Automated monitoring of health and welfare in groups of pigs using evidential reasoning and videoanalytics
The overall aim of this studentship is to use artificial intelligence (AI) to build an early warning system for pig health. To do this we will use AI to analyse a video stream of a pig pen. The AI system will monitor the social interactions between pigs within a pen as well as their individual behaviours. The AI system will build up a picture of the group behaviour within the pen over time and identify deviations from the normal pattern. The hypothesis we want to explore is that subtle changes in group behaviour can indicate future health and welfare problems. We aim to develop an early warning system to alert farmers so that they can quickly intervene at the first sign of health and welfare problems to prevent them from becoming more serious.
On a commercial scale, human observation of subtle changes in group dynamics that may indicate early-stage disease or aberrant behaviour is impractical as such observations are time-consuming and modern pig systems deal with hundreds or thousands of individuals simultaneously. Instead, we will use AI to address this problem. A video camera will monitor
the pen, and our AI computer-vision software will track the movements of each pig, recognise activities such as eating and drinking and recognise interactions between pigs. To combine all this information to give a complete picture of the
group-level behaviour we will use Evidential Reasoning Networks (ERN). These networks provide a principled way to combine multiple sources of uncertain evidence, such as the activities of multiple individual pigs, to give a coherent picture of the world. By combining evidence related to the behaviour of multiple individual pigs, together with their
interactions, we aim to achieve an understanding of the pattern of behaviours at the group level. Detecting subtle changes at the group level behaviour will form the basis for the alerts generated by our early warning system.
The following activities will be performed by the student:
1) Build on and extend existing computer-vison tools for monitoring animal behaviour. The aim will be to develop a real-time tracker and activity recognition system capable of long-term operation. The system will produce a longterm record of the location of each pig and its activities such as social interactions, play, and feeding and drinking
events. Current approaches can only be applied for limited time periods as they require costly offline non-realtime processing.
2) Given a dataset of long-term pig behaviour information, we will apply ERN to combine trajectory information, provided by the tracker, together with deep-learning-based event and activity detection methods such as animal posture, feeding and drinking behaviours and social interactions. We will design evidential networks to reason
about and combine multiple sources of low-level evidence in order to trigger alerts related to group-level behavioural changes.
3) To detect group-level interactions/events, while reducing reliance on manually labelled datasets, we will explore ways to automatically data-mine long-term behavioural datasets. To do this we will develop a statistical model
for normal pig behaviour and develop methods to detect whether the current behaviour is deviating from normal. Changes in group behaviour will then trigger alerts giving warnings of welfare events.
4) We will finally combine all our components together to develop a complete system for understanding the group behaviour of pigs. The system will be capable of continuously monitoring animal welfare and health in real time in order to trigger alerts and enable early intervention if needed. In addition to the obvious health and welfare
benefits, this system will have the potential to open a new field of research, enabling data mining to be applied to newly available large datasets of animal behaviour in ways that have not been possible before.
This is a cross-disciplinary project where the training opportunities will be adapted to the skills and expertise of the student appointed. A computer science student will be offered the chance to attend modules of the MSc course in
Animal Behaviour and Welfare (QUB), to obtain an understanding of the behaviours that underlie the two health challenges to be addressed. Alternatively, a student with a biological background will be trained on modules provided by
the MSc in Data Analytics (QUB), especially in Machine learning, Evidential Reasoning Networks and Generative adversarial networks. In both cases, the candidate will acquire a rare combination of technical, scientific and hands-on
skills that are highly valued in both industry and academia.
The student will benefit from the association through his/her supervisors with two major initiatives: Global Innovation Institute (GII, QUB) and vHive at University of Surrey, both aiming to lead the development and application of
transformational digital and data analytics tools to advance the well-being of animals and the resilience of their systems.
As a member of these initiatives, the student will have access to the latest development in these fields. The student will spend 2x3 months placements at Surrey to benefit from this association and acquire novel skills.
This project would be suitable for candidates who have an upper second-class degree in a related field (e.g., animal science, veterinary, computer science, mathematics, or physics), and interested in the application of novel Artificial
Intelligence methods to a biological problem. The ideal candidate will have strong problem-solving and ideally computer programming abilities. They will be mathematically proficient. They will have skills in reviewing literature, writing
technical documents, teamwork and time management. They will be highly motivated to learn state-of-the-art in computer vision techniques and be ready to adapt and modify these techniques to the biological context.
Alameer, A., Kyriazakis, I., Dalton, H.A., Miller, A.L. and Bacardit, J., 2020. Automatic recognition of feeding and foraging behaviour in pigs using deep learning. biosystems engineering, 197, pp.91-104.
McLaughlin, N., del Rincon, J.M. and Miller, P., 2017. Video person re-identification for wide area tracking based on recurrent neural networks. IEEE Transactions on Circuits and Systems for Video Technology, 29(9), pp.2613-2626.
Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Jose Santofimia Romero, M., Martinez del Rincon, J., & Zhou, H. (2016). Evidential event inference in transport video surveillance. Computer Vision and Image Understanding,
144 , 276–297.
HOW TO APPLY:
Please visit: https://research.reading.ac.uk/foodbiosystems/apply-for-a-foodbiosystems-phd/for-phd-students-2/
DO NOT APPLY DIRECTLY TO QUEEN'S UNIVERSITY BELFAST
Application deadline is 10.00 am GMT on Monday 30 January 2023.
FoodBioSystems DTP students receive an annual tax free stipend (salary) that is paid in instalments throughout the year.
For 2022/23 this will be £17,668 and this will increase slightly each year at rate set by UKRI.
For up to date information on funding eligibility, studentship rates and part time registration, please visit: https://research.reading.ac.uk/foodbiosystems/apply-for-a-foodbiosystems-phd/for-phd-students-2/
Computer Science overview
The School of Electronics, Electrical Engineering and Computer Science (EEECS) aims to enhance the way we use technology in communication, data science, computing systems, cyber security, power electronics, intelligent control, and many related areas.
You’ll be part of a dynamic doctoral research environment and will study alongside students from over 40 countries world wide; we supervise students undertaking research in key areas of computer science, including: computing systems, artificial intelligence and cybersecurity. As part of a lively community of over 100 full-time and part-time research students you’ll have the opportunity to develop your research potential in a vibrant research community that prioritises the cross-fertilisation of ideas and innovation in the advancement of knowledge.
Within the School we have a number of specialist research centres including a Global Research Institute, the Institute of Electronics, Communications and Information Technology (ECIT) specialising in Cyber Security, Wireless Innovation and Data Science and scalable computing.
Many PhD studentships attract scholarships and top-up supplements. PhD programmes provide our students with the opportunity to acquire an extensive training in research techniques.
Computer Science Highlights
- ECIT brings together, in one building, internationally recognised research groups specialising in key areas of advanced digital and communications technology.
- Queen’s researchers have strong links with the local industry, which boasts a rich mix of local startups and multi-nationals. Belfast is the second fastest growing region in the UK in terms of Knowledge Economy activity (Northern Ireland Economy Report, 2018).
- CSIT brings together research specialists in complementary fields such as data security, network security systems, wireless-enabled security systems, intelligent surveillance systems; and serves as the national point of reference for knowledge transfer in these areas.
World Class Facilities
- The state-of-the-art £14m Computer Science Building and the Institute of Electronics, Communications and Information Technology offer bespoke research environments.
The Institute of Electronics, Communications and Information Technology (ECIT), with state-of-the-art technology, offers a bespoke research environment.
Internationally Renowned Experts
- You will be working under the supervision of leading international academic experts.
Research students are encouraged to play a full and active role in relation to the wide range of research activities undertaken within the School and there are many resources available including:
- A wide range of personal development and specialist training courses offered through the Personal Development Programme
- Access to the Queen's University Postgraduate Researcher Development Programme
- Office accommodation with access to computing facilities and support to attend conferences for full-time PhD students
To do a PhD was one of the most challenging but rewarding decisions I have taken. While having a PhD was helpful in the job market, the real benefit was in stretching my mind and deepening my thinking. This is proving particularly useful as I head up a new local R&D team which has to stay ahead of the game by exploiting the latest research.
R&D Team Leader, Mintel
Research within the School is organised into research themes combining strengths by working together on major projects, in many cases in collaboration with key technology companies.
ECIT brings together internationally recognised research groups specialising in key areas of advanced digital and communications technology.
PhD Opportunities are available in a wide range of computer science subjects, aligned to the specific expertise of our PhD supervisors.
Queen’s is a leader in commercial impact and one of the five highest performing universities in the UK for intellectual property commercialisation. We have created over 80 spin-out companies. Three of these -
Kainos, Andor Technology and Fusion Antibodies - have been publicly listed on the London Stock Exchange.
Queen’s has strong collaborative links with industry in Northern Ireland, and internationally. It has a strong funding track record with EPSRC and the EC H2020 programme.
The research profile produced by the 2014 UK Research Excellence Framework (REF) graded 80 per cent of our research activity as 'world-leading' or 'internationally excellent', confirming the School's reputation as an internationally-leading department.
For further information on career opportunities at PhD level please contact the Faculty of Engineering and Physical Sciences Student Recruitment Team on askEPS@qub.ac.uk. Our advisors - in consultation with the School - will be happy to provide further information on your research area, possible career prospects and your research application.
People teaching you
Course structureThere is no specific course content as such. You are expected to take research training modules that are supported by the School which focus on quantitative and qualitative research methods. You are also expected to carry out your research under the guidance of your supervisor.
Over the course of study you can attend postgraduate skills training organised by the Graduate School.
You will normally register, in the first instance, as an ‘undifferentiated PhD student’ which means that you have satisfied staff that you are capable of undertaking a research degree. The decision as to whether you should undertake a PhD is delayed until you have completed ‘differentiation’.
Differentiation takes place about 8-9 months after registration for full time students and about 16-18 months for part time students: You are normally asked to submit work to a panel of up two academics and this is followed up with a formal meeting with the ‘Differentiation Panel’. The Panel then make a judgement about your capacity to continue with your study. Sometimes students are advised to revise their research objectives or to consider submitting their work for an MPhil qualification rather than a doctoral qualification.
To complete with a doctoral qualification you will be required to submit a thesis of approx 80,000 words and you will be required to attend a viva voce [oral examination] with an external and internal examiner to defend your thesis.
A PhD programme runs for 3-4 years full-time or 6-8 years part-time. Students can apply for a writing up year should it be required.
The PhD is open to both full and part time candidates and is often a useful preparation for a career within academia or consultancy.
Full time students are often attracted to research degree programmes because they offer an opportunity to pursue in some depth an area of academic interest.
The part time research degree is an exciting option for professionals already working in the education field who are seeking to extend their knowledge on an issue of professional interest. Often part time candidates choose to research an area that is related to their professional responsibilities.
If you meet the Entry Requirements, the next step is to check whether we can supervise research in your chosen area. We only take students to whom we can offer expert research supervision from one of our academic staff. Therefore, your research question needs to engage with the research interests of one of our staff.
- Assessment processes for the Research Degree differ from taught degrees. Students will be expected to present write up their work at regular intervals to their supervisor who will provide written and oral feedback; a formal assessment process takes place annually.
This Annual Progress Review requires students to present their work in writing and orally to a panel of academics from within the School. Successful completion of this process will allow students to register for the next academic year.
The final assessment of the doctoral degree is both oral and written. Students will submit their thesis to an internal and external examining team who will review the written thesis before inviting the student to orally defend their work at a Viva Voce.
- Supervisors will offer feedback on the research work at regular intervals throughout the period of registration on the degree.
Full time PhD students will have access to a shared office space and access to a desk with personal computer and internet access.
The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.
For information on international qualification equivalents, please check the specific information for your country.
English Language Requirements
Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component or equivalent qualification acceptable to the University is required (*taken within the last 2 years).
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
|Northern Ireland (NI) 1||£4,596|
|Republic of Ireland (ROI) 2||£4,596|
|England, Scotland or Wales (GB) 1||£4,596|
|EU Other 3||£23,850|
1 EU citizens in the EU Settlement Scheme, with settled or pre-settled status, are expected to be charged the NI or GB tuition fee based on where they are ordinarily resident, however this is provisional and subject to the publication of the Northern Ireland Assembly Student Fees Regulations. Students who are ROI nationals resident in GB are expected to be charged the GB fee, however this is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.
2 It is expected that EU students who are ROI nationals resident in ROI will be eligible for NI tuition fees. The tuition fee set out above is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted are for the academic year 2021-22, and relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
Computer Science costs
There are no specific additional course costs associated with this programme.
Additional course costs
Depending on the programme of study, there may also be other extra costs which are not covered by tuition fees, which students will need to consider when planning their studies . Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £100 per year for photocopying, memory sticks and printing charges. Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen. There are also additional charges for graduation ceremonies, and library fines. In undertaking a research project students may incur costs associated with transport and/or materials, and there will also be additional costs for printing and binding the thesis. There may also be individually tailored research project expenses and students should consult directly with the School for further information.
Some research programmes incur an additional annual charge on top of the tuition fees, often referred to as a bench fee. Bench fees are charged when a programme (or a specific project) incurs extra costs such as those involved with specialist laboratory or field work. If you are required to pay bench fees they will be detailed on your offer letter. If you have any questions about Bench Fees these should be raised with your School at the application stage. Please note that, if you are being funded you will need to ensure your sponsor is aware of and has agreed to fund these additional costs before accepting your place.
How do I fund my study?1.PhD Opportunities
Find PhD opportunities and funded studentships by subject area.2.Funded Doctoral Training Programmes
We offer numerous opportunities for funded doctoral study in a world-class research environment. Our centres and partnerships, aim to seek out and nurture outstanding postgraduate research students, and provide targeted training and skills development.3.PhD loans
The Government offers doctoral loans of up to £26,445 for PhDs and equivalent postgraduate research programmes for English- or Welsh-resident UK and EU students.4.International Scholarships
Information on Postgraduate Research scholarships for international students.
Funding and Scholarships
The Funding & Scholarship Finder helps prospective and current students find funding to help cover costs towards a whole range of study related expenses.
How to Apply
Find a supervisor
If you're interested in a particular project, we suggest you contact the relevant academic before you apply, to introduce yourself and ask questions.
To find a potential supervisor aligned with your area of interest, or if you are unsure of who to contact, look through the staff profiles linked here.
You might be asked to provide a short outline of your proposal to help us identify potential supervisors.