected by different water supply treatments. The greater the degree of water stress, the lower the net photosynthetic rate (Pn), transpiration rate (Tr), and Sc of the leaves. 3) In the three Sc prediction models, The KNR model, which was constructed by combining image texture features and VI had the optimum prediction effect (training set: R² = 0.91076, RMSE = 0.00070; validation set; R² = 0.77937, RMSE = 0.00165). Compared with the KNR model, which was only based on VI or image texture features, the R² of the validation set of the KNR model based on combined variables was improved respectively by 6.97% and 28.42%.

Discussion: This study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology. Moreover, it can be used to monitor the dynamic changes of Sc and provide a new technique for gaining a better understanding of the growth status and water stress of citrus crops.

Keywords: GLCM; VI; machine learning; stomatal conductance; threshold segmentation.

Grants and funding

This work was supported by the Guangdong Province Science and Technology Special Fund (“Major Project + Task List”) project, China (No. 2020020103). It was also partly supported by the Co-constructing Cooperative Project on Agricultural Sci-tech of New Rural Development Research Institute of South China Agricultural University (No. 2021XNYNYKJHZGJ032), the Guangdong Provincial Special Fund for Modern Agriculture Industry Technology Innovation Teams, China (No. 2022KJ108), the Laboratory of Lingnan Modern Agriculture Project (No. NT2021009), the Guangdong Province Rural Revitalization Strategy Projects (No. TS-1-4), the China Agriculture Research System of MOF and MARA, China (No. CARS-32-14), the Guangdong Science and Technology Innovation Cultivation Special Fund Project for College Students (“Climbing Program” Special Fund), China (No. pdjh2021b0077 and No. pdjh2023a0074), and the National College Students’ innovation and entrepreneurship training program(No. 202110564044).