Self-Learning Digital Twins for Sustainable Land Management

Lead Research Organisation: University of Leicester
Department Name: Sch of Geog, Geol & the Environment


Greenhouse gas emissions from agriculture and land use in the UK contribute to global climate change. The UK is committed to achieving net zero greenhouse gas emissions by 2050. Since 1990, greenhouse gas emissions from agriculture and land use have fallen, but in 2020 and 2021 they started rising again. 11% of UK GHG emissions stem from cattle and sheep grazing (7%) and degraded peatlands (4%).

This research project is developing an Artificial Intelligence algorithm called a 'Self-Learning Digital Twin' for sustainable land management. A Digital Twin applies computational modelling, environmental measurements and an Artificial Intelligence algorithm to provide new environmental insights into the functioning of a system. Farmers and land managers can ask questions that the Digital Twin can answer. In a nutshell, it is a digital model of the physical environment and is updated from real-time data, so that it mirrors the environment at all times. Digital Twins can support farmers and environmental managers to achieve better outcomes for their greenhouse gas emission reductions, ultimately saving time and resources.

The self-learning digital twin learns from real-time satellite images, greenhouse gas measurements from field instruments and other data. Its underlying model improves over time as new data are becoming available.
The project will promote sustainable cattle and sheep farming practices and peatland restoration. We will prepare the ground for an ethical and socially responsible application of artificial intelligence for achieving net zero greenhouse gas emissions. An important part of our work is to build a 'Community of Practice in AI for Net Zero' that brings together computer scientists with environmental, behavioural and social science researchers to develop a common approach. We will incorporate the social and ethical dimensions of digital twins, including who they may benefit or disadvantage.


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