Precision livestock farming (PLF) aims to improve the health and welfare of livestock animals and farming outcomes through the use of advanced technologies. Computer vision, combined with recent advances in machine learning and deep learning artificial intelligence approaches, offers a possible solution to the PLF ideal of 24/7 livestock monitoring that helps facilitate early detection of animal health and welfare issues. However, a significant number of livestock species are raised in large outdoor habitats that pose technological challenges for computer vision approaches. This review provides a comprehensive overview of computer vision methods and open challenges in outdoor animal monitoring. We include research from both the livestock and wildlife fields in the review because of the similarities in appearance, behaviour, and habitat for many livestock and wildlife. We focus on large terrestrial mammals, such as cattle, horses, deer, goats, sheep, koalas, giraffes, and elephants. We use an image processing pipeline to frame our discussion and highlight the current capabilities and open technical challenges at each stage of the pipeline. The review found a clear trend towards the use of deep learning approaches for animal detection, counting, and multi-species classification. We discuss in detail the applicability of current vision-based methods to PLF contexts and promising directions for future research.

翻译：精准畜牧业旨在通过先进技术改善牲畜健康与福利，并提升养殖效益。计算机视觉结合机器学习与深度学习人工智能方法的最新进展，为实现全天候牲畜监测的精准畜牧业理想提供了可能解决方案，有助于及早发现动物健康与福利问题。然而，大量牲畜物种饲养在广阔的户外栖息地中，这对计算机视觉方法提出了技术挑战。本综述全面概述了户外动物监测中的计算机视觉方法及开放挑战。鉴于许多牲畜与野生动物在外观、行为和栖息地方面的相似性，本综述同时纳入了畜牧业和野生动物领域的研究。我们聚焦于大型陆生哺乳动物，如牛、马、鹿、山羊、绵羊、考拉、长颈鹿和大象。通过图像处理流程框架组织讨论，重点阐述该流程各阶段当前的技术能力与开放挑战。综述发现，深度学习在动物检测、计数和多物种分类方面的应用呈现明显趋势。我们详细探讨了当前基于视觉的方法在精准畜牧业场景中的适用性，并指出了未来研究的有前景方向。