Revolutionizing agriculture through AI-driven digital twin technology to optimize productivity, reduce emissions, and achieve sustainability.
The AI-powered Digital Twins for Sustainable Agriculture project is a transformative initiative that seeks to revolutionize farming by addressing critical environmental challenges, enhancing productivity, and fostering sustainability. Using cutting-edge artificial intelligence, machine learning, and digital twin technologies, the project provides tools that enable farmers, researchers, and policymakers to understand, predict, and reduce greenhouse gas (GHG) emissions while optimizing agricultural practices.
Our mission is to bridge the gap between innovation and practicality, offering a platform that supports data-driven decisions to combat climate change, advance sustainable farming, and achieve global net-zero emissions goals. This initiative reimagines agriculture as a driver of environmental stewardship and resilience. This research is funded by UK Research and Innovation (UKRI) and the Engineering and Physical Sciences Research Council (EPSRC), a £2.5 million research grant on Self-Learning Digital Twins for Sustainable Land Management.
Agriculture is a significant contributor to global GHG emissions, with methane from livestock playing a critical role. Developed by a team Led by Professor Baihua Li and Professor Qinggang Meng at Department of Computer Science Loughborough University, the research team tackles this challenge by delivering innovative tools to monitor, predict, and manage emissions effectively. The digital twin platform include machine learning models designed to estimate methane emissions from livestock farming, predict milk productivity and ammonia emissions from dairy farms, and analyse how land use and environmental factors influence methane emissions across the UK.
By integrating advanced AI technologies with practical agricultural solutions, the project supports informed decision-making and fosters collaboration among farmers, researchers, and policymakers. The platform empowers stakeholders to enhance agricultural productivity and resilience while reducing environmental impact, driving meaningful progress toward global net-zero emissions.
A range of AI models, tools and emission calculators are developed and integrated into the digital twin platform. Key features include:
1. Comprehensive Visualization Tools
Global real-time methane observations from Sentinel-5P TROPOMI satellite.
Interactive geospatial maps integrate methane (CH4) concentration data with land use and environmental factors, revealing hotspots and spatial trends in emissions.
Historical analysis tools track variables like soil temperature, rainfall, and methane concentrations, helping stakeholders identify temporal patterns and make informed decisions.
2. AI Predictive Modeling
Deep learning AI models for predicting greenhouse gas emissions (e.g., methane (CH4) and carbon dioxide (CO2) using time-series analysis, capturing complex interactions with environmental factors and historical trends.
Hybrid AI Models: Innovative AI combines advanced time-series analysis with machine learning to predict methane emissions accurately, considering complex interactions and historical trends.
Farm-Scale Methane Modeling: Models provide insights into environmental and biological factors influencing emissions, enabling targeted mitigation strategies.
Livestock Productivity Tools: Stacking ensemble models predict milk yield based on biological and feeding data, improving farm efficiency and sustainability.
3. Livestock Emission and Productivity Calculators
Intuitive calculators estimate methane emissions for cattle and sheep, helping farmers explore reduction strategies tailored to their practices.
Predictive tools optimize outcomes like milk yield, ammonia emissions, and feed efficiency.
4. Explainable AI for Stakeholder Insights
Transparent AI techniques illustrate how environmental and biological factors affect emissions, offering actionable insights and building trust in decision-making.
5. Integrated and Holistic Data Framework
Combines satellite methane data with farm-scale measurements for a comprehensive emissions dataset.
Uses advanced modeling techniques to align historical data with predictions, ensuring accuracy and relevance.
6. Dynamic and Accessible Platform
A user-friendly interface with real-time visualizations, customizable tools, and global accessibility ensures that farmers, researchers, and policymakers can easily utilize the platform.
Open-source design allows for scalability and adaptation to diverse agricultural systems.
We envision a future where agriculture is not only productive but also a leader in combating climate change. Through innovation and sustainability, the AI-powered Digital Twins for Sustainable Agriculture project aims to redefine farming practices, creating a resilient and environmentally responsible agricultural sector.
Join us in transforming agriculture for a sustainable future. Together, we can achieve net-zero emissions and secure a healthier planet for generations to come.
This research was funded by UK Research and Innovation (UKRI) and the Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/Y00597X/1. The AI models powering the tools were trained on a diverse range of real-world data, made possible through collaborations with National Bovine Data Centre, Cattle Information Service, Hooks Farm Dairy, North Wyke Farm Platform, Fullwood Joz, Silvasheep etc. We extend our sincere gratitude to the Department for Environment, Food & Rural Affairs (DEFRA), European Space Agency (ESA), UK Met Office, Natural Environment Research Council (NERC) for invaluable data that significantly contributed to our research. We acknowledge the partnership with University of Leicester and University of Bristol on this successful UKRI-funded project.