AI-Powered Digital Twin for Sustainable Agriculture and Greenhouse Gas Reduction

作者：Baihua Li, Mike Thompson, Tom Partridge, Ruiming Xing, Jack Cutler, Bashar Alhnaity, Qinggang Meng · 年份：2024 · DOI：10.1109/honet63146.2024.10822980 · 被引用次数：7 · 研究领域：Digital Transformation in Industry

Agricultural and livestock farming are important contributors to greenhouse gas (GHG) emissions, with methane emissions from ruminants being particularly significant. This research aims to showcase the innovative development of a digital twin platform integrated with AI for analyzing both current and historical GHG emissions. The digital twin harnesses AI for advanced predictive analytics, enabling the tracking and understanding of broad GHG emission trends over time. Key features of the platform include AI and machine learning models tailored for informative GHG estimation. It also offers interactive maps for visualizing spatial data and conducting association analysis. Users can benefit from dynamic historical data comparisons and utilize livestock emission calculators aligned with Intergovernmental Panel on Climate Change (IPCC) guidelines. The project supports Net Zero efforts by empowering farmers and organizational stakeholders to enhance environmental stew-ardship and promote sustainable agriculture.