Machine-Cast: A scalable machine learning framework for forecasting risk of crop pests and pathogens
Lead Participant:
CLIMATE EDGE LIMITED
Abstract
In this project we are building a novel pest/disease forecasting service that uses machine learning 'ensemble' techniques to imbue highly localised predictive power and wide pest-crop-geography application potential.
This broad-spectrum approach to forecasting is highly innovative and has the potential to drive synergistic improvements in the usage of inputs across all of the UK's most important crops. This innovation will drive a reduction in agro-chem usage, increase in crop yields and reduce the carbon footprint of UK agriculture.
This broad-spectrum approach to forecasting is highly innovative and has the potential to drive synergistic improvements in the usage of inputs across all of the UK's most important crops. This innovation will drive a reduction in agro-chem usage, increase in crop yields and reduce the carbon footprint of UK agriculture.
Lead Participant | Project Cost | Grant Offer |
---|---|---|
CLIMATE EDGE LIMITED | £160,997 | £ 112,698 |
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Participant |
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INNOVATE UK | ||
JAMES HUTTON LIMITED | £23,213 | £ 11,606 |
ALO MUNDUS LIMITED | ||
THE JAMES HUTTON INSTITUTE | £59,031 | £ 59,031 |
People |
ORCID iD |
Robert Crow (Project Manager) |