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The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. Due to the low spatial resolution of satellite remote sensing images, it is difficult to identify cultivated land of small areal extent in critical regions; this requires image data of high spatial resolution for specific or general cases. Simultaneously, unmanned aerial vehicle (UAV) has been increasingly used for natural resource applications in recent years as a result of its greater availability, the miniaturization of sensors, and the ability to deploy UAV relatively quickly and repeatedly at low altitudes. In order to solve the problem of large quantity, distortion and accumulated error in the process of UAV image mosaic, how to reduce accumulated error efficiently was researched. First of all, matching area was calculated according to the record center points in process of matching. Then error equation was listed based on the results of regional network summary calculation. Next, images were given weight value by different terrain features areas to conduct area weighted adjustment. Finally, mosaic experiments were completed by the proposed method and direct mosaic method based on three flight strips UAV images. The experimental results show that the ghost and dislocation phenomenon was decreased by 12%, mosaic efficiency was increased by 15%, and the area after mosaic was expanded by 8%. The method can mosaic UAV images better at error control and efficiency.