Seeing Spectral Signatures

There are many application in agri-food production where it would be very useful to measure the spectrum of light reflected from a scene rather than measuring a camera RGB. Indeed spectral signatures are useful for identifying plant species, monitoring growth and to determine the presence of disease, among other tasks . Unfortunately, spectral measurement devices are expensive, cumbersome to use, and often unsuitable for deployment in the field. In contrast conventional RGB cameras are cheap and reliable and are easy to use outside the lab.

The core aim of this project is to develop algorithms for mapping the RGB signal recorded by a conventional camera to corresponding spectral signatures that are useful for solving classification problems including ripe vs non-ripe and the diagnosis of plant diseases. The research will be based on calibration, measurement and inference . That is, we will understand the physical characteristics of our device, the spectra in the world we would like to estimate and then we will develop algorithms for mapping the RGB camera measurements to spectra. Our work will also consider mapping RGB+Near Infra Read camera systems and develop algorithms for predicting spectral signature that extend to the NIR part of the spectrum. The research will be developed in close collaboration Antobot The project will dually focus on the development of robust algorithms and the engineering of practical systems that are deployed in the field.

At all stages the project will take place in collaboration with Antobot. The combintion of spectral measurement, computer vision and engineering in the context of Antobot's robotic system makes for a truly exciting collaboration that will equip the student with a unique skillset bridging traditional research led computer /color science and engineering and provide springboard for a future inter-disciplinary career in the Agriforwards area. Throughout the project the student will participate in UEA's doctoral College - including its training programme - and the cohort development activities of the Agriforwards CDT.

The UEA+Antobot team will meet regularly to review progress and plan for the near and medium term (thus providing an opportunity to identify and then manage risk). The UEA team have a long track record in helping industrial partners to better use their vision systems. Not only does this track record serve to mitigate risk but we will be able to call on our network to help advise on unforeseen problems.

The proposed CDT provides a unique vision of advanced RAS technologies embedded throughout the food supply chain, training the next generation of specialists and leaders in agri-food robotics and providing the underpinning research for the next generation of food production systems. These systems in turn will support the sustainable intensification of food production, the national agri-food industry, the environment, food quality and health.

RAS technologies are transforming global industries, creating new business opportunities and driving productivity across multiple sectors. The Agri-Food sector is the largest manufacturing sector of the UK and global economy. The UK food chain has a GVA of £108bn and employs 3.6m people. It is fundamentally challenged by global population growth, demographic changes, political pressures affecting migration and environmental impacts. In addition, agriculture has the lowest productivity of all industrial sectors (ONS, 2017). However, many RAS technologies are in their infancy - developing them within the agri-food sector will deliver impact but also provide a challenging environment that will significantly push the state of art in the underpinning RAS science. Although the opportunity for RAS is widely acknowledged, a shortage of trained engineers and specialists has limited the delivery of impact. This directly addresses this need and will produce the largest global cohort of RAS specialists in Agri-Food.

The impacts are multiple and include;

1) Impact on RAS technology. The Agri-Food sector provides an ideal test bed to develop multiple technologies that will have application in many industrial sectors and research domains. These include new approaches to autonomy and navigation in field environments; complex picking, grasping and manipulation; and novel applications of machine learning and AI in critical and essential sectors of the world economy.

2) Economic Impact. In the UK alone the Made Smarter Review (2017) estimates that automation and RAS will create £183bn of GVA over the next decade, £58bn of which from increased technology exports and reshoring of manufacturing. Expected impacts within Agri-Food are demonstrated by the £3.0M of industry support including the world largest agricultural engineering company (John Deere), the multinational Syngenta, one of the world's largest robotics manufacturers (ABB), the UK's largest farming company owned by James Dyson (one of the largest private investors in robotics), the UK's largest salads and fruit producer plus multiple SME RAS companies. These partners recognise the potential and need for RAS (see NFU and IAgrE Letters of Support).

3) Societal impact. Following the EU referendum, there is significant uncertainty that seasonal labour employed in the sector will be available going forwards, while the demographics of an aging population further limits the supply of manual labour. We see robotic automation as a means of performing onerous and difficult jobs in adverse environments, while advancing the UK skills base, enabling human jobs to move up the value chain and attracting skilled workers and graduates to Agri-Food.

4) Diversity impact. Gender under-representation is also a concern across the computer science, engineering and technology sectors, with only 15% of undergraduates being female. Through engagement with the EPSRC ASPIRE (Advanced Strategic Platform for Inclusive Research Environments) programme, AgriFoRwArdS will become an exemplar CDT with an EDI impact framework that is transferable to other CDTs.

5) Environmental Impact. The Agri-food sector uses 13% of UK carbon emissions and 70% of fresh water, while diffuse pollution from fertilisers and pesticides creates environmental damage. RAS technology, such as robotic weeders and field robots with advanced sensors, will enable a paradigm shift in precision agriculture that will sustainably intensify production while minimising environmental impacts.