Publications

• Darbyshire, M., Salazar-Gomes, A., Lennox, C., Gao, J., Sklar, E., and Parsons, S. (2022) ‘Localising Weeds Using a Prototype Weed Sprayer‘, UKRAS2022 proceedings.
• Gao, X., Xue, W., Lennox, C., Stevens, M., & Gao, J. (2023) ‘Advancing Early Detection of Virus Yellows: Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields‘, Preprint (August 2023).
• Gao, X., Xue, W., Lennox, C., Stevens, M., & Gao, J. (2024) Developing a hybrid convolutional neural network for automatic aphid counting in sugar beet fields, Computers and Electronics in Agriculture, 220, 108910.

Presentations

• “Automatic Detection of Black Rot in Images of Grapes” (oral) – AgriFoRwArdS CDT Summer School 2021 [June 2021] – Online.
• “Automatic Detection of Black Rot in Images of Grapes” (oral) – AgriFoRwArdS CDT Annual Conference 2021 [July 2021] – Online.
• “Synthetic Image Generation Pipeline for Weed Detection in Fields” (poster) – AgriFoRwArdS CDT Annual Conference 2022 [June 2022] – Lincoln, UK.
• “Reel-Bot” (oral) – AgriFoRwArdS CDT Summer School 2022 [July 2022] – Norwich, UK.
• “Real-time vision-based spot spraying development for high efficiency and precision weed management” (oral) – AgriFoRwArdS CDT Quarterly PhD Research Progress Meeting [January 2023] – Online.
• “Serving a Full English Breakfast” (oral) – AgriFoRwArdS CDT Summer School 2023 [March 2023] – Lincoln, UK.
• “Real-time vision-based spot spraying development for high efficiency and precision weed management” (oral) – AgriFoRwArdS CDT Quarterly PhD Research Progress Meeting [July 2023] – Norwich, UK.
• “Real-time vision-based spot spraying development for high efficiency and precision weed management” (poster) – Towards Autonomous Robotic Systems (TAROS) 2023 / AgriFoRwArdS CDT Annual Conference 2023 / Joint Robotics CDT Annual Conference 2023 [September 2023] – Cambridge, UK.
• “Interview with fellow students: about you, your research, & the PhD experience” (oral) – AgriFoRwArdS CDT Quarterly PhD Research Progress Meeting & new student welcome [September 2023] – Lincoln, UK.
• “Title unknown” (poster) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.
• “Exploration of LLM-Enhanced State-Machine function-calls for Planning Robot Actions” (oral) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.

Other Activities

• Worked on an AI Unleashed Robotics project with Prof Elizabeth Sklar
• Represented the AgriFoRwArdS CDT at Douglas Bomford Trust Bi-Annual Meeting – showcasing research and robotic demonstrations to delegates [April 2022] – Lincoln, UK.
• UK RAS RoboLab Live demonstration (University of Lincoln submission) for the UKRAS festival of robotics – Demonstration of robotics systems in strawberry polytunnel for two applications [June 2022] – Online.
• Represented the AgriFoRwArdS CDT at the University of Lincoln’s stand at the Lincolnshire Show 2024 – showcasing research and robotics demonstrations [June 2024] – Lincoln, UK.

About me

I studied an MEng in Electronical Engineering at the University of Southampton. During this programme I also completed two placements at the University of Manchester. I am particularly excited about the industry links within the CDT as I feels that industry input will provide specific direction and ensure practical application for research. I am attracted to the areas of robot vision, robot navigation and sensing but also has a general interest in control of embedded systems, computer vision and robotics.

MSc Project

Synthetic Image Generation Pipeline for Weed Detection in Fields

This project will involve the generation of a pipeline that will produce synthetic images of weeds/crops by taking existing images containing weeds/crops and transposing them into other images of fields. This can be used to greatly increase the size of the datasets that are available to train machine learning models for weed detection as these synthetic images can be used for training alongside the original image dataset. The visual corrections that are necessary to make the weed/crop look like it belongs in the image it’s being transposed onto will be the main area of interest/research for this project.

PhD Project

Real-time vision-based spot spraying development for high efficiency and precision weed management 

Spot spraying, as a spatially variable weed management strategy, targets only weed species in fields to minimize the use of chemicals. Commercially available technologies based on sensing of vegetation optical properties are typically constrained by detecting weeds on a soil background (i.e. greenness detection in a bare soil background) and are not suitable to detect weeds among a growing crop. A vision-based spot spraying system enables discrimination between vegetation species. One of the key components for the vision-based spot spraying development is to build a reliable and robust weed/crop discrimination model. Traditionally, the development of a vision-based weed/crop discrimination model is highly relying on image analysis with prior knowledge of the defined colour, texture and morphology features between weed and crop. But this might fail to generalise over different crop fields with multiple weed species. The recent technology advancements in machine learning and computer vision have provided new opportunities to develop a robust and reliable vision-based weed/crop discrimination model under unstructured field conditions.  

The main objectives for this project are as follows: To build image libraries containing weeds and crops using various methods of both data augmentation and data collection; to develop a deep learning model to detect the presence of weeds/crops in an image; and then to integrate this deep learning model onto a physical system that uses a spray boom to spray weeds 

Callum’s PhD project is being carried out in collaboration with the Douglas Bomford Trust, with primary supervision by Prof Elizabeth Sklar.