Unmanned Aerial Vehicle (UAV) has been widely used in Agriculture application thanks to its simplicity, cost-effectiveness, and autonomous flight capability. Ranch management involves identifying cattle and tick-carrier in an area. This project describes the possibility of applying UAV technology in ranch management and monitoring. Cattle and tick-carrier animals identification in many cases relies heavily on human eye observation. This method is usually time consuming and expensive. Applying UAV to this area could reduce the cost of human labor and increase the accuracy of the tracking process. With an RGB camera mounted to the drone, ranch managers can acquire the bird's eye view of the large area that human observation does not offer. Besides, adding a thermal camera to the drone helps managers locate the hiding wild animals and thus increases the accuracy of the cattle tracking. Having the dual camera side-byside, both thermal and standard RGB images can be obtained at the same time for the same scene. Thermal imaging cameras open the capability of spotting live animals while RGB cameras provide detailed view of the animals. By feeding the live images into an object recognition algorithm, animals in ranch can be classified and detected. This paper presents an application of dual camera set-up on an UAV with object recognition algorithm for cattle and tick-carrier identification. Post processing object classification algorithm, YOLO, is applied to the captured data to determine the animals in the scene. Preliminary results suggest that UAV equipped with dual camera and object recognition algorithm can potentially be used to help farmers identify living objects in farms. The final goal of this research is to create an autonomous vehicle, capable of automatically identifying and providing real time data of cattle and any other tick carrier animal in one area.