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Cinnamomum camphora(Linn.) Presl essential oil has great market potential in the development of forestry economy. Multi-spectral remote sensing yield prediction is a new way to efficiently invert C.camphora essential oil. The yield of essential oil in the harvest period of C.camphora was taken as the research object. Using UAV multispectral remote sensing technology, the sensitive vegetation index was selected as the input variable, and the essential oil yield of ground synchronous observation was taken as the output variable. Three machine learning methods, support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN), were used to construct the estimation model of essential oil yield of C.camphora. The results showed that modified soil adjusted vegetation index (MSAVI), optimized soil adjusted vegetation index (OSAVI), renormalized difference vegetation index (RDVI), soil adjusted vegetation index (SAVI) and nonlinear vegetation index (NLI) were highly sensitive to the essential oil yield of C.camphora, and the correlation coefficients R were 0.7651, 0.8131, 0.7711,0.7794 and 0.8183, respectively. The yield prediction models for essential oil of C.camphora were constructed by using three machine learning methods, SVM, RF, and BPNN. In the training set, the coefficients of determination R2 were 0.723, 0.853 and 0.770, respectively; the root mean square errors (RMSE) were 11.649kg/hm2, 9.179kg/hm2 and 10.484kg/hm2, respectively; the mean relative errors (MRE) were 7.204%, 10.808% and 7.181%, respectively. In the validation set, the R2 of validation set were 0.688, 0.869 and 0.732, respectively; RMSE were 7.951kg/hm2, 5.809kg/hm2, 8.483kg/hm2; MRE were 6.914%, 5.545%, 7.999%, respectively. Through the comprehensive comparison, with MSAVI, OSAVI, RDVI, SAVI, NLI as input data, the prediction model of C.camphora essential oil yield based on RF method achieved the highest accuracy. The research can provide a theoretical basis for improving the prediction accuracy of essential oil yield of C.camphora leaves based on UAV multi-spectral remote sensing and provide technical support for rapid monitoring of largearea economic plant growth.