Abstract: Canopy information is an important element of tea field management and an important basis for the design of related equipment. Aiming at the traditional methods of obtaining crop canopy information, which are time-consuming, subjective and prone to damage, a method of obtaining and estimating the height and outline of the tea tree canopy was proposed. Firstly, the point cloud data of the tea field was collected from multiple sites by 3D LiDAR, and the original point cloud was pre-processed with attitude correction, ROI selection, alignment, noise reduction, and elevation normalization to obtain the elevation-normalized tea tree point cloud. Secondly, the canopy height model（CHM） of tea trees was generated by inverse distance weight（IDW） and triangulation irregular network（TIN） at different spatial resolutions, among which, the CHM of tea trees generated by IDW at 0.05 m spatial resolution had better interpolation accuracy and the model produced relatively fewer pits. Finally, the raster values of CHM were extracted from 21 percentiles between 90 and 100 as the canopy height of tea trees and compared with the measured values. The results showed that the estimated value was most accurate when the percentile was 98.5, and the correlation coefficient with the true value was 0.88, with an average absolute error of 3.17 cm, and a root mean square error of 4.16 cm. In addition, totally 20 canopy section point clouds were extracted from the elevation-normalized tea tree point clouds and their outlines were fitted by elliptic, Gaussian and quadratic polynomial models, respectively. The results showed that the quadratic polynomial model could better reflect the characteristics of the tea tree canopy outline, and the mean value of the average minimum distance between the points and the fitted curves was 2.60 cm with a variance of 0.21 cm~2. The research can provide theoretical support for the modern management of tea fields and the design of related equipment.