Application of Machine Learning in Drone Technology for Tracking Cattle Movement

Abstract

Background: Drones and other unmanned aerial vehicles have expanded the freedom to control and observe operations from distant areas. This paper presents a comprehensive review of the application of machine learning in drone technology for monitoring and analyzing cattle movement patterns. The traditional methods of tracking cattle movement, such as manual surveys or using satellite imagery, are time-consuming and often lack precision. With the integration of machine learning algorithms, drones offer a cost-effective and efficient solution to monitor large grazing areas accurately. This project will use algorithms to be able to test the viability and possible advantages of merging machine learning and drones for tracking cattle movement, Methods: This study makes use of a dataset of images collected from open data initiatives and crowd-sourced ground truth. Support Vector Machine (SVMs) are one of the machine learning approaches used as a classifier. The encouraging findings demonstrate that if a low precision (10 to 25%) is acceptable, true positive rates in the series of 70 to 85% are feasible. The study also covers data acquisition-related characteristics, like image resolution. Result: The integration of machine learning algorithms in drone technology for tracking cattle movement represents a promising approach to revolutionizing the livestock industry.