David Churchill is from Dorset but is already studying at the University of Lincoln. David loved doing project work in the third year of their degree and so felt the CDT was a great opportunity to continue this. David is already familiar with Lincoln and says one of their favourite things about the area is that they are able to walk to everything, which “definitely justifies trips to get doughnuts and milkshakes”! David is looking forward to continuing their study at the University of Lincoln and will remain at Lincoln to do their PhD. Their long-term goal is to be able to create/modify an open-source system, which is accessible to as many people as possible. In their spare time David enjoys drawing and playing video games, and they are also an advocate of the LGBTQ+ community.
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.