Tail biting in pigs is a serious and unpredictable animal welfare problem for farmers worldwide. It results in losses for farmers of £10.4M a year in the UK alone, mainly from carcass condemnation. Before damaging tail biting begins, pigs hold their tails down. This project will develop a system to detect these tail posture changes using a 3D video system giving farmers advance warning of tail biting in time to intervene. We will 1) Collect continuous 3D video from pigs at a high risk of tail biting to capture the changes in tail posture pre-tail biting, 2) Provide a detailed behaviour analysis of tail posture changes and 3) Develop software algorithms to automate this. The project partners provide expertise in pig behaviour (SRUC), a route to market and algorithms for automated 3D video analysis (Innovent Technology Ltd), pork supply chain knowledge (Sainsbury's) video expertise and access to a network of expertise in engineering and precision agriculture (Agri-EPI centre).

In the UK, dairy milk is a key part of the economy and an important source of nutrition. There are several diseases that regularly develop in UK dairy cows which compromise health and welfare, and lead to economic losses for the farmer and industry. Ill cows have also been found to contribute disproportionately to methane emissions and hence the environmental sustainability of the sector. In addition, high welfare is more important than ever to satisfy societal demands for food production. To help farmers detect and treat these diseases, numerous solutions for automated monitoring of dairy cattle are now available to farmers. A critical disadvantage of all these technologies is that they are focussed on detecting the observable symptoms of later stage disease, when treatment options may be limited, reduction of milk production persistent and animal welfare more severely compromised. A cow's response to infection and trauma is to de-prioritise behaviours not immediately essential to survival and recovery - such as social interactions - in favour of those that remain critical for longer, In a recent study we have found that social exploration, the grooming of others and receiving headbutts were lower in individuals with early stage mastitis. We hence hypothesise that social behaviour changes could be early predictors of disease. Detecting social behaviour changes is difficult for the busy farmer, but is possible by monitoring them at key focal points, such as when queueing for milking or feeding at the feed bunk, using video cameras and artificial intelligence (AI). We have developed highly robust AI that can track the motion of cows in video and recognises each individual through their distinctive coat pattern. Others have now demonstrated good classification of affiliative and agonistic social interactions from video and hence we now propose combining the two ideas to track changes in activities and social behaviours over time for each identified cow in a herd. From collecting two years of video from 64 cameras covering the main barn at our John Oldacre Centre dairy farm, we will train a model that learns what types of behaviours change over time that are indicative of different early stage diseases. We will focus on mastitis and lameness, as these diseases have the greatest incidence in our data and are the most important for the UK dairy industry. At the same time, we will sample the saliva of a subset of our herd so we can determine general levels of inflammation, enabling us to see how specific our behavioural predictors are to particular diseases. Dairy farmers are specialists in the behaviour and personalities of their cattle and their input will be vital to helping understand vagaries in farm data and how our system is functioning. We will test our system by deploying it at a network of recruited farms, and will conduct in-depth semi-structured interviews with the farmers regarding their experiences of camera placement (including intrusiveness and social acceptance by farm workers), operation and any other perceived impacts to their farms, farm workers or animal management, health and welfare. It is also critical that we design the system with all facets of industry, to engage their diverse insights and expertise in setting alert levels, designing user-friendly interfaces that will be well placed to be uptaken and discussing additional routes to market such as for disease surveillance. We have therefore assembled a consortium of partners covering all key areas from farmers to vets, the supply chain, data/diagnostic service providers and business development, all of whom we have a proven track record of successful engagement and impact with. Through consultation we will develop a sustainable strategy for meaningful lay stakeholder and public involvement with our system and results, helping to promote a widespread understanding and public/stakeholder acceptance of the system.

Our vision is to maximise the food potential of UK pasture by using targeted chemical processing and novel biotechnology to convert grass into nutritious edible fractions for healthier and more affordable alternative foods, making UK agriculture more resilient and sustainable. Our proposal aims to use novel chemical processing methods to extract the central edible fractions from grass (protein, digestible carbohydrates, vitamins, lipids, fibre) before culturing the yeast Metschnikowia pulcherrima on the cellulosic fraction to produce mycoprotein and a lipid suitable as a palm oil substitute. These ingredients will then be combined in a range of alternative meat and dairy products, displacing environmentally damaging imported ingredients currently used. Further processing of the waste products from the process will produce nutrient rich fertilizers and help create a model for future circular farming economies. When optimised this process would only need 10 to 15kg of fresh grass (20% dry matter content) to produce 1kg of edible food ingredients, of which approximately 25% would be lipid and 35% protein. Whilst not entirely comparable on a nutritional basis this represents a ten-fold increase in productivity compared to cattle raised for meat, or twice the productivity of dairy cows. By converting grass into edible food components, a number of advantages are realised including: - UK produced substitutes for palm oil, soya protein, and other imported food ingredients. This has environmental benefits in the UK and abroad. It will provide UK produced healthy nutritional substitutes for ingredients grown on former rainforest sites, whilst significantly reducing food miles; - Produce UK food substitutes for over two billion pounds worth of annual food imports, with the opportunity to export significant quantities of surplus produce; - Improved UK resilience to climate change as grass is more resilient to flooding and other extreme weather conditions than most other crops; - As the process is feedstock agnostic, it should work equally well with wildflower rich pasture grass. This potentially enables the reintroduction of grasslands with greater biodiversity without having an impact on the grasses usability, an environmentally beneficial by-product of the process; - Providing a commercially viable non-livestock based market for forage production that would also allow arable land that is prone to flooding to profitably return to meadow grass production; - The profitable inclusion of grass in arable rotations to help combat blackgrass and other pesticide resistant weeds; - At present, in some areas it is uneconomic to build and maintain livestock fencing, resulting in grassland in these regions having little commercial agricultural value. These grasslands will now become commercially viable, and contribute to UK food production; - Limited risk in scaling up as there is no need to invest in new farm machinery, existing forage equipment and storage facilities will suffice and the bio-processing technology is mature and already used for many other industrial applications; - Opportunities for investment in a new UK food industry; - With the production of more digestible fractions, this project would produce more sustainable, UK sourced, feed for monogastric livestock; - Initial research suggests that sufficient unutilised grass is available for the P2P process, therefore, this system should have little or no impact on grass supplies for dairy and livestock farming.

Robotics and Autonomous Systems (RAS) technologies are set to transform global industries. Agri-Food is the largest manufacturing sector in the UK, contributing over £38bn GVA to the UK economy and employing 420,000 people. It supports a food chain (primary farming through to retail), which generates a GVA of £108bn, with 3.9m employees in a truly international industry, with £20bn of exports in 2016. The global food chain cannot be taken for granted: it is under pressure from global population growth, climate change, political pressures affecting migration (e.g. Brexit), population drift from rural to urban regions and the demographics of an aging global population in advanced economies. In addition, jobs in the agri-food sector can be physically demanding, conducted in adverse environments and relatively unrewarding. The opportunity for RAS in Agri-Food is compelling - however, large-scale investment in basic underpinning research is required. We propose to create a CDT that focuses on advanced RAS technologies, which will advance the state of the art by creating the largest global cohort of RAS specialists and leaders focused on the Agri-Food sector. This will include 50 PhD scholarships in projects co-designed with industry to give the UK global leadership in RAS across critical and essential sectors of the world economy, expanding the UK's science and engineering base whilst driving industrial productivity and mitigating the environmental and societal impacts of the currently available solutions. In terms of wider impact, the RAS challenges that need to be overcome in the agri-food sector will have further application across multiple sectors involving field robotics and/or robotics in manufacturing. Studying robots for agriculture and food production together allows us to address fundamental challenges in RAS, while delivering whole supply chain efficiencies and synergies across both sides of the farm gate. Core research themes include autonomous mobility in challenging, often GPS-denied and unstructured environments; manipulation and soft robotics for handling delicate and unstructured food products; sensing and image interpretation in challenging agricultural and manufacturing environments; fleet management systems integrating methods for goal allocation, joint motion planning, coordination and control; and 'co-bots' for maintaining safe human-robot collaboration and interaction in farms and factories. All these themes will be applied across a range of applications in agri-food from soil preparation to selective harvesting and on-site grading, through to food processing, manufacturing and supply chain optimisation. The Centre brings together a unique collaboration of leading researchers from the Universities of Lincoln, Cambridge and East Anglia, located at the heart of the UK agri-food business, together with the Manufacturing Technology Centre, supported by leading industrial partners and stakeholders. The wide-scale engagement with industry (£3.0M committed) and end users in the CDT will enable this basic research to be pushed rapidly towards real-world applications in the agri-food industry. An ongoing training programme will take place throughout the CDT, addressing subject-specific and general scientific and technical skills, agriculture and food manufacturing, Responsible Research and Innovation, entrepreneurship, ethics, EDI, and personal and career development. The programme is supported by excellent facilities, including an agri-robotics field centre with a fleet of state-of-the-art agri-robots; a demonstration farm with arable holdings, glasshouses, polytunnels, and livestock; an experimental food factory with robots for food production and intra-logistics; multiple robotics laboratories; advanced robotic manipulators and mobile robots; advanced sensing, imaging and camera technologies; high-performance computing facilities; and excellent links to industrial facilities and test environments.

The UK has fallen significantly behind other countries when it comes to adopting robotics/automation within factories. Collaborative automation, that works directly with people, offers fantastic opportunities for strengthening UK manufacturing and rebuilding the UK economy. It will enable companies to increase productivity, to be more responsive and resilient when facing external pressures (like the Covid-19 pandemic) to protect jobs and to grow. To enable confident investment in automation, we need to overcome current fundamental barriers. Automation needs to be easier to set up and use, more capable to deal with complex tasks, more flexible in what it can do, and developed to safely and intuitively collaborate in a way that is welcomed by existing workers and wider society. To overcome these barriers, the ISCF Research Centre in Smart, Collaborative Robotics (CESCIR) has worked with industry to identify four priority areas for research: Collaboration, Autonomy, Simplicity, Acceptance. The initial programme will tackle current fundamental challenges in each of these areas and develop testbeds for demonstration of results. Over the course of the programme, CESCIR will also conduct responsive research, rapidly testing new ideas to solve real world manufacturing automation challenges. CESCIR will create a network of academia and industry, connecting stakeholders, identifying challenges/opportunities, reviewing progress and sharing results. Open access models and data will enable wider academia to further explore the latest scientific advances. Within the manufacturing industry, large enterprises will benefit as automation can be brought into traditionally manual production processes. Similarly, better accessibility and agility will allow more Small and Medium sized Enterprises (SMEs) to benefit from automation, improving their competitiveness within the global market.