[Background] In orchard, phenotyping the crown related characteristics (e.g. crown diameter and area) is important for monitoring the dynamic of crown growth during growing season and optimizing farming management (e.g. precision spraying and machine harvesting). However, it is lacking for methodologies to rapidly and reliably extracting the important features.[Objectives] Here we proposed an image segmentation method that integrated with UAV mapping technique and time series information for automatically measurement of the crown diameter and area of individual tree in peach orchard. [Methods] Images of 12 peach trees were collected by using Unmanned Aerial Vehicle (UAV) at five sampling times. Then 3D point cloud model and digital surface model (DSM) were generated by a commercial software. Crown characteristics were automatically calculated including two perpendicular diameters (D1 and D2) and areas (A) after recognizing individual trees, using adaptive threshold algorithm and marker-based watershed segmentation algorithm with branch-only DSM as marker. [Results] The accuracy of crown characters was evaluated through comparing with the measurement from field investigation and manual digitization. 1) Field investigation only measured perpendicular diameters. The mean absolute deviations were 0.255 (R2=0.74), 0.216 m (R 2=0.90) for D1 and D2, respectively. 2) Manual digitization scored perpendicular diameters and areas. The mean absolute deviations were 0.128 m (R2=0.96), 0.059 m (R2=0.97) and 4.107 m2 (R2=0.89) for D1, D2 and A, respectively. In 2017, the crown diameter increased from May to July and then decreased a little from July to September while area was continually increasing which indicated the solidity of crown was increasing. [Conclusions] The method developed from this study is not only valuable for farmers to dynamically monitor the growth of orchard trees, but can also could be the key technologies of precision farming.