| Principal Investigator | Stuart King |
| Institution | University of Edinburgh |
| Funding awarded | £18,847.13 |
| Project duration | June 2025 – November 2025 |
This pilot project explored the application of machine learning approaches to soil health datasets, with the aim of improving the analysis and interpretation of complex environmental data. By leveraging AI methods, the project sought to enhance understanding of soil health indicators and support more effective use of large-scale environmental datasets in bioscience research.
Key Outputs
- Machine learning workflows to clean, harmonise and analyse a global soil health dataset.
- Recruitment and training of an MSc Bioinformatics student through the project.
- An academic paper, currently in preparation.
- Project code and notebooks to be released openly on GitHub.
Impact
The project improved the quality and usability of a large global soil health dataset for machine learning applications, providing new insights into regenerative farming practices. It also supported researcher development through the involvement of an MSc student and has led to discussions with an agri-tech company regarding future collaborative research and follow-on funding opportunities.