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

Invasive plant diseases threaten agricultural production and natural ecosystems.For instance, Xylella fastidiosa has devastated large numbers of olive trees in Italy, with future economic impacts projected to reach billions of euros. Compounding the issue, resources for managing these pathogens are often 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.