World's Best Scientists 2026 revealed!

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Computer Science

D-Index
44
Citations
11523
World Ranking
7428
National Ranking
444

Overview

Tony P. Pridmore is affiliated with the University of Nottingham in the United Kingdom. Their research spans several interdisciplinary fields, primarily focusing on plant science, computer vision and pattern recognition, and radiology, nuclear medicine and imaging. Additional areas of study include ocean engineering and biomedical engineering.

Their research topics cover a diverse range of subjects related to both agriculture and medical imaging technologies. Key topics include:

  • Smart Agriculture and AI
  • Medical Imaging Techniques and Applications
  • Geophysical Methods and Applications
  • Advanced X-ray and CT Imaging
  • Radiomics and Machine Learning in Medical Imaging
  • Geophysical and Geoelectrical Methods
  • Image Processing and 3D Reconstruction

Tony P. Pridmore has published numerous papers in various scientific venues. Frequent publication venues include:

  • Zenodo (CERN European Organization for Nuclear Research)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • F1000Research
  • Frontiers in Plant Science
  • Plant Methods

Their recent papers, reflecting contributions to computational plant science, robotics, and imaging, include the following:

  • Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields, 2020, Plant Methods
  • Meeting sustainable development goals via robotics and autonomous systems, 2022, Nature Communications
  • Potential of geoelectrical methods to monitor root zone processes and structure: A review, 2020, Geoderma
  • Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks, 2020, IEEE Transactions on Image Processing
  • Data management challenges for artificial intelligence in plant and agricultural research, 2023, F1000Research

Collaboration is a significant aspect of their research activity. Frequent co-authors include:

  • Andrew P. French
  • Dimitrios Bellos
  • Mark Basham
  • Michael P. Pound
  • Sacha J. Mooney

Best Publications

  • The Visual Object Tracking VOT2016 Challenge Results

    Matej Kristan;Aleš Leonardis;Jiři Matas;Michael Felsberg

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Plant Phenomics, From Sensors to Knowledge

    François Tardieu;Llorenç Cabrera-Bosquet;Tony P. Pridmore;Malcolm J. Bennett

  • Developing X-ray Computed Tomography to non-invasively image 3-D root systems architecture in soil

    S. J. Mooney;T. P. Pridmore;J. Helliwell;M. J. Bennett

  • Colocalization of fluorescent markers in confocal microscope images of plant cells

    Andrew P French;Steven Mills;Steven Mills;Ranjan Swarup;Malcolm J Bennett

  • Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

    Michael P. Pound;Jonathan A. Atkinson;Alexandra J. Townsend;Michael H. Wilson

  • RooTrak: automated recovery of three-dimensional plant root architecture in soil from x-ray microcomputed tomography images using visual tracking.

    Stefan Mairhofer;Susan Zappala;Saoirse R. Tracy;Craig Sturrock

  • RootNav: Navigating Images of Complex Root Architectures

    Michael P. Pound;Andrew P. French;Jonathan A. Atkinson;Darren M. Wells

  • Classroom collaboration in the design of tangible interfaces for storytelling

    Danae Stanton;Victor Bayon;Helen Neale;Ahmed Ghali

  • High-Throughput Quantification of Root Growth Using a Novel Image-Analysis Tool

    Andrew French;Susana Ubeda-Tomás;Tara J. Holman;Malcolm J. Bennett

  • Expected, sensed, and desired: A framework for designing sensing-based interaction

    Steve Benford;Holger Schnädelbach;Boriana Koleva;Rob Anastasi

  • Knowledge-directed interpretation of mechanical engineering drawings

    S. H. Joseph;T. P. Pridmore

  • British Machine Vision Conference 1999

    Tony P Pridmore

  • Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

    Junfeng Gao;Junfeng Gao;Junfeng Gao;Andrew P. French;Michael P. Pound;Yong He

  • Deep Learning for Multi-task Plant Phenotyping

    Michael P. Pound;Jonathan A. Atkinson;Darren M. Wells;Tony P. Pridmore

  • Automated Recovery of Three-Dimensional Models of Plant Shoots from Multiple Color Images

    Michael P. Pound;Andrew P. French;Erik H. Murchie;Tony P. Pridmore

  • RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

    Robail Yasrab;Jonathan A Atkinson;Darren M Wells;Andrew P French

  • Root System Markup Language: Toward a Unified Root Architecture Description Language

    Guillaume Lobet;Michael P. Pound;Julien Diener;Christophe Pradal

  • Validation of a novel computer-assisted sperm analysis (CASA) system using multitarget-tracking algorithms.

    Mathew James Tomlinson;Karen Pooley;Tracey Simpson;Thomas Newton

  • Behavioural changes in dairy cows with lameness in an automatic milking system

    Giuliana G. Miguel-Pacheco;Jasmeet Kaler;John Remnant;Lydia Cheyne

Frequent Co-Authors

Malcolm J. Bennett
Malcolm J. Bennett University of Nottingham
Sacha J. Mooney
Sacha J. Mooney University of Nottingham
Erik H. Murchie
Erik H. Murchie University of Nottingham
Steve Benford
Steve Benford University of Nottingham
Sue Cobb
Sue Cobb University of Nottingham
Ranjan Swarup
Ranjan Swarup University of Nottingham
Jonathan P. Lynch
Jonathan P. Lynch Pennsylvania State University
John Porrill
John Porrill University of Sheffield
Sotirios A. Tsaftaris
Sotirios A. Tsaftaris University of Edinburgh
Michel Valstar
Michel Valstar University of Nottingham

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