IoT, Data Quality, Data Management Systems, Data analysis, ML
Mitigate GHG emissions in agricultural and food systems
Accurate measurement and modeling of GHG emissions are crucial for understanding the impact of agricultural activities and for formulating effective policies to mitigate emissions. By developing data collection systems and leveraging machine learning techniques, it becomes possible to collect real-time data on emissions, analyze patterns and trends, and make predictions for future emissions scenarios. This can aid in the formulation and evaluation of climate policies and provide valuable insights for decision-makers.
The IoT and Network Data Analytics Research group conducts work on precision agriculture (https://www.ucd.ie/consus/) and GHG modeling and monitoring (https://smartbog-monitor.ucd.ie/) projects which aligns well with the growing need for data-driven approaches in agriculture and environmental management. By leveraging technologies such as IoT, machine learning, and data analytics, the group has contributed significantly to the development of insights, models, and tools that aid in tracking, understanding, and mitigating GHG emissions, as well as supporting evidence-based policy decisions.
University College Dublin is an public university focused on high quality research.
The university operates a production farm suited for conducting research in agri-food projects.