Dr Iain Gould, University of Lincoln; Prof Georgios Leontidis, University of Aberdeen; Prof Simon Parsons, University of Lincoln. Industry partner to be confirmed.

Cover crops are widely promoted as a way to improve soil health, reduce nutrient losses, and make farming more sustainable. Yet many UK farmers experience mixed results, particularly on heavy soils and in variable weather. 

Establishment can be patchy, multi-species mixtures may not persist as intended, and poor performance can affect spring crop planting. This project tackles a timely and important challenge: how can data and AI be used to understand when cover crops work, when they don’t, and how to manage them more effectively? 

This PhD will combine satellite imagery, drone data, and field measurements to study cover crop performance across the UK. It will use machine learning and computer vision to analyse large Earth-observation datasets, map establishment success and patchiness, and track species dominance in multi-species mixtures. 

At selected field sites, it will link these AI-derived insights to soil structure and seedbed quality following cover crop termination. The final stage of the project will integrate these datasets into an AI-driven decision-support tool designed to help farmers make better cover crop choices. 

The student will receive interdisciplinary training in AI, data science, alongside agronomy and soil science, including opportunities to undertake professional accreditation (BASIS). They will also gain experience working with real-world agricultural data, engaging with farmers and industry partners, and translating research into practical impact—preparing you for careers at the interface of AI, sustainability, and food systems.