EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS - Callum Lennox

Callum Lennox

  • University of Lincoln

Research Interests

Robot vision, robot navigation and sensing, control of embedded systems, computer vision and robotics.

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.
  • Lennox. C., Groves, K., Hondru, V., Arvin, F., Gornicki K., Lennox, B. (2019) ‘Embodiment of an Aquatic Surface Vehicle in an Omnidirectional Ground Robot’, 2019 IEEE International Conference on Mechatronics (ICM).

Presentations

  • AgriFoRwArdS CDT Annual Conference (2021): Automatic Detection of Black Rot in Images of Grapes.

Posters

  • AgriFoRwArdS CDT Annual Conference (2022): Synthetic Image Generation Pipeline for Weed Detection in Fields.

Other Activities and Outputs

  • Took part in the AgriFoRwArdS Summer School 2021 resulting in a co-authored presentation at AgriFoRwArdS CDT Annual Conference 2021: Automatic Detection of Black Rot in Images of Grapes (in collaboration with Mohammed Terry-Jack, Haihui Yan, YoonJu Cho, Grey Churchill, Charalampos Matsantonis)
  • Worked on an AI Unleashed Robotics project with Prof Elizabeth Sklar
  • Represented the CDT at the Douglas Bomford Trust bi-annual meeting (Mar 2022)

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

Title to be confirmed