Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia.
Faculty of Biological Sciences, The University of Leeds, Leeds LS2 9JT, UK.
Anim Health Res Rev. 2022 Jun;23(1):59-71. doi: 10.1017/S1466252321000177. Epub 2022 Jun 9.
Livestock welfare assessment helps monitor animal health status to maintain productivity, identify injuries and stress, and avoid deterioration. It has also become an important marketing strategy since it increases consumer pressure for a more humane transformation in animal treatment. Common visual welfare practices by professionals and veterinarians may be subjective and cost-prohibitive, requiring trained personnel. Recent advances in remote sensing, computer vision, and artificial intelligence (AI) have helped developing new and emerging technologies for livestock biometrics to extract key physiological parameters associated with animal welfare. This review discusses the livestock farming digital transformation by describing (i) biometric techniques for health and welfare assessment, (ii) livestock identification for traceability and (iii) machine and deep learning application in livestock to address complex problems. This review also includes a critical assessment of these topics and research done so far, proposing future steps for the deployment of AI models in commercial farms. Most studies focused on model development without applications or deployment for the industry. Furthermore, reported biometric methods, accuracy, and machine learning approaches presented some inconsistencies that hinder validation. Therefore, it is required to develop more efficient, non-contact and reliable methods based on AI to assess livestock health, welfare, and productivity.
家畜福利评估有助于监测动物健康状况，以维持生产力、识别伤害和压力，并避免恶化。由于它增加了消费者对动物待遇更人性化转变的压力，因此也成为了一个重要的营销策略。专业人员和兽医常见的视觉福利做法可能具有主观性和成本过高，需要经过培训的人员。远程感测、计算机视觉和人工智能 (AI) 的最新进展有助于开发用于家畜生物识别的新技术，以提取与动物福利相关的关键生理参数。本综述通过描述 (i) 健康和福利评估的生物识别技术、(ii) 用于可追溯性的家畜识别以及 (iii) 机器和深度学习在畜牧业中的应用，讨论了畜牧业的数字化转型，以解决复杂问题。本综述还对这些主题和迄今为止所做的研究进行了批判性评估，为在商业农场中部署 AI 模型提出了未来步骤。大多数研究都专注于模型开发，而没有针对该行业的应用或部署。此外，报告的生物识别方法、准确性和机器学习方法存在一些不一致之处，这阻碍了验证。因此，有必要基于 AI 开发更高效、非接触和可靠的方法来评估家畜的健康、福利和生产力。
