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

Reference	BB/M011364/1
Principal Investigator / Supervisor	Professor Ilias Kyriazakis
Co-Investigators / Co-Supervisors	Dr Jaume Bacardit, Dr Thomas Ploetz
Institution	Newcastle University
Department	Sch of Natural & Environmental Sciences
Funding type	Research
Value (£)	592,856
Status	Completed
Type	Research Grant
Start date	01/10/2014
End date	31/07/2018
Duration	46 months

Abstract

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.

Summary

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.

Impact Summary

Impact on pork industry Subclinical and clinical diseases in farm pigs contribute significantly to system inefficiency and its consequent environmental impact, especially in the system's carbon footprint. The onset of vices, such as tail biting, as well as being a significant welfare issue also results in significant losses for pig farms. The introduction of the proposed early detection system will lead to corrective action and will enhance the efficient use of resources in pig systems. The four industry partners and BPEX would be the major beneficiaries of the proposed re-search. The pig producers associated with the four companies will benefit from the devel-oped system for early health detection through increased efficiency on farm. We do not necessarily envisage that all UK pig operations will install the developed product on their farms, but expect that through licensing agreements to license the developed algorithms to other developers of similar equipment. Both Innovent and Zoetis operate internationally, either by being based in all the major pig producing countries (or having strategic alliances that enable them to do so. In addition both companies operate and have customers in emerging economies, including Brazil and China. The developed system is expected to be of relevance to all large scale operations of pig and pork producers in the EU, Americas and China. Government Agencies and Societal impact In general the outcomes of the project will be of particular relevance to policy makers, especially to those that aim to ensure the production of safe and high quality meat products, whilst having a minimum environmental impact. There is increased public interest in the safety of livestock products, including authentication and incidence of zoonotic diseases, health and welfare standards of the livestock industries and the reduction in the environmental impact that arise from livestock operations. Processors and retailers are the conduit for such concerns. Part of the project outcomes will be the efficient production of safer pork products that minimizes environmental impact, which is of interest to retailers and public alike. Defra and FSA publish reports about the incidence of such diseases and pathologies on their websites and retailers are keen to disseminate information about the safety of their products. Academic Impact Academic partners will benefit from the application of their skills to pig production systems, and the challenges this brings, and the consequent enhancement of their on-going research. Potentially the algorithms developed for the detection of health and welfare problems in pig systems could be extended to detect similar issues in other livestock. This will significantly expand the scope of the research of the academic partners. The application of the developed algorithms to health detection will be subject to communication at the later stages of the project. Exploitation and Application The product generated by the project will require some development before it is applied widely on pig farms. Plans on how to achieve this are provided in the main application. We expect licensing of the developed algorithms for disease detection to be a main source of income generation. The application of the automated monitoring system can be adopted more widely, at least for larger scale operations (such as those with more than 300 sows), and therefore to benefit the UK pig Industry as a whole. RAFT and BPEX will have a pivotal role to play in ensuring the Industry-wide adoption. In addition ZOE and Harbro expect to increase their share of the pig health and feed market, respectively, through commercialisation of the developed product, i.e., licencing the product to their exclusive customers, within 5 years from product commercialisation.

Committee	Not funded via Committee
Research Topics	Animal Health, Animal Welfare, Neuroscience and Behaviour
Research Priority	X – Research Priority information not available
Research Initiative	Agri-Tech Catalyst (ATC) [2013-2015]
Funding Scheme	X – not Funded via a specific Funding Scheme

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