The colours, or RGB pixels, recorded by a digital camera are the result of the interaction of the prevailing light in the scene striking and being reflected by objects and the characteristics of the camera itself. The complexity is such that different cameras see differently and no cameras see the world exactly as we do. You will have noticed this when looking at photos where sometimes the colours don't look right or the pictures captured by one camera look 'better' than another. Moreover, sometimes we see colours change dramatically. We have all probably observed that white clothes can look bluish under ultra violet light (say in a night club). But, in fact the colours we see change subtly, all the time, as we move from one light to another (which is why it is always a good idea to check the colour of your clothes outside the shop). Here, even small changes can lead to poor customer satisfaction or, potentially, in a medical imaging application the wrong diagnosis. Good pictures, by which we might mean accurate 'colour measurement' are possible if we know the spectral colour characteristics of a camera and/or the spectrum of light in a scene. While we can, in principle, measure these quantities the measurement is not easy to do so and is expensive (not easy as it requires considerable (Physics) lab time and expensive because spectral measurement devices cost many thousands of pounds). When measurement is not feasible, there do in fact exist methods for estimating (say) the spectrum of light in a scene. Yet, these methods only tend work if the camera is accurately calibrated first (a sort of chicken and the egg situation). Our 'Rank Based Spectral Estimation' Project aims to make it much easier to calibrate a camera or measure the illuminant in situ (and as such also make it easier to measure reflectance too) So, how does our method work. Well suppose we gave you 50 grey tiles all of which appeared to have a different brightness. It would be an easy task for you to rank them from darkest to brightest. But, now suppose we change the colour of the light. Depending on the spectral shape of the grey reflectances, the ranking order can change (sometimes considerably). No problem, it is a simple matter to reorder the tiles. Remarkably, for specially chosen reflectances, the rank order will strongly correlate with the spectral shape of the light. Thus a simple ranking experiment gives us a strong clue to the colour of the light. (And, if we knew the colour of the light we could, for example predict whether the colour of our clothes might change when we go outdoors.) The Rank Based Spectral Estimation project aims to take this simple ranking idea and provide simple, and accurate, estimation tools for deriving the spectral shape of the prevailing light, the spectral characteristics of a camera and the spectral reflectances of surfaces. At the heart of our method is a specially designed reflectance target containing many reflectances (whose design is part of the proposed research). Ranking these reflectances will allow us to accurately estimate the light spectrum and the spectral attributes of a camera. Accurate spectral estimates are required in many applications from photography, through, visual inspection to forensic imaging and telepresence (e.g. remote diagnosis). Remarkably, we believe the methods we develop will also prove useful in understanding how we see. Indeed, it is very likely that you see the world a little differently than I do. Yet estimating an individual's spectral response is notoriously difficult. To the extent it can be done at all, it requires many hours of (tedious) detailed visual experiments. Through ranking it will be possible to uncover an observers spectral response (technically called 'colour matching curves') quickly and simply. We simply ask the observer to carry out a simple ranking of the kind mentioned above.

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.

This project will unlock the potential of wheat grain heterogeneity. We will: 1) Develop a novel single seed phenotyping tool based on hyperspectral imaging technology (HSI) integrated with next generation machine learning 2) Explain the determinism of the variance of uniformity of single seed grain quality parameters and explore a broad range of both known, and novel and exotic wheat genotypes for previously undefinable unique single seed traits, this will allow breeders to target previously unavailable grain quality uniformity traits, as well as speed selection from segregating populations. 3) Deploy the single grain HSI technology as a novel molecular breeding tool by determining key genes controlling single grain quality uniformity traits and validating the candidate genes by developing lines with contrasting expressing of the novel genes which we will test in field experiments. 4) Demonstrate the application of the single seed phenotyping tool as a sorting technology at laboratory and pilot production scale for wheat. This will demonstrate the ultimate value of the approach by producing exemplar food products (bread, biscuit and malted wheat) with enhanced quality and health credentials and validating the findings through sensory and consumer insight testing. Ultimately this project offers the potential for breeders to significantly upgrade the UK wheat grain production, reduce the requirements to use imported wheat of millers, and enhance the nutritional quality and sensory quality traits of bread, biscuits and food products containing malted wheat for the consumer. The impact of this project will be very significant as sorting by hyperspectral classification for protein content would allow tighter segregation of the wheat supply chain into defined applications such as those that require lower protein (cakes, biscuits, pastry) from those that require higher protein with good protein quality and consistency and resulting good rheology (bread, pasta, high protein flour) and allow tighter adherence to supplier specifications in addition to reducing the need of imported wheat. At the highest capacities, a single sorting machine can process around 0.5 million tons per year, this indicates a very significant impact on the UK wheat industry with a relatively low-cost intervention, often in centralised milling sites. Furthermore, premium wheat with unique bread-making properties (e.g. elevated micronutrients, very high protein) and unique flavour potential through the malting process, will be sold with a price premium. If a further 20% of UK farmers growing bread-making wheat varieties were to achieve the grain protein market specification of 13% for the premium each year, it would be worth an extra £25 M per year to the UK agriculture sector.

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.