Investigating automatic detection of emotion in biometrically identified pig faces using machine learning
Lead Research Organisation:
Scotland's Rural College
Department Name: Research
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Technical Summary
We propose a highly novel approach of using advances in machine vision and machine learning to automatically detect and monitor key affective states in individually identified pigs and measure performance traits using only the face. The basis for this project is our successful proof-of-concept work showing pig' faces are reliable biometrics.
Our project is phased: Phase 1 involves a series of controlled experimental studies to ground-truth the technology by generating facial images of animals in negative and positive affective states. Images will optimise our existing animal identification algorithms and establish and validate specific facial characteristics relating to affective states. Phase 2 applies this knowledge in a commercial context and investigates facial correlates of animal performance (i.e. weight). Negative affect will be investigated by using established models of pain (naturally occurring lameness) and stress (social defeat). Positive affect will be induced by temporary amelioration of chronic hunger (i.e. removal of a negative state). We have shown that deep learning achieves 97% accuracy in identifying pigs using conventional 2D images. The dataset size and composition will significantly increase to establish scalability limits and quantify the benefits of adding 2.5D and 3D features for more robust biometric recognition. Changes in facial expression can be subtle so 2D, 2.5D, 3D and infrared data will all be explored for automated expression recognition. To detect deviations in individual facial expressions and recognise emotional valence different machine learning techniques will be used (x2 established feature-based approaches, x1 novel, state-of-the-art Convolutional Neural Network). Finally a refined prototype system will be tested on a large commercial farm and additional data taken to investigate facial correlates of weight. This phase determines the feasibility of our innovative, animal-centric welfare assessment tool for the end-user.
Our project is phased: Phase 1 involves a series of controlled experimental studies to ground-truth the technology by generating facial images of animals in negative and positive affective states. Images will optimise our existing animal identification algorithms and establish and validate specific facial characteristics relating to affective states. Phase 2 applies this knowledge in a commercial context and investigates facial correlates of animal performance (i.e. weight). Negative affect will be investigated by using established models of pain (naturally occurring lameness) and stress (social defeat). Positive affect will be induced by temporary amelioration of chronic hunger (i.e. removal of a negative state). We have shown that deep learning achieves 97% accuracy in identifying pigs using conventional 2D images. The dataset size and composition will significantly increase to establish scalability limits and quantify the benefits of adding 2.5D and 3D features for more robust biometric recognition. Changes in facial expression can be subtle so 2D, 2.5D, 3D and infrared data will all be explored for automated expression recognition. To detect deviations in individual facial expressions and recognise emotional valence different machine learning techniques will be used (x2 established feature-based approaches, x1 novel, state-of-the-art Convolutional Neural Network). Finally a refined prototype system will be tested on a large commercial farm and additional data taken to investigate facial correlates of weight. This phase determines the feasibility of our innovative, animal-centric welfare assessment tool for the end-user.
Planned Impact
Welfare of managed animals: Machine vision and machine learning offer potential to realise low-cost, non-intrusive and practical means to both biometrically identify individual animals and assess and record their condition daily using only the face. This would facilitate on-going learning about individuals and allows for early detection of altered health/welfare, personalised thresholds for intervention, and tailored treatment. Whilst the absence of negative affective state is and should be a priority such technology provides further opportunities to identify positive affective state thus moving closer to the ultimate goal of measuring whether animals experience "a good life".
Pig Producers, veterinarians, breeding companies: Our consortium has strong industry links, with academic partners demonstrating track records in engaging with industry to develop precision livestock technology for commercial uptake. Our industry veterinary partners say tools that can identify animals requiring attention "earlier than the most skilled stockperson" would be highly valued especially in larger operations with increased mechanisation where the ratio of pigs to stockpersons is increasing. Breeding companies are interested in technology that could help incorporate new traits into breeding programmes and select the most robust animals for clients. An ability to detect animals that might be particularly pain and/or stress susceptible at a pre-selection phase would be highly valued.
Welfare accreditation schemes, retailers, supply chain: Welfare assessment measures undertaken on farm for assurance schemes (RSPCA Assured, Red Tractor, Retailer-specific - e.g. "Taste the Difference") rely heavily on systems-based approaches by inspectors observing the farm intermittently at a group level. This project offers a highly animal-centric health and welfare assessment tool that would operate continuously to assess the affective state of individual animals on-farm.
Environmental: Benefits accrue from a more efficient, sustainable pig industry. Higher health and welfare status for breeding animals will lead to longevity in herds, will reduce environmental impact and economic losses for the industry and help improve feed conversion efficiency per kg of meat produced, boosting food security. The individualised data recording in our automated system can also be used in a wider precision farming context by association with other measurable parameters, eg individual food and water intake, treatment history, growth/weight gain, to better optimise farm production efficiency.
Society: Consumers are increasingly aware of and concerned for farm animal welfare. They have historically stimulated legislation to protect animal welfare and recent heated discussions between policy makers and citizens over animal sentience confirms the public's significant and enduring demand that welfare of farmed animals is assured by government. The potential for improved health, welfare and quality standards in the pig sector that this technology presents will lead to an enhanced public perception of industry as well as, via improved animal health and reduced pig farming costs, providing consumers with greater access to lower-cost, higher quality pork products.
Economic: The UK farm industry has a global reputation for livestock production under high welfare standards and transparency of such a reputation will enhance the integrity of the UK pig industry and provide a USP for pig meat produced in this way, giving the UK an added advantage and economic competitiveness. Such an endeavour is particularly timely as the UK agricultural sector aims to position itself in trade negotiations for a post-Brexit era. There will be more immediate economic impacts via exploitation by project commercial partners and pig farmers who will be provided with a precision tool for automatically and objectively monitoring individual pig health and wellbeing, thus improving herd efficiency.
Pig Producers, veterinarians, breeding companies: Our consortium has strong industry links, with academic partners demonstrating track records in engaging with industry to develop precision livestock technology for commercial uptake. Our industry veterinary partners say tools that can identify animals requiring attention "earlier than the most skilled stockperson" would be highly valued especially in larger operations with increased mechanisation where the ratio of pigs to stockpersons is increasing. Breeding companies are interested in technology that could help incorporate new traits into breeding programmes and select the most robust animals for clients. An ability to detect animals that might be particularly pain and/or stress susceptible at a pre-selection phase would be highly valued.
Welfare accreditation schemes, retailers, supply chain: Welfare assessment measures undertaken on farm for assurance schemes (RSPCA Assured, Red Tractor, Retailer-specific - e.g. "Taste the Difference") rely heavily on systems-based approaches by inspectors observing the farm intermittently at a group level. This project offers a highly animal-centric health and welfare assessment tool that would operate continuously to assess the affective state of individual animals on-farm.
Environmental: Benefits accrue from a more efficient, sustainable pig industry. Higher health and welfare status for breeding animals will lead to longevity in herds, will reduce environmental impact and economic losses for the industry and help improve feed conversion efficiency per kg of meat produced, boosting food security. The individualised data recording in our automated system can also be used in a wider precision farming context by association with other measurable parameters, eg individual food and water intake, treatment history, growth/weight gain, to better optimise farm production efficiency.
Society: Consumers are increasingly aware of and concerned for farm animal welfare. They have historically stimulated legislation to protect animal welfare and recent heated discussions between policy makers and citizens over animal sentience confirms the public's significant and enduring demand that welfare of farmed animals is assured by government. The potential for improved health, welfare and quality standards in the pig sector that this technology presents will lead to an enhanced public perception of industry as well as, via improved animal health and reduced pig farming costs, providing consumers with greater access to lower-cost, higher quality pork products.
Economic: The UK farm industry has a global reputation for livestock production under high welfare standards and transparency of such a reputation will enhance the integrity of the UK pig industry and provide a USP for pig meat produced in this way, giving the UK an added advantage and economic competitiveness. Such an endeavour is particularly timely as the UK agricultural sector aims to position itself in trade negotiations for a post-Brexit era. There will be more immediate economic impacts via exploitation by project commercial partners and pig farmers who will be provided with a precision tool for automatically and objectively monitoring individual pig health and wellbeing, thus improving herd efficiency.
Publications
Hansen M
(2021)
Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs
in Agriculture
Hansen M
(2018)
Towards on-farm pig face recognition using convolutional neural networks
in Computers in Industry
Description | We have trained a Convolutional Neural Network (CNN) that is able to discriminate between stressed and unstressed pigs with an accuracy of >90% in unseen animals using frontal images (i.e. facial expressions) acquired in a real-world farm setting. To the best of our knowledge this is the first time this has been achieved. This model was less successful in identifying animals in pain (via images of sows experiencing naturally occurring lameness or not) and detecting 'happiness' (via images of pigs experiencing a feeding reward) remains challenging and highlights the need for more fundamental work on detecting animal emotion. We have optimised biometric recognition by extending to over-head camera angles, as well as successfully identifying pigs over time without the need for model retraining as they age and grow. Using images from the same pigs at grower and then later at finisher stages of production, the original facial biometric work has been replicated with a new network architecture (a Siamese CNN network architecture was deployed as an alternative in order to learn embeddings rather than features) and has given a very similar accuracy (97.6%). We also successfully estimated animal weight using frontal images of pig faces using a deep learning model - another first. This was achieved on a small cohort of sows followed over time with weight data and facial images collected. A deep learning approach was able to predict even small weight changes with an error rate of only 4.2kg (or ~5%). Further work in this area is warranted as the results were highly promising. |
Exploitation Route | It could be used to develop an on-farm monitoring system for animal welfare. The commercial partner hopes to exploit the outcomes. An InnovateUK follow-on grant has been awarded to undertake a project entitled "Intellipig: An automated on-farm pig health monitoring system" (Project number: 10073790) to move this work towards a commercial application |
Sectors | Agriculture Food and Drink Digital/Communication/Information Technologies (including Software) Electronics Government Democracy and Justice Retail |
Description | BBSRC included an item our Emotional Pig project as a part of their annual Impact Showcase 2023 (BBSRC Impact Showcase 2023 "Automatic detection of stressed pigs" https://www.discover.ukri.org/bbsrc-impact-showcase-2023/). An interview for Science magazine was conducted by lead PI for our project. The article will be a feature on use of AI for interpreting animal emotion and would have impact in the wider scientific academic community given Science magazines reach. |
First Year Of Impact | 2023 |
Sector | Agriculture, Food and Drink |
Impact Types | Societal Economic |
Description | FARM interventions to Control Antimicrobial ResistancE (FARM-CARE) |
Amount | £400,453 (GBP) |
Funding ID | MR/W031264/1 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 02/2022 |
End | 02/2025 |
Description | Pig ID: developing a deep learning machine vision system to track pigs using individual biometrics |
Amount | £612,000 (GBP) |
Funding ID | BB/X001385/1 |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 02/2023 |
End | 01/2025 |
Description | the GCRF Agri-tech Catalyst Seeding Award competition. |
Amount | £142,000 (GBP) |
Funding ID | GCRF-SA-2020-UWE |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2020 |
End | 07/2021 |
Description | LINK partners |
Organisation | Agsenze Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | Ethical approval, experimental planning and data collection to provide images of animals experiencing different emotional states. These are then labelled and provided to UWE for machine vision and learning aspects of project. Press release and response to multiple press requests for interviews, site visits, filming etc. Talks to industry and internal talks for collaborative partners and their interested stakeholders. |
Collaborator Contribution | Veterinary consultation for experimental protocol for experiment one looking at lameness. Further veterinary and technical support for commercial phase. Promise of financial contribution to help "ruggidize" camera equipment for commercial data collection and some in-kind support. Farms access, equipment and travel. More specifically JSR costs include: access to multiplication and nucleus herds for extensive data collection over the course of the project; assistance with on-farm logistics and data collection from our farm managers, animal and ICT technicians. To support eventual goal of realising genetic applications we will provide a geneticist who will inform data collection and will extract data suitable for analysis. |
Impact | Multi-disciplinary: genetics, agricultural and veterinary, technical |
Start Year | 2019 |
Description | LINK partners |
Organisation | Garth Partnership |
Country | United Kingdom |
Sector | Private |
PI Contribution | Ethical approval, experimental planning and data collection to provide images of animals experiencing different emotional states. These are then labelled and provided to UWE for machine vision and learning aspects of project. Press release and response to multiple press requests for interviews, site visits, filming etc. Talks to industry and internal talks for collaborative partners and their interested stakeholders. |
Collaborator Contribution | Veterinary consultation for experimental protocol for experiment one looking at lameness. Further veterinary and technical support for commercial phase. Promise of financial contribution to help "ruggidize" camera equipment for commercial data collection and some in-kind support. Farms access, equipment and travel. More specifically JSR costs include: access to multiplication and nucleus herds for extensive data collection over the course of the project; assistance with on-farm logistics and data collection from our farm managers, animal and ICT technicians. To support eventual goal of realising genetic applications we will provide a geneticist who will inform data collection and will extract data suitable for analysis. |
Impact | Multi-disciplinary: genetics, agricultural and veterinary, technical |
Start Year | 2019 |
Description | LINK partners |
Organisation | JSR Genetics |
Country | United Kingdom |
Sector | Private |
PI Contribution | Ethical approval, experimental planning and data collection to provide images of animals experiencing different emotional states. These are then labelled and provided to UWE for machine vision and learning aspects of project. Press release and response to multiple press requests for interviews, site visits, filming etc. Talks to industry and internal talks for collaborative partners and their interested stakeholders. |
Collaborator Contribution | Veterinary consultation for experimental protocol for experiment one looking at lameness. Further veterinary and technical support for commercial phase. Promise of financial contribution to help "ruggidize" camera equipment for commercial data collection and some in-kind support. Farms access, equipment and travel. More specifically JSR costs include: access to multiplication and nucleus herds for extensive data collection over the course of the project; assistance with on-farm logistics and data collection from our farm managers, animal and ICT technicians. To support eventual goal of realising genetic applications we will provide a geneticist who will inform data collection and will extract data suitable for analysis. |
Impact | Multi-disciplinary: genetics, agricultural and veterinary, technical |
Start Year | 2019 |
Description | "Applied Machine Vision for the Real World", Invited speaker at The 38th Research and Academic Conference Research and Technology 2020. December 2020 |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Mark Hansen gave an invited talk entitled "Applied Machine Vision for the Real World" at The 38th Research and Academic Conference Research and Technology 2020. December 2020 |
Year(s) Of Engagement Activity | 2020 |
Description | "Computer vision and deep learning for on-farm welfare assessments of dairy cows and pigs", Invited speaker at The 7th International Conference on Animal Computer Interaction 2020. November 2020 |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Mark Hansen gave an invited talk on our work in "Computer vision and deep learning for on-farm welfare assessments of dairy cows and pigs", at The 7th International Conference on Animal Computer Interaction 2020. |
Year(s) Of Engagement Activity | 2020 |
URL | http://www.aciconf.org/aci2020/program |
Description | Article for Slate magazine on facial recognition technology |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Interview for social commentary web-based magazine about facial recognition technology. |
Year(s) Of Engagement Activity | 2019 |
URL | https://slate.com/technology/2019/03/facial-recognition-pigs-precision-farming-china.html |
Description | BBC News website article |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | "Facial recognition tool 'could help boost pigs' well-being" - BBC news website wrote article based on original press release and follow up interview questions. Requests for BBC film crew to attend farm visit and see technology at SRUC and UWE. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.bbc.co.uk/news/uk-scotland-edinburgh-east-fife-47614890 |
Description | BBC Scotland TV broadcast (news) |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Media (as a channel to the public) |
Results and Impact | BBC Scotland and Bristol news crews came to film at SRUC (facial recognition software and cameras set-up and pigs filmed) and UWE (facial recognition algorithms and deep learning capabilities shown and explained) respectively. Filming involved a "dummy" run of the animals walking up to the 3D cameras and talking about the project and potential impacts. Please note the project is non-invasive and pigs were simply walked up to a 3D camera and given a food reward. This was done for the purposes of filming and not for data collection. Piece was broadcast in BBC evening news slot (1800 and 2200) and was posted on the BBC website (see link). The intended purposes was to respond to multiple media interest and talk about what we plan to do and the wider applications of the technology for animal welfare. The outcomes involved wider conversations about monitoring animal welfare on-farm and requests for further information and revisits once project complete. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.bbc.co.uk/news/science-environment-49376506 |
Description | BBC4 Farming Today radio interview |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Radio Interview about BB/S002138/1 Investigating automatic detection of emotion in bio-metrically identified pig faces using machine learning. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.bbc.co.uk/programmes/m0003cx9 |
Description | Demonstration of facial recognition and facial expression work to MSPs |
Form Of Engagement Activity | Participation in an open day or visit at my research institution |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Policymakers/politicians |
Results and Impact | Tour of pig unit and display of research capabilities and image capture for BBSRC project |
Year(s) Of Engagement Activity | 2019 |
Description | Filming for Netflix series on big data |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Netflix series producers requested interviews and filming in relation to project as part of an intended big data series looking at positive applications of surveillance technology. Outcomes: wide and diverse audience for outreach of positive collaboration between technology and agriculture to tackle animal welfare issues. Highlights ground-breaking research by both UK academic institutes in collaboration with industry and supported by Research Councils. |
Year(s) Of Engagement Activity | 2019 |
Description | Interview and photo shoot with National Geographic magazine |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Interview with National Geographic journalist as part of Animal Minds extended magazine piece (October 2022) and a photo shoot at our pig research facility photographing one of the experimental set-ups to detect facial expressions of emotion in pigs. |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.nationalgeographic.com/magazine/article/what-are-animals-thinking-feature |
Description | Interview and site visit for Netflix series CONNECTED |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Netflix filmmakers developing a documentary series (Connected - the Hidden Science of Everything) attended the SRUC research farm to film interviews with PIs (Baxter and Smith) and mock data collection for BBSRC project investigating automatic detection of emotion in pig facial expression and on our work looking at individual facial recognition in pigs. Once the series started streaming (August 2nd 2020) there was international interest in the project outcomes. |
Year(s) Of Engagement Activity | 2020 |
URL | https://www.youtube.com/watch?v=B-aZrftUPlk |
Description | Interview for Swedish agriculture publication - Landlantbruk |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Interview for Swedish agriculture magazine after picking up press release |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.landlantbruk.se/lantbruk/koll-pa-grisarnas-kanslor-med-hjalp-av-3d-teknik/ |
Description | Interview with New Scientist |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Gave an interview to a science journalist specialising in animal health and behaviour, Christa Lesté-Lasserre, from which an article appeared on 20 September 2022 in New Scientist, entitled "Face recognition technology for pigs could improve welfare on farms". |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.newscientist.com/article/2338447-face-recognition-technology-for-pigs-could-improve-welf... |
Description | Interview with Science magazine conducted by lead-PI |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | An interview for Science magazine was conducted by lead PI for the project. The article will be a feature on use of AI for interpreting animal emotion and would have impact in the wider scientific academic community given Science magazines reach |
Year(s) Of Engagement Activity | 2024 |
URL | https://www.science.org/ |
Description | Invited presentation at Swedish Veterinary Conference on PLF and ethics |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation at the Swedish Veterinary Conference 2021 who invited me to talk about PLF opportunities and challenges for animal welfare. The presentation and seminar was online and was made live as well as kept on conference website to allow later viewing. Difficult to know how many participants. |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.svf.se/veterinarmedicin/veterinarkongressen/kongressen-2021/ |
Description | Items appeared on our work with pigs and dairy cattle as part of the' future of farming' on BBC Six and Ten o'clock news, digital (online video) and Newsround. |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | Television interviews recorded on both our work in pig face and emotion recognition, jointly with SRUC, and also our on-going work in condition monitoring of dairy cattle. Follow-up interest occurred both from media organisations and others (academic and commercial) interested in potential collaboration. Other media interest included: Item recorded for Netflix series "Connected" in September 2019 to be broadcast summer 2020. Daily Mail: https://www.dailymail.co.uk/wires/pa/article-6821859/Facial-recognition-technology-used-discover-emotional-state-pigs.html New Food Magazine: https://www.newfoodmagazine.com/article/92909/researchers-develop-machine-vision-technology-to-detect-a-pigs-emotional-state/ Imaging and Machine Vision Europe: https://www.imveurope.com/news/pig-expressions-studied-face-recognition SPIE - Optics - https://optics.org/news/10/3/35 |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.bbc.co.uk/news/av/science-environment-49362428/pigs-emotions-could-be-read-by-new-farmin... |
Description | KTN Case study: How happy is your pig? |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Melvyn Smith gave an interview to KTN for a case study and an item to appear in their newsletter. The article was also blogged by UWE. |
Year(s) Of Engagement Activity | 2020 |
URL | http://blogs.uwe.ac.uk/research-business-innovation/case-study-how-happy-is-your-pig/ |
Description | Magazine article for CountryLife |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | Magazine article following requests for interview after initial press release. Outcome = highlights research activity to general public |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.countrylife.co.uk/news/happy-pig-mud-feeling-sow-er-new-technology-read-animals-emotions... |
Description | Magazine article on project for Farming UK |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Article based on initial press release highlighting project intentions and reasons for trial. Outcome = advertising work in national farming press. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.farminguk.com/news/3d-tech-which-detects-emotional-state-of-pigs-could-benefit-farmers_5... |
Description | News article for The Metro |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Metro article "Pigs have feelings too? scientists used facial recognition to understand them" picking up on initial press release. Generated more interest and requests for interviews and more information. |
Year(s) Of Engagement Activity | 2019 |
URL | https://metro.co.uk/2019/03/19/pigs-feelings-scientists-used-facial-recognition-understand-8936163/ |
Description | Newspaper and web article for Scottish Agricultural Press |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Industry/Business |
Results and Impact | Article based on initial press release. Outcome = Dissemination of project intentions to Scottish farming industry. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.thescottishfarmer.co.uk/opinion/17597050.finding-out-if-your-pig-is-happy/ |
Description | Newspaper and web-based article for The Times |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Newspaper and web-based article on project based on initial press release and follow up interview. Outcomes include outreach to non-scientific audience and requests for more information. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.thetimes.co.uk/article/mood-recognition-technology-will-see-whether-pigs-really-are-happ... |
Description | Newspaper press article on 3D tech and pig welfare - The Daily Mail |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Newspaper article based on original press release with additional material as a result of request for interview. Outcomes include outreach to audience not typically reached. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.dailymail.co.uk/wires/pa/article-6821859/Facial-recognition-technology-used-discover-emo... |
Description | Plenary talk at BSAS on technology and animal welfare |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Invited presentation at British Society for Animal Science's 75th Anniversary Congress. Talk in Research into Practice and use of technology in animal welfare. Outcomes = demonstrate potential for cross-collaboration and multi-disciplinary approach to tackle long-standing challenges in animal production. |
Year(s) Of Engagement Activity | 2019 |
Description | Presentation at "The Bristol AI and Nature Week" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Postgraduate students |
Results and Impact | Talk given as part of 'The Bristol AI and Nature Week' at University of Bristol entitled "Early detection of stress in pig faces using machine vision to reduce anti-microbial use for diseases". |
Year(s) Of Engagement Activity | 2023 |
URL | https://camtrapai.github.io/ai_nature_week.html |
Description | Presentation at AWRN PLF workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Invited presentation on positive animal welfare and precision livestock farming at AWRN's virtual workshop/seminar on PLF concepts and developments (100 attendees on the day and recordings open to AWRN members thereafter). Talk was mainly aimed at the academic community and sparked much debate about the opportunities and risks of PLF in animal welfare science and the overall ethics of their use. |
Year(s) Of Engagement Activity | 2020 |
URL | https://awrn.co.uk/event/awrn-funded-workshop-on-current-developments-in-precision-livestock-farming... |
Description | Presentation at New Scientist Live event |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Virtual presentation given as part of "New Scientist Live's Future of Food and Agriculture". Aimed at inspiring children and young adults into a science career by demonstrating applications of technology in agriculture to improve animal welfare. |
Year(s) Of Engagement Activity | 2020 |
URL | https://www.newscientist.com/science-events/future-food-agriculture/ |
Description | Presentation to MSP |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Policymakers/politicians |
Results and Impact | Presentation to MSP on facial recognition and detecting emotional expression using machine vision approaches |
Year(s) Of Engagement Activity | 2019 |
Description | Press Release |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Joint press release by both academic institutions to advertise the project and its intended outcomes. Expected to be picked up by various media outlets to reach multiple and diverse audiences. There was unprecedented interest in the press release leading to over 200 "pick-ups" by media outlets and multiple requests for interviews by radio, written press, web-based and television. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.sruc.ac.uk/news/article/2352/facial_recognition_technology_aims_to_detect_emotional_stat... |
Description | Press interview in agricultural press (Press and Journal) |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Press article by agricultural press based on initial press release. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.pressandjournal.co.uk/fp/business/farming/1703832/trials-use-facial-recognition-to-test-... |
Description | TV feature with Hannah Fry for series Bloomberg |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Showcased the research on machine learning to detect facial expression of emotion in pigs and general discussion about measuring emotion in animals and its importance |
Year(s) Of Engagement Activity | 2023 |
URL | https://www.bloomberg.com/originals/series/the-future-hannah-fry |
Description | The Spie - Article on 3D technology in technology-based journal |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Magazine article based on initial press release. Main outcome is interest in animal welfare in a predominantly machine vision audience. |
Year(s) Of Engagement Activity | 2019 |
URL | https://spie.org/news/facial-recognition-spots-happy-pigs?SSO=1 |
Description | Virtual presentation to UK-RAS Strategic Task group in Agri-Robotics |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Melvyn Smith to give a virtual presentation to UK-RAS Strategic Task group in Agri-Robotics that included our work on automated weed detection, cattle condition monitoring and pig face recognition and expression detection on 29th Sept. |
Year(s) Of Engagement Activity | 2020 |
Description | Web article for Pig Progress |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Web article on project following up from initial press release and interviews for further information. Large international pig audience reached. Outcomes include interest within the pig sector and links to proof-of-concept work on facial recognition and on facial expression. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.pigprogress.net/Sows/Articles/2019/3/Facial-recognition-for-detecting-pig-emotions-40644... |
Description | Web article in Pig World |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Web article based on initial press release. Outcomes = highlights project intentions and brings awareness to potential to detect emotional state in pigs. |
Year(s) Of Engagement Activity | 2019 |
URL | http://www.pig-world.co.uk/news/facial-recognition-technology-aims-to-detect-emotional-state-in-pigs... |
Description | Web article on project and interview published in Global Meat News |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Interview for Global Meat News based on initial press release. Outcomes include highlighting animal welfare monitoring globally and the importance of welfare indicators of positive and negative emotional states. Highlighting tech capabilities. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.globalmeatnews.com/Article/2019/04/08/Pig-facial-recognition-technology?utm_source=copyr... |
Description | Web article on project for IMV Europe |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Article based on original press release and follow up interview requests. Outcomes = highlights animal welfare to a tech audience and the potential of technology to enhance animal well-being. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.imveurope.com/news/pig-expressions-studied-face-recognition |
Description | Web article on project for TechRadar |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Article written based on original press release. Aimed at a tech audience. Outcome = highlights animal welfare issues to a non-agricultural audience |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.techregister.co.uk/scientists-used-facial-recognition-technology-to-find-pigs-feelings/ |
Description | White paper - Agricultural Robotics: The Future of Robotic Agriculture |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | UK-RAS Network White Papers, ISSN 2398-4414 |
Year(s) Of Engagement Activity | 2018 |
URL | https://arxiv.org/abs/1806.06762 |