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Robotics and AI to Support Scalable Agronomy (RAISSA) 

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

Research Aims

The "Robotics and AI to Support Scalable Agronomy (RAISSA)" PhD Project within the UKRI AI Centre for Doctoral Training in SUSTAIN, addresses the pressing need for sustainable and efficient agricultural practices by using Robotics and AI to support agronomists to scale their services. The 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. This is crucial in meeting the growing demand for food while reducing environmental impact. 

The successful student will work on tasks such as developing algorithms and techniques for creating spatio-temporal representations of various crop traits, incorporating expert knowledge from agronomists into the learned spatio-temporal models, and investigating methods for detecting anomalies and deviations from predictions derived from learned crop models. This work involves working with deployed robotic fleets and collaborating with agronomy experts to guide the development of the models and the identification of anomalies.

Through this project, the successful candidate will gain valuable skills in machine learning, explainable AI, and robotics, as well as hands-on experience in working with a deployed robotic fleet, machine learning for time series, and collaborating with agronomy experts. Additionally, the successful student will have access to training and development opportunities provided by the UKRI AI Centre for Doctoral Training in SUSTAIN, including workshops, seminars, and networking events. This project offers an exciting opportunity for a student to contribute to the advancement of sustainable and efficient agricultural practices through cutting-edge research and practical application.

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Interview date

Apply for this studentship

See our Application Page.

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