Papers
18
Total Citations
905
H-Index
14
About
Tony Pridmore is a prominent computer scientist and researcher whose work spans computer vision, robotics, and automated plant phenotyping. Based at the University of Nottingham, Pridmore has made foundational contributions to the application of machine learning and image analysis in agricultural and biological sciences. His 2017 paper demonstrating that deep learning achieves state-of-the-art performance in image-based plant phenotyping (373 citations) stands as a landmark contribution, helping establish automated approaches as essential tools for large-scale genetic discovery. His research group has pioneered 3D plant shoot reconstruction using active vision systems, addressing the formidable challenge of modeling complex plant architectures to support phenotyping and photosynthesis simulation studies. Pridmore's influence extends to agricultural robotics and autonomous systems, with influential work examining how such technologies can advance the UN Sustainable Development Goals. His career spans over three decades, with early contributions to 3D vision systems like TINA shaping the foundations of industrial robotics. His interdisciplinary reach is further demonstrated through work on root growth dynamics and dairy cow behaviour monitoring, reflecting a sustained commitment to applying intelligent vision systems across real-world biological challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Meeting sustainable development goals via robotics and autonomous systems86 citations · 2022
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- 6Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling45 citations · 2019
- 7Agricultural Robotics: The Future of Robotic Agriculture36 citations · 2018
- 8Geometrical Modeling from Multiple Stereo Views32 citations · 1989
- 9Plant phenomics:: history, present status and challenges26 citations · 2018
- 10TINA: a 3D vision system for pick and place25 citations · 1988