As Artificial Intelligence (AI) continues to develop, so does its versatility and applicability to different fields. With a growing population, limited space, and further urbanization, the application of AI to agriculture will have a significant impact on society. However, there are unique challenges when developing an AI based system for use with living organisms—animals in particular. Animals are unique individuals with their own habits, genetics, and quirks. Without ensuring that a PMA (Precision Management of Animals) system can be applied on an individual basis, there is risk of a significant lack of broad usability in industry. The best way to address this issue is to create an accurate system for identifying each individual animal in a given facility. This project attempts to create an accurate individual identification system based on computer vision for a group of 14 gilts using a deep learning model. Video data of each gilt was collected every day from birth to finishing (6 months). Data collection focused on 3 specific views of each pig: side, top down, and face. The primary goals of this project are to create an accurate deep learning model for individual identification, identify which view produces the best accuracy, and determine the lower bound data threshold for accurate identification (p ≥ 0.98).