John Atanbori

Dr John Atanbori

Lecturer in Computer Science

Faculty and Department

  • Faculty of Science and Engineering
  • Department of Computer Science and Technology

Qualifications

  • BSc
  • MSc (University of Sunderland )
  • MSc (University of Bradford)
  • PhD (University of Lincoln)

Summary

Dr John Atanbori is a Computer Science Lecturer at the University of Hull. He completed his PhD in Computer Science from the University of Lincoln. His research focuses on the application of Computer Vision, Machine Learning, and Deep Learning to Plant Phenotyping, Agric-Tech and Animal Behaviour. Before his lecturing career, he worked in the computing industry, developing Computer Vision algorithms for Agricultural systems that use hyperspectral and depth cameras. He has also worked as a Research Fellow at the University of Nottingham’s Computer Vision Laboratory.

Undergraduate

Database Techniques

Advanced Computational Science

Recent outputs

View more outputs

Journal Article

Towards infield, live plant phenotyping using a reduced-parameter CNN

Atanbori, J., French, A. P., & Pridmore, T. P. (2020). Towards infield, live plant phenotyping using a reduced-parameter CNN. Machine Vision and Applications, 31(1), https://doi.org/10.1007/s00138-019-01051-7

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

Atanbori, J., Montoya, M., Selvaraj, M. G., French, A. P., & Pridmore, T. P. (2019). Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images. Frontiers in Plant Science, 10, https://doi.org/10.3389/fpls.2019.01516

A low-cost aeroponic phenotyping system for storage root development: unravelling the below-ground secrets of cassava (Manihot esculenta)

Selvaraj, M. G., Montoya-P, M. E., Atanbori, J., French, A. P., & Pridmore, T. (2019). A low-cost aeroponic phenotyping system for storage root development: unravelling the below-ground secrets of cassava (Manihot esculenta). Plant Methods, 15(1), https://doi.org/10.1186/s13007-019-0517-6

Classification of bird species from video using appearance and motion features

Atanbori, J., Duan, W., Shaw, E., Appiah, K., & Dickinson, P. (2018). Classification of bird species from video using appearance and motion features. Ecological informatics, 48, 12-23. https://doi.org/10.1016/j.ecoinf.2018.07.005

Presentation / Conference

Towards Low-Cost Image-based Plant Phenotyping using Reduced-Parameter CNN.

Atanbori, J., Chen, F., French, A. P., & Pridmore, T. (2018, September). Towards Low-Cost Image-based Plant Phenotyping using Reduced-Parameter CNN. Paper presented at British Machine Vision Conference 2018, BMVC 2018, Northumbria University

Research interests

Computer Vision

Machine Learning

Deep Learning

The application of Computer Vision, Machine and Deep Learning to Plant Phenotyping, Agric Technology and Animal Behaviour.