A Systematic Literature Review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles

With the ever-increasing importance of dairy and meat production, precision livestock farming (PLF) using advanced information technologies is emerging to improve farming production systems. The latest automation, connectivity, and artificial intelligence developments open new horizons to monitor livestock in the pasture, controlled environments, and open environments. Due to the significance of livestock detection and tracking, this systematic review extracts and summarizes the existing deep learning (DL) techniques in PLF using unmanned aerial vehicles (UAV). In the context of livestock recognition studies, UAVs are receiving growing attention due to their flexible data acquisition and operation in different conditions. This review examines the implemented DL architectures and scrutinizes the broadly exploited evaluation metrics, attributes, and databases. The classification of most UAV livestock monitoring systems using DL techniques is in three categories: detection, classification, and localization. Correspondingly, this paper discusses the future benefits and drawbacks of these DL-based PLF approaches using UAV imagery. Additionally, this paper describes alternative methods used to mitigate issues in PLF. The aim of this work is to provide insights into the most relevant studies on the development of UAV-based PLF systems focused on deep neural network-based techniques.