14TSB_ATC_IR A Catalyst for Automated Capture & Analysis of Behaviour & Performance Changes in Pigs for Early Detection of Health and Welfare Problems

亚临床和临床疾病是造成养猪系统效率低下和生产力和福利降低的最大因素。目前，疾病或不良反应的检测要么通过人类观察，要么使用诊断性监测，这两种方法都有局限性，即对大量动物进行连续、高频监测所需的成本和精力。该项目旨在开发和验证创新技术，以自动监测生长猪和育肥猪系统的性能和行为，目的是自动检测农场健康和福利挑战的后果。通过自动化，我们将能够持续客观地分析动物的健康状况。与创新的分析方法相结合，这将成为早期发现健康和福利挑战后果的基础，从而为快速干预提供基础，从而提高农场的效率。我们的技术方法将包括计算机视觉和模式识别方法，用于监测室内猪圈中的猪群。我们将改进现有的视觉分析系统，以估计喂食器的活重，朝着连续和独立于位置的分析方向发展。此外，我们的视觉监控系统将跟踪猪的位置和动态运动。基于这些连续的视觉观察，我们将开发正常建模的方法，即预期的性能发展和行为，以及检测偏离这种“正常”的方法。这些分析技术将经过校准，以便从温度和湿度等环境因素中抽象出来。自动预警系统将导致：i)更早地发现健康和福利问题，从而进行有效干预，这是基于学术合作伙伴的工作，表明行为变化早在临床疾病症状出现之前就表现出来；Ii)提高效率，降低成本，尤其适用于大规模作业。因此，该项目将有助于英国养猪业的可持续性和竞争力。拟议项目的技术方法将：i)开发一个强大的、基于摄像机的监测系统，用于分析猪的行为和性能发展，该系统适用于连续监测动物群体；㈡制定算法，模拟动物个体和群体的正常行为，并允许对相关的发展标准进行定量测量，并制定自动评估方法，以发现在受控和自发的健康和福利问题期间偏离正常的情况，即预期的发展和行为；iii)在云和移动计算基础设施中实施拟议的系统，以便广泛访问并向农场人员提供近乎实时的反馈；iv)在实际部署中验证该框架，作为发现养猪场出现健康/福利问题的一种手段；(v)确保对相关利益相关者的有效KT。该项目汇集了英国领先的农业部门创新软件解决方案设计师（Innovent）、世界领先的动物保健公司（Zoetis）和两家英国领先的猪健康和管理公司（Raft和Harbro），以及一所在计算机视觉、模式识别技术和猪管理和健康研究领域处于前沿的英国大学（纽卡斯尔大学）。来自英国养猪管理局（BPEX）的额外资金将确保该项目的成果将与更广泛的英国养猪业相关并传播。

Subclinical and clinical disease is the biggest factor responsible for pig system inefficiency and reductions in productivity and welfare. Currently disease or vice detection is done either via human observation or using diagnostic surveillance, both of which have limitations w.r.t. cost and effort required for continuous, high-frequency monitoring of large numbers of animals. This project aims to develop and validate innovative technology to automatically monitor the performance and behaviour in grower and finisher pigs systems, with the objective of automatically detecting the consequences of health and welfare challenges on farm. Through automation we will enable continuous and objective analysis of animal well being. Combined with innovative analysis methods this serves as the basis for early detection of the consequences of health and welfare challenges, and thus for rapid intervention that will lead to increased efficiency on farms. Our technical approach will comprise computer vision and pattern recognition methods for monitoring groups of pigs in indoor pens. We will refine an existing visual analysis system that estimates liveweights at feeders, towards continuous, and location independent analysis. Furthermore, our visual monitoring system will track locations and dynamics of pig movements. Based on these continuous visual observations we will develop methods for modelling normal, i.e., expected performance development and behaviour, and for detecting deviations from this 'normality'. These analysis techniques will be calibrated such that they abstract from environmental factors such as temperature and humidity.An automated early warning system will lead to: i) earlier detection of health & welfare issues enabling effective intervention, which is grounded in the academic partner's work demonstrating that behaviour changes manifest long before clinical disease signs; ii) increased efficiency and reduce costs, especially for large-scale operations. The project will thus contribute towards sustainability and competitiveness of the UK pig Industry.The technical approach of the proposed project will: i) Develop a robust, camera-based monitoring system for the analysis of both behaviour and performance development in pigs that is suitable for continuous monitoring of groups of animals; ii) Develop algorithms that model normal behaviour of individuals and groups of animals, and allow for quantitative measurements of relevant development criteria and develop automatic assessment methods that detect deviations from normal, i.e., expected development and behaviour, during controlled and spontaneous health and welfare problems; iii) Implement the proposed system within a cloud- and mobile computing infrastructure for wide accessibility and near real-time feedback to farm personnel; iv) Validate the framework in realistic deployments as a means of detecting the onset of health/welfare problems on pig farms; and (v) Ensure effective KT to the relevant stakeholders. .The project brings together the UK leading designer of innovative software solutions for the agricultural sector (Innovent), the world's leading animal health company (Zoetis) and two of the UK's leading companies for pig health and management (Raft and Harbro), with a UK University that is at the forefront of research in computer vision, pattern recognition techniques and pig management and health (Newcastle University). Additional funding from the British Pig Executive (BPEX) will ensure that the outcomes of the project will be relevant and disseminated to the wider UK pig industry.