Publications

• Camara, F., Waltham, C., Churchill, G., & Fox, C. (2022) OpenPodcar: an Open Source Vehicle for Self-Driving Car Research, Preprint.

Presentations

• “Automatic Detection of Black Rot in Images of Grapes” (oral) – AgriFoRwArdS CDT Summer School 2021 [June 2021] – Online.
• “Fish Sorting: A whatsinyour.net solution” (oral) – AgriFoRwArdS CDT Summer School 2022 [July 2022] – Norwich, UK.
• “Iterative Particle Swarm Optimisation –Hyperparameter Tuning” (poster) – Towards Autonomous Robotic Systems (TAROS) 2023 / AgriFoRwArdS CDT Annual Conference 2023 / Joint Robotics CDT Annual Conference 2023 [September 2023] – Cambridge, UK.
• “Title Unknown” (poster) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.
• “SLAM (Simultaneous localisation and mapping)” (oral) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.

Other Activities

• Member of the AgriFoRwArdS CDT Equality, Diversity and Inclusion (EDI) Panel (March 2022 to present).
• Discussion Panel member at the AgriFoRwArdS CDT Annual Conference 2022 – Discussion topic: AgriFoRwArdS PhD research progress [July 2022].

About me

My long-term goal is to be able to create/modify an open-source system, which is accessible to as many people as possible.

MSc Project

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.

PhD Project

Machine learning-based vision for “green-on-green” spraying

The goal of intelligent spraying is that herbicides are more precisely targeted. This reduces waste and is beneficial for the environment. A key step in such spraying is identifying weeds. Typical approaches to doing this use computer vision, typically methods based around the use of machine learning, operating on pictures taken from cameras that view weeds and crops from above.
Current vision technology has proved able to handle “green-on-brown” scenarios, producing good accuracy of detection of weeds where weeds and crops are easy to spot against very distinctly coloured backgrounds, such as soil. This is sufficient in the early stages of growth, when crops and weeds are small. However, in later stages of growth, and the canopies of crops and weeds begin to overlap, accurately and efficiently detecting weeds becomes much harder. This “green-on-green” scenario is currently beyond what can feasibly be handled. Solving the “green-on-green” weed detection problem is the focus of this PhD.

The reason that “green-on-green” is hard, is that we cannot rely on simple colour segmentation. In the “green-on-brown”, segmenting images into green and brown areas identifies green areas with distinctive shapes that can easily be classified. (This is what is going on in existing detectors whether they are based on classical machine vision or more modern deep learning approaches.) When plants overlap, the green regions no longer contain such distinctive shapes, or such large areas of distinctive shapes, and existing approaches to detection struggle as a result.

The answer is to build detectors that look for things other than just colour. This PhD will pursue two lines of inquiry: adding additional dimensions to the image data, and building detectors that focus on different features, in the framework of deep learning-based vision.

Grey’s PhD project is being carried out in collaboration with Syngenta, under the supervision of Prof Simon Parsons.