Deep Learning Approaches for Automatic Livestock Detection in UAV Imagery: State-of-the-Art and Future Directions
Muhammad Adam, Jianchao Song, Wei Yu, Qingqing Li
Abstract
Accurate livestock monitoring is critical for precision agriculture, supporting effective farm management, disease prevention, and sustainable resource allocation. Deep learning and remote sensing are recent technological advancements that have created auspicious opportunities for the development of livestock monitoring systems. This paper presents a comprehensive survey of deep learning approaches for automatic livestock detection in unmanned aerial vehicles (UAVs), highlighting key deep learning techniques, livestock detection challenges, and emerging trends. We analyze the innovations of popular deep learning models in the area of object detection, including You Look Only Once (YOLO) versions, Region-based Convolutional Neural Networks (RCNN), Anchor-based networks, and transformer models, to discuss their suitability for scalable and cost-efficient UAV-based livestock detection scenarios. To complement the survey, a case study is conducted on a custom UAV cattle dataset to benchmark representation detection models. Evaluation results demonstrate a trade-off between Precision, Recall, F1 score, IoU, mAP@50, mAP@50-95, inference speed, and model size. The case study results provide a clear understanding of selection and adapting deep learning models for UAV-based livestock monitoring and outline future directions for lightweight, domain-adapted frameworks in precision farming applications.