Open-source hardware and software.
- Member of the AgriFoRwArdS CDT Equality, Diversity and Inclusion Panel.
David’s long-term goal is to be able to create/modify an open-source system, which is accessible to as many people as possible.
Machine Learning for the Detection of Weeds among Sugar Beets
This project will create a vision system able to detect and localise weeds in images gathered from an RGB camera mounted on phenotyping robots. Previous work has led to a number of systems that can provide bounding boxes of weeds in images, however the accuracy of localisation is a rarely used metric during evaluation. The output of the system proposed by this project in intended for informing the use of herbicides, and as such the localisation accuracy will be key to its success. The data used to train the model(s) will be a combination of labelled images gathered from the University of Lincoln’s Riseholme campus and Campus Klein Altendorf in Bonn, Germany.