Muhammad Muaz Abni HajarNoor Hanis Izzuddin Mat LazimAhmad Rasdan RosdiLiyana Ramli2024-05-312024-05-312021-09-09https://oarep.usim.edu.my/handle/123456789/18505Date: 9 September 2021 Platform: MSTeams, USIM Nilai, Negeri Sembilan, Malaysia.Unmanned Aerial Vehicles (UAV) have been one of the most crucial technological advances in this era. One of the sectors that have been taken over gradually by UAV is agriculture. UAV has assisted the farmer in managing farmland autonomously especially in monitoring livestock. This study proposes the cattle detection and counting system based on deep learning via UAV. This detection and counting system is done autonomously for monitoring livestock, specifically cattle. We take advantage also of the assistant of UAVs to replace the “human eye” or real-time image in monitoring the livestock. The deep learning method used in this research is the YOLOv3 and Deep SORT that have been integrated into drone real time images to capture the real-time cattle monitoring system. Keywords: UAV, YOLOv3, Deep SORT, Real-timeenUAV,YOLOv3,Deep SORT,Real-timeArticle