top of page

Our 2024 Studentships 

We are delighted to announce our fully-funded, four-year PhD studentships in the application of Artificial Intelligence to sustainable agri-food, to start in October 2024.

 

Explore the projects in more detail below and apply via our Application Page.

Mini in Walled Garden.png

Robotics and AI to Support Scalable Agronomy (RAISSA) 

Prof Marc Hanheide, University of Lincoln; Prof Georgios Leontidis, University of Aberdeen; plus an Industry Supervisor

The use of Robotics and AI to help agronomists scale their services will be crucial in meeting the growing demand for food while reducing environmental impact. This project aims to develop methods for autonomous data gathering and interpretation supporting agronomic decision-making in agriculture by creating models of data fidelity and anomalies. Read more.

Home applicants only

On the Laptop

Transparent Carbon Footprint Quantification and Reporting in Agriculture 

Dr Milan Markovic, University of Aberdeen; Dr Paul Williams, Queen’s University Belfast; plus an Industry Supervisor)

Agriculture, responsible for 10% of global greenhouse gas emissions, holds significant potential for negative emissions, particularly in soil carbon sequestration. However, existing Monitoring, Reporting, and Verification (MRV) systems lack standardization, transparency, and face challenges in data availability. This PhD will explore the practical applications of AI technologies to capture additional information which could enhance transparency and thus support novel MRV systems. Read more.

Home applicants only

6538ded380e6bba929787d7d_growth towres white light-p-1080.webp

Machine Learning Crop Breeding in Precision-Controlled Vertical Farming for a Sustainable Future

Dr Aiden Durrant, University of Aberdeen; Dr Mamatha Thota, University of Lincoln; Professor Derek Stewart, Advanced Plant Growth Centre, The James Hutton Institute; Dr Tanveer Khan, Intelligent Growth Solutions (Industry Advisor)

This PhD opportunity addresses the imperative for innovative crops and production systems in vertical farming environments. The goal is to develop advanced Artificial Intelligence (AI) methods for Computer Vision (CV) to address plant production and breeding, assessing the impact of AI-driven approaches in controlled vertical farming settings. Read more.

Home applicants only

Green Pastures

Data and knowledge dual-driven artificial intelligence in remote sensing 

Anna Jurek-Loughrey, Queen’s University Belfast; Yeran Sun, University of Lincoln; plus an Industry Supervisor

Remote sensing data analysis plays a crucial role in advancing sustainable agriculture. However, the complexity of remote sensing, with intricate environmental interactions and the variability of natural landscapes, poses challenges for data-driven approaches. The key aim of this project will be to explore innovative AI technology that integrates both data-driven and knowledge-driven methods, aiming for more accurate and trustworthy remote sensing results. Read more.

Home applicants only

Polytunnel 4.jpg

Intelligent, Energy-efficient De-leafing for the Soft Fruit Industry 

Prof Elizabeth Sklar, University of Lincoln; Prof Greg Keeffe, Queen’s University Belfast; Sarah Palmer, Driscolls Genetics Limited (Industry Supervisor)

Plants that produce fruit also produce leaves; but when such plants are cultivated for fruit production, it is desirable to prune excess leaves and concentrate the plant's energy on growing fruit. This project will explore the application of intelligent robots to undertake de-leafing, including investigation of in-field practices conducted by human workers and study of plant science strategies to optimise fruit yield. Read more.

Home applicants only

Black Soil

Nature-based Solutions for Restoration of Degraded Soils in
Sub-Saharan Africa

Prof Jo Smith, University of Aberdeen; Prof Anil Fernando, University of Strathclyde; Getahun Yakob, Southern Agricultural Research Institute (SARI), Ethiopia (Industry Supervisor), Moses Kimani, Lentera Ltd, Kenya (Adviser)

Many soils in Sub-Saharan Africa are degrading due to high levels of erosion, decreasing organic matter, salinisation, acidification and contamination, resulting in declining productivity, farm income and household well-being. This project will use machine-learning to investigate the use of nature-based solutions to improve soil health, so allowing increased resilience to climate change. Read more.

Home applicants only

Dairy Farm

Advanced Video Processing to Optimise Feeding of Livestock in Farms

Dr Paul Murray, University of Strathclyde; Prof Ilias Kyriazakis, Queen’s University Belfast; Anthony McMahon Peacock Technology (Industry Supervisor)

A key contributor to the GHG footprint of milk production is associated with animal feedstock, which, if not optimised for each animal, can lead to increases in CH4. This PhD project will introduce new video processing tools to interpret animal eating and feeding- behaviours with the aim of personalising and optimising the feedstock for individual farm animals. To achieve this, the successful candidate will design state-of-the-art image and video processing analytics using AI (artificial intelligence) and machine learning (ML), potentially in combination with traditional signal processing techniques. Read more.

Home applicants only

Lamb Leaping on Grass

Multi-Object Visual Tracking (MOT) of Animals in Agriculture

Niall McLaughlin, Queen’s University Belfast; Christos Tachtatzis, University of Strathclyde; plus an Industry Supervisor

Computer vision, video analytics, and AI can help monitor and improve animal welfare while improving farm productivity. However, monitoring animals poses unique challenges for AI, including tracking with erratic and non-linear motion and coping with changes in appearance as animals grow. This studentship will seek to develop improved Multi-Object Visual Tracking (MOT) algorithms, laying the foundation for future methods for applying AI in agriculture and opening avenues for new commercial applications. Read more.

Home applicants only

bottom of page