Use of Image Analysis in Livestock Farming

Abstract

The integration of computer vision and machine learning in livestock farming has revolutionized precision agriculture, enabling real-time monitoring, health assessment, and behavioral analysis of animals. Among the most promising tools, image-based systems, powered by deep learning architectures such as YOLO, have demonstrated remarkable performance in detecting, identifying, and tracking animals under dynamic farming conditions. Current applications include body condition assessment, disease detection, lameness monitoring, and feed efficiency optimization, providing valuable insights into animal welfare and productivity. The convergence of these visual systems with Internet of Things (IoT) devices, drones, and environmental sensors has given rise to intelligent and interconnected ecosystems of precision livestock management. Advances in edge computing and cloud computing enable real-time or near-real-time data processing, while interoperable platforms enhance decision support at different stages of production. However, challenges persist related to data quality and variability, obstacles in annotation, and the need for data science specialists with specific knowledge in the livestock domain to extract actionable insights from large volumes of information. Looking to the future, research trends point to the development of multimodal deep learning frameworks, explainable artificial intelligence models, and the integration of digital twins capable of virtually replicating farm environments. These innovations will not only increase productivity and traceability but also contribute to more ethical and environmentally responsible production practices.2.1 Introduction