Application of multi-altitude UAV multi-spectral imaging in LAI monito-ring of jujube trees at different growth stages
In order to achieve rapid estimation of leaf area index(LAI)of jujube trees,unmanned aerial vehicle(UAV)multispectral cameras were used to obtain canopy UAV images of jujube trees at three growth stages in Alar Reclama-tion Area.The LAI values of sample points were measured synchronously on the ground.A model was constructed based on 180 vegetation indices,and the tree structure Parzen estimator(TPE)in Bayesian algorithm was used to extract the optimal fea-ture combination and optimize the model parameters,so as to improve the performance of the model.The monitoring ability of models(CatBoost,RF,DNN,SVR)for jujube tree LAI values was compared and analyzed.The results showed that the TPE-CatBoost model was the best among the four models during the fruit setting period at a flight altitude of 60 meters,with a coef-ficient of determination(R2)of 0.867 5 and a mean square error(MSE)of 0.005 2,respectively.The spatial interpolation method and TPE-CatBoost model were used to analyze the LAI of jujube trees,revealing the overall trend and accurate local distribution.The TPE-CatBoost model proposed in this study can effectively monitor the LAI of ju-jube trees in reclaimed jujube orchards,providing an effective technical reference for the growth monitoring of jujube in re-claimed areas.
jujube treeleaf area indexTPE optimization algorithmCatBoostfeature optimizationmodel pa-rameter optimization
万方数据
维普
