From stocks and shares to apples and pears - Norwich scientists aim to adapt financial software to help crop growth
- Credit: Anthony Cullen
There may not seem to be much in common between rustling ears of wheat and the bustling world of the stock exchange.
But now a team of scientists are learning from the financial sector as they aim to use 'big data' to improve the future of arable farming.
Earlham Institute (EI), based on the Norwich Research Park, hopes to harness high-speed, big data analytics software, perfected by technology firm Kx Systems over the past 20 years in the world's financial markets, to determine the health of crops. The project is being led by Norwich-based researcher Dr Ji Zhou, who is trying to adapt the software so that it can take in and process huge amounts of data about wheat, collected by monitoring cameras and scanners, rather than about stocks and shares. The aim is to create predictive models for studying crop growth patterns and agricultural tactics.
Tim Stitt, head of scientific computing at EI, said: 'The project is looking at what wheat looks like when it is growing healthily and what it is like when it is in an unhealthy state.
'Crop breeders are very keen to know which bits are good and which bits are bad when growing and the sooner they get that information the better. If they know they could lose a full field of wheat they can put measures in place to prevent it.'
The project, which has £25,000 funding for the fist six months, will use a form of artificial intelligence (AI) called machine learning which allows computers to teach themselves and grow as they are exposed to new data. It is hoped this will allow a program to recognise what is wrong with a crop in the early stages, giving growers the opportunity to take action quickly.
Dr Stitt added: 'For EI to remain at the forefront of bioscience research it is imperative that we continue to partner with the world's leading technology providers. I'm therefore delighted to collaborate with Kx, as we continue to explore new technologies for addressing the challenges of ever-increasing data volumes.'
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