Publications

• Trimble, R., & Fox, C. (2023) Skid-steer friction calibration protocol for digital twin creation. In: TAROS, September 12-15 2023, Cambridge, UK.
• Russell, R., & Cunniffe, N.J. (2024) Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics, pre-print.

Presentations

• “Improved Control of Invasive Plant Disease Epidemics Using Partially Observable Markov Decision Processes” (poster) – AgriFoRwArdS CDT Annual Conference [June 2022] – Lincoln, UK.
• “With machine learning, data fusion and an understanding of the marine environment to guide fishers to the best catch” (oral) – AgriFoRwArdS CDT Summer School 2022 [July 2022] – Norwich, UK.
• “Using reinforcement learning to optimise adaptive control of invading plant disease epidemics” (oral) – AgriFoRwArdS CDT Quarterly PhD Research Progress Meeting [January 2023] – Online.
• “Team Toast” (oral) – AgriFoRwArdS CDT Summer School 2023 [March 2023] – Lincoln, UK.
• “Reinforcement Learning for Control of Invasive Plant Diseases” (oral) – University of Cambridge Plant Sciences Departmental Seminar [May 2023] – Cambridge, UK.
• “Can we use Machine Learning to Improve Control of Invasive Plant Diseases?” (oral) – Accelerate AI in Biological Science workshop [May 2023] – Cambridge, UK.
• “Using reinforcement learning to optimise adaptive control of invading plant disease epidemics” (oral) – AgriFoRwArdS CDT Quarterly PhD Research Progress Meeting [July 2023] – Norwich, UK.
• “Integrating Reinforcement Learning and Epidemiological Models for Disease Control Optimisation with Limited Information” (oral) – 12th International Congress of Plant Pathology: ICPP Satellite Event 2023 [August 2023] – Lyon, France.
• “Integrating Epidemiological Modelling with Reinforcement Learning for Effective Controls of Invasive Plant Disease” (poster) – Towards Autonomous Robotic Systems (TAROS) 2023 / AgriFoRwArdS CDT Annual Conference 2023 / Joint Robotics CDT Annual Conference 2023 [September 2023] – Cambridge, UK.
• “Skid-steer friction calibration protocol for digital twin creation” (poster) – Towards Autonomous Robotic Systems (TAROS) 2023 / AgriFoRwArdS CDT Annual Conference 2023 / Joint Robotics CDT Annual Conference 2023 [September 2023] – Cambridge, UK.
• “Baselines for optimising epidemiological control” (poster) – International Epidemiology Workshop 2024 [April 2024] – Foz do Iguaçu, Brazil.
• “Integrating epidemiological modelling with reinforcement learning for effective control of invasive plant disease” (poster) – AgriFoRwArdS CDT Quarterly PhD Research Progress Meeting [June 2024] – Cambridge, UK.
• “Exploration of LLM-Enhanced State-Machine function-calls for Planning Robot Actions” (oral) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.
• “Reinforcement learning for optimising prioritization of epidemic control – baselining and interfaces” (poster) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.
• “Baselines for optimising epidemiological control” (oral) – AgriFoRwArdS CDT Annual Conference 2024: Robots in Action [July 2024] – Norwich, UK.

Other Activities and Outputs

• Discussion Panel member for the AgriFoRwArdS Seminar Series – Discussion topic: Charles Nicklin’s talk “Agricultural Engineering and the role of the IAgrE” [October 2021].
• Discussion Panel member for the AgriFoRwArdS Seminar Series – Discussion topic: Prof Dionysis Bochtis’ talk “The digital transformation of agricultural production – advancements, opportunities, and challenges” [November 2021].
• Chaired the February 2022 AgriFoRwArdS Seminar Series with Dr Mark Ryan, watch here.
• Discussion Panel member for the AgriFoRwArdS Seminar Series – Discussion topic: Dr Ayse Kuckyilmaz’s talk “Dynamic Role Allocation and Shared Control in Human-Robot Collaborative Teamwork” [March 2022].
• Discussion Panel member at the AgriFoRwArdS CDT Open Day for potential students – Discussion topic: What is it like to be an AgriFoRwArdS student [November 2022].

About me

Before joining the CDT I was an electronics engineer working for Arm for over a decade. I led some big teams building Neural network Processing Units and I’ve come back to academia to explore how ML technology can be applied in agriculture. There’s a crunch coming up in the next couple of decades between population, climate change and biodiversity and I believe improving agricultural efficiency is a big part of how we can get through that. I’ve also had various placements and consultancy roles including keeping BBC Alba on air in Glasgow, working for Hitachi in Japan, designing control electronics for a telecoms start up and developing a drug delivery device for arthritis patients. I’ll be studying my PhD in the plant sciences department in Cambridge in cooperation with Defra. I also enjoy running, climbing and cooking.

MSc Project

Improved Control of Invasive Plant Disease Epidemics Using Partially Observable Markov Decision Processes

Invasive plant diseases cause damage to agricultural crops and ecosystems. However, growers and policy makers have limited resources for control of any given epidemic and collecting information about epidemic status can be expensive. Partially Observable MDPs (POMDPs) are used to model sequential decision making problems where the agent has only partial information about the situation. This project will explore application of POMDP techniques to plant disease epidemics by building a compartment based epidemic model and measuring the effectiveness of epidemic control against a simple, heuristic-based agent.

PhD Project

Using reinforcement learning to optimise adaptive control of invading plant disease epidemics

Invasive plant diseases threaten agricultural production and natural ecosystems. For example, Xylella Fastidiosa has killed large numbers of olive trees in Italy with future economic impact estimated in the billions of euros. At the same time, the resources to manage these pathogens are limited. Fast responses to outbreaks can minimise the damage. However, this means decisions on how resources are deployed must be made at the start of an outbreak when information about the disease progress and epidemic parameters in a new environment may be limited.

This project aims to explore and evaluate the use of reinforcement learning approaches to optimise control of invasive plant diseases. This will mean building plant disease models and reinforcement learning agents and evaluating the performance of the agents. The work will investigate the simulated cost of control, epidemic outcomes and robustness to uncertainty in the epidemic model. The techniques will aim to be generally applicable but will also be tested with real case studies. 

Rachel’s PhD project is being carried out in collaboration with Defra, with primary supervision by Dr Nik Cunniffe.