Chlorophyll is an important physiological and biochemical indicator that reflects the growth level and health status of plants, how to obtain the chlorophyll content of citrus canopy quickly and nondestructively on a large scale which can accurately guide orchard management has become an urgent problem. A multi-rotor UAV DJI M600Pro with a multispectral sensor Sequoia manufactued by Parrot was used, which had four bands, including green, red, red edge and near infrared to acquire multi-band reflectance data, after removing the canopy shading and soil background by using normalized difference canopy shadow index, the vegetation index and texture characteristics were calculated. With the ground-truthed chlorophyll content values collected by handheld chlorophyll meter CCM-300 manufactured by OPTI-SCIENCES as validation, the inversion accuracy of full subset regression, partial least squares regression and deep neural network was compared to select the optimal model. The results showed that the correlation between vegetation index and chlorophyll content was high. Comparing the modeling results using only vegetation index with those using only texture features, the inversion accuracy of full subset regression and partial least squares regression of the model using only texture features was significantly improved and the inversion accuracy of full subset regression and partial least squares regression could be improved by introducing both vegetation index and texture features. The deep neural network which had 46 input units, 4 hidden layers and 1 output unit obtained the highest inversion accuracy with R2, MAE, and RMSE of 0.665, 7.69mg/m2, and 9.49mg/m2, respectively, due to its good nonlinear fitting ability, it was selected as the optimal model. The research used UAV multispectral images to obtain citrus canopy chlorophyll content by inversion, which was of practical significance for monitoring citrus growth status.