Estimation of apple tree canopy SPAD based on UAV multispectral remote sensing
To explore the feasibility of using UAV(unmanned aerial vehicle)multispectral remote sensing images to monitor the chlorophyll content of apple tree canopy,apple trees planted closely on low rootstocks in southern Xinjiang were taken as the research object,and UAV was used to obtain multispectral images of the experimental area.In this study,10 vegetation indices were selected and the measured canopy SPAD values of the orchard were extracted from multispectral remote sensing ima-ges for Pearson correlation analysis,and 7 vegetation indices with better correlation with SPAD were taken as the input variables of the model.The machine learning algorithms,such as univariate linear regression,partial least squares regression,support vector machine regression,random forest regression and ridge regression,were constructed.The SPAD inversion model of apple tree canopy was constructed,and the optimal model was determined by accuracy test.The results show that seven vege-tation indices ND VI,EVI,SAVI,OSAVI,GND VI,RVI,and GRVI have good correlation with SPAD,with correlation coefficients in the ranging from 0.4 to 0.7,and all of which are highly significant corre-lation at the P less than 0.01 level.The model established using random forest regression model exhibits superior performance,achieving a modeling set R2 of 0.728,an RMSE of 2.292,and an RPD of 1.920,respectively.For the validation set,the R2 is 0.702,RMSE stands at 2.527,and RPD rea-ches 1.832,respectively.Thus,the combination of UAV multispectral remote sensing and a random forest regression model enables real-time and accurate estimation monitoring of SPAD in apple tree canopies.
apple treeunmanned aerial vehiclemultispectral remote sensingSPADmachine learning
万方数据
维普
