David Hall

Postdoctoral Research Fellow

General Information


    • Australian Centre for Robotic Vision (ACRV)
    • Queensland University of Technology

Fields of Expertise:

    • Robotic Vision
    • Machine Learning
    • Evaluation Measures
    • Clustering
    • Plant Recognition

ORCID iD: 0000-0002-5520-0128

Staff Profile: profile

Contact: d20.hall@qut.edu.au

Hi, I am David Hall, Postdoctoral Research Fellow at the Queensland University of Technology. This website outlines my current publications and general information about myself as a researcher. My research focuses are on making robotic vision systems applicable for real-world situations and all of the problems which that can entail. My PhD focused on producing a weed classification system for agricultural robotics which can be applied rapidly to any field without needing to assume that we know what species will be present in advance. My current research with the ACRV robotic vision challenge project focuses on how we evaluate vision systems for robotics applications. Ensuring they are robust, probabilistic, and adaptable to changing environments and conditions. It is my hope that in future, robots will be smart enough to implement basic tasks in more open environments, with management and guidance from human operators.



Title: A rapidly deployable approach for automated visual weed classification without prior species knowledge

Authors: David Hall

Year: 2018

Published by: Queensland University of Technology

Link to Paper: thesis2018.pdf

Bibtex: thesis.bib

Title: A rapidly deployable classification system using visual data for the application of precision weed management.

Authors: David Hall, Feras Dayoub, Tristan Perez, Chris McCool

Year: 2018

Published in: Computers and Electronics in Agriculture, Vol. 148

Manuscript Download Link: COMPAG2018_manuscript.pdf

Official Paper Link:


Bibtex: compag2018.bib

Title: A Transplantable System for Weed Classification by Agricultural Robotics

Authors: David Hall, Feras Dayoub, Tristan Perez, Chris McCool

Year: 2017

Published in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems

Paper Link: IROS2017.pdf

Bibtex: iros2017.bib

Title: Towards Unsupervised Weed Scouting for Agricultural Robotics

Authors: David Hall, Feras Dayoub, Jason Kulk, Chris McCool

Year: 2017

Published in: IEEE International Conference on Robotics and Automation 2017

Paper Link: ICRA2017.pdf

Bibtex: icra2017.bib

Title: Evaluation of Features for Leaf Classification in Challenging Conditions

Authors: David Hall, Chris McCool, Feras Dayoub, Niko Sünderhauf, Ben Upcroft

Year: 2015

Published in: 2015 IEEE Winter Conference on Applications of Computer Vision

Paper Link: WACV2015.pdf

Bibtex: wacv2015.bib