Villanelle O'Reilly

SUSTAIN Cohort 1 Student

University

University of Lincoln

SUSTAIN Project

Robotics and AI to Support Scalable Agronomy (RAISSA)

Supervisors

Prof Marc Hanheide (University of Lincoln)
Prof Georgios Leontidis (University of Aberdeen)

Research Interests

Computer vision anomaly detection, robotics and autonomous systems, machine learning, self-supervised learning and hyperspectral imaging

Exploring a simulated tactile SLAM system in my undergraduate dissertation gave me a taste for research, and I joined the CDT as an opportunity to learn about and engage in research full-time! My favourite thing about Lincoln is the collaboration between the agricultural and compsci schools. Having access to a farm emulating a commercial environment and a well established robotics research centre will I'm sure make for some very good datasets! My PhD project is exploring anomaly detection on hyperspectral spatio-temporal data in agriculture, initially looking at strawberry crops. In my spare time, I enjoy playing tabletop role-playing games and Minecraft!

Robotics and AI to Support Scalable Agronomy (RAISSA)

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

This project addresses the pressing need for sustainable and efficient agricultural practices by using Robotics and AI to support agronomists to scale their services and 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.

Villanelle is working 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 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, Villanelle 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. This project is an exciting opportunity for a Villanelle to contribute to the advancement of sustainable and efficient agricultural practices through cutting-edge research and practical application.

Welcome to the UKRI AI Centre for Doctoral Training in Sustainable Understandable agri-food Systems Transformed by Artificial INtelligence (SUSTAIN).