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

14TSB_ATC_IR 自动捕获的催化剂

• 批准号: BB/M011364/1
• 负责人: Ilias Kyriazakis
• 金额: $ 75.54万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM011364%2F1
• 关键词: 14TSB_ATC_IR; Catalyst; Automated; Capture; Analysis

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.

亚临床和临床疾病是导致猪系统效率低下以及生产力和福利下降的最大因素。目前，疾病或恶习检测是通过人类观察或使用诊断监测来完成的，这两种方法都有局限性。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，目标是自动检测农场健康和福利挑战的后果。通过自动化，我们将能够对动物福祉进行持续、客观的分析。与创新的分析方法相结合，这可以作为早期发现健康和福利挑战后果的基础，从而进行快速干预，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，用于监测室内猪栏内的猪群。我们将改进现有的视觉分析系统，该系统可估计喂食器的活重，以实现连续且与位置无关的分析。此外，我们的视觉监控系统将跟踪猪的移动位置和动态。基于这些连续的视觉观察，我们将开发对正常（即预期的表现发展和行为）进行建模的方法，以及检测与这种“正常”的偏差的方法。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）更早发现健康和福利问题，从而实现有效干预，这是基于学术合作伙伴的工作，证明行为变化早在临床疾病迹象之前就显现出来； ii) 提高效率并降低成本，特别是对于大规模运营。因此，该项目将为英国养猪业的可持续性和竞争力做出贡献。拟议项目的技术方法将： i) 开发一个强大的、基于摄像头的监测系统，用于分析猪的行为和性能发展，适合对动物群体进行持续监测； ii) 开发模拟个体和动物群体正常行为的算法，并允许对相关发育标准进行定量测量，并开发自动评估方法，以检测在受控和自发的健康和福利问题期间与正常情况（即预期的发育和行为）的偏差； iii) 在云和移动计算基础设施内实施拟议的系统，以实现广泛的可访问性和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，作为检测养猪场健康/福利问题发生的一种手段； (v) 确保向相关利益相关者提供有效的知识技术。该项目汇集了英国领先的农业创新软件解决方案设计公司（Innovent）、世界领先的动物保健公司（Zoetis）和英国两家领先的生猪健康和管理公司（Raft和Harbro），以及一所在计算机视觉、模式识别技术和生猪管理与健康研究领域处于前沿的英国大学（纽卡斯尔大学）。英国养猪局 (BPEX) 提供的额外资金将确保该项目的成果与更广泛的英国养猪业相关并得到传播。

项目成果

Toward the automated detection of behavioral changes associated with the post-weaning transition in pigs.

• DOI: 10.3389/fvets.2022.1087570
• 发表时间: 2022
• 期刊: Frontiers in veterinary science
• 影响因子: 3.2

Automated tracking to measure behavioural changes in pigs for health and welfare monitoring.

• DOI: 10.1038/s41598-017-17451-6
• 发表时间: 2017-12-14
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Matthews SG;Miller AL;PlÖtz T;Kyriazakis I
• 通讯作者: Kyriazakis I

How many pigs within a group need to be sick to lead to a diagnostic change in the group's behavior?1.

一组内需要有多少头猪生病才能导致该组行为发​​生诊断性变化？1。

• DOI: 10.1093/jas/skz083
• 发表时间: 2019
• 期刊: Journal of animal science
• 影响因子: 3.3
• 作者: Miller AL

Early detection of health and welfare compromises through automated detection of behavioural changes in pigs.

通过自动检测猪行为变化的自动检测，对健康和福利的早期发现妥协。

• DOI: 10.1016/j.tvjl.2016.09.005
• 发表时间: 2016-11
• 期刊: VETERINARY JOURNAL
• 影响因子: 2.2
• 作者: Matthews, Stephen G.;Miller, Amy L.;Clapp, James;Plotz, Thomas;Kyriazakis, Ilias
• 通讯作者: Kyriazakis, Ilias

Automatic recognition of feeding and foraging behaviour in pigs using deep learning

• DOI: 10.1016/j.biosystemseng.2020.06.013
• 发表时间: 2020-09-01
• 期刊: BIOSYSTEMS ENGINEERING
• 影响因子: 5.1
• 作者: Alameer, Ali;Kyriazakis, Ilias;Bacardit, Jaume
• 通讯作者: Bacardit, Jaume