PhD Studentship: AI and Deep Learning for Tree and Plant Health Monitoring: Advancing Biosecurity and Biodiversity

Plants and trees are vital to the UK’s economy, ecosystems, and climate goals, providing an estimated £15.7 billion annually in environmental and economic value and playing a crucial role in achieving Net Zero by 2050. In partnership with Plant Health at Defra (Department for Environment, Food & Rural Affairs), this project introduces a novel AI-driven framework to protect the nation’s plant life by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy.

The project harnesses computer vision, deep learning, and large language model (LLM) technologies to create advanced, multimodal predictive tools for plant health monitoring. Using imagery from RGB cameras, drones, satellites, and multispectral and hyperspectral sensors, the system will analyse data across multiple scales—from broad landscape views to microscopic symptom detection.

Through vision–language AI models, the framework will interpret visual and textual data to recognise early signs of decline, such as discolouration, wilting, or spotting, and generate predictive insights on emerging risks. This cutting-edge approach will provide early warnings, identify disease hotspots, and enable data-driven decisions that strengthen environmental protection, enhance biosecurity, and support a sustainable, resilient future for UK plant and tree health.

Entry requirements:

Applicants will normally need to hold, or expect to gain, at least a 2:1 degree (or equivalent) in Computer Science, Engineering, or an appropriate Master’s degree with good programming skills.

English language requirements:

Applicants must meet the minimum English language requirements. Further details are available on the International website

Funding information:

This studentship which is partially funded by NERC, provides a tax-free stipend of £20780 per annum (in 2025/26) and tuition fees at the UK rate for 3.5 years. It also provides a Research Training Support Grant (RTSG) of £8,000. Due to UKRI funding rules, no more than 30% of the studentships funded by this grant can be awarded to International candidates, but successful International candidates will have the difference between the UK and International tuition fees provided by the University.

Bench fees required: No

How to apply:

1. Complete a CENTA studentship application form in Word format (available from https://centa.ac.uk/apply/) under the ‘Our project-based studentships’ section on that page
2. All applications should be made online via the above ‘Apply’ button. Under Campus, please select ‘Loughborough’ and select the Programme “School of Science/Computer Science”. Please quote the advertised reference number ‘CENTA2026-LU05’ in your online application.
   During the online application process, upload the CENTA studentship application form and a CV in addition to the other required minimum supporting documents.
3. Application closing date is midnight (UK time) on Wednesday January 7th 2026. Interviews for short-listed candidates are expected to be held sometime in the period Monday February 2nd – Friday February 13th 2026

Advert information