Inversion of Photosynthetic Parameters of Ramie Leaves Based on UAV Multispectral Images
Jiao Xinwei
Yue Yunkai
Zhao Liang
Chen Jianfu
Xu Mingzhi
Liao Ao
Cui Guoxian
She Wei
Abstract：Photosynthetic parameters are important indicators reflecting the photosynthetic status of plants.In order to study the feasibility of inverting ramie(Boehmeria nivea L.)photosynthetic parameters from multispectral images captured by unmanned aerial vehicle(UAV),a multispectral UAV platform was utilized to collect the multispectral images of ramie at seedling,row closure,vigorous growth,and ripening stages un-der different nitrogen application levels and topdressing periods,and the net photosynthetic rate(Pn),stoma-tal conductance(Gs),intercellular CO2 concentration(Ci),and transpiration rate(Tr)of the ramie leaves were measured at the same time.Through correlation analysis,seven vegetation indices(NDVI,GNDVI,RVI,SIPI,WDRVI,MSR,MCARI)were selected which had higher correlation with the four photosynthetic parameters.Inversion of the photosynthetic parameters was performed using three machine learning models of Random Forest(RF),Support Vector Machine(SVM),and Back-Propagation Neural Networks(BPNN),and the model validation and accuracy comparison were also conducted.The results showed that the best mod-els for inverting ramie photosynthetic parameters of Pn,Gs,Ci and Tr from UAV multispectral images were RF models at ripening,ripening,seedling and ripening stages,respectively,with the coefficient of determination(R2)values of 0.640,0.790,0.790 and 0.720,and the root mean square error(RMSE)values of 1.040,0.070,9.190 and 1.380,respectively.Therefore,UAV multispectral images combined with RF model could effectively invert the photosynthetic parameters of ramie leaves.
Keywords：RamiePhotosynthetic parametersMultispectral images captured by unmanned aerial vehi-cleVegetation indicesMachine learning
Publication Date：2025-08-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：8( 160-167 )