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

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

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1054587

DOI=10.3389/fpls.2023.1054587

ISSN=1664-462X

ABSTRACT=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. To solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to invert the Sc 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 HSV 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 inversion models were constructed, which were based on single and combined variables. 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 Pn, Tr, and Sc of the leaves. 3) In the three Sc inversion models, The KNR model, which was constructed by combining image texture features and VI had the optimum inversion 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%. This study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology.