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Among the most effective ways for businesses to benefit from our world-leading academic expertise are to partner with us in collaborative research and to licence our technologies.
To see how research partnerships are helping to meet industry partners' challenges, you can browse the case studies hosted here. To discuss what sort of collaboration or licensing opportunities would best meet your needs, please contact a member of the industrial liason team.
Precision Agriculture
Introduction to the purpose of the research
One aspect of the DCE programme’s Grand Challenge of ‘Resilient and robust infrastructure’ is to develop adaptive machine learning techniques for changing environments. This area of work seeks to combine reliable sensing technologies with online data analytics to provide continuous learning, prediction and control in changing environments to guide the design, construction and maintenance of critical infrastructures. The development of such end-to-end sensor systems and algorithms for measuring physical infrastructure has applications in a wide range of real-world situations, including precision agriculture.
What is Precision Agriculture, and how does it serve as a solution?
The ultimate goal of precision agriculture is to optimise the sustainability and quality of its products and processes. It requires that farmers use sensor networks to become intimately acquainted with the state of their crops and soil at any given time and location in their fields. By understanding what is happening in sufficient detail, farmers can tailor inputs such as fertiliser and water, and disease outbreaks quickly identified. In this way, inputs are optimised, crop yields are maximised, and through more judicious use of pesticides, harm to the soil and biodiversity are minimised. Professor McCann, Dr. Po-Yu Chen and the team have designed a unique system able to transform a passive sensing network into a control network – a crucial innovation for precision farming.
How did the project start and what did the researchers do?
To develop and demonstrate such a system on a smaller scale, Professor McCann and Dr. Chen approached Ridgeview, a vineyard based in the South Downs in Sussex, with a proposal to develop an end-to-end monitoring system of their vineyards. Building on a previous EPSRC-funded project with Ridgeview to monitor energy, temperature and water use within the winery building, the vineyard provided a small-sized, manageable system where the researchers could develop this technology further. The researchers placed sensors every 50 metres throughout the wine estate and within the winery building. This set-up makes it possible to measure how the air around the vineyard changes in terms of humidity and temperature, and to measure soil moisture content at both the micro and macro levels across the fields of vines as well as water and energy use across the areas.
What happened?
The initial goal was to model, in as close to real-time as possible, the state of the fields. These data are then fed into a life-cycle analysis, delivered in collaboration with researchers from the University of Exeter. This work could eventually allow Ridgeview to understand small changes in different vineyard areas, helping them identify potential disease hotspots and understand localised conditions or trends in microclimates across the vineyard. Not all the work on the project can take place in a working vineyard, so much of the project is being done in the laboratory. A research challenge is how to make sense of data trickling in from potentially hundreds of sensors, at different times. The trick is to process the data so that they remain robust, even if parts of the network degrade – as they always will “in the wild” – so an adequate prediction can be made of the current state of the field.
Future plans for the project
The development of sensor networks to read the state of a farm at high resolution will facilitate sustainable practices. However, for agriculture more broadly, the sensor technology is only half of the challenge. The other half is to exploit the information gathered by a sensor network when making practical decisions, such as using actuators to add fertiliser or water in the appropriate amounts and locations. The longer-term aims of the work would be to create a farm that can not only feel itself but also feed itself.
Improved understanding and reliability of such systems increase their potential for scaling in applications beyond agriculture and farming to systems that are more complex and more critical, such as water networks. With the successful application of an end-to-end sensor and control network, water pipe networks could be mostly self-regulating, improving resilience and their ability to mitigate failures and leaks and be better matched to demand.
Related people
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Data-Centric Engineering Strategic Leader |
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Co-Organiser of Sustainable Infrastructure Project |
Funders
Collaborators
Introduction to the purpose of the research
What is Graphics Fuzz?
From freeing up time for busy households to assisting the elderly to lead an independent life, the home robotics market is expected to triple in size in the next five years, and the Dyson Robotics Lab is developing the technology that will enable home robotics to perform intelligent tasks without human intervention. The Dyson Robotics Lab (DRL) at Imperial College London was founded in 2014 as a collaboration between Dyson Technology Ltd and Imperial College. It is the culmination of a thirteen-year partnership between Professor Andrew Davison and Dyson to bring his Simultaneous Localisation and Mapping (SLAM) algorithms out of the laboratory and into commercial robots, resulting in Dyson’s 360 Eye vision-based vacuum cleaning robot in 2015 which can map its surroundings, localise and plan a systematic cleaning pattern.
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