David Hall

PhD Candidate Website

General Information

Current PhD Title: A Rapidly Deployable Approach for Automated Visual Weed Classification without Prior Species Knowledge

Expected Completion Date: 19th January 2018

Fields of Expertise:

              • Computer Vision
              • Machine Learning
              • Clustering
              • Plant Recognition


Hi, I am David Hall, PhD student at the Queensland University of Technology. This website contains the work which I have performed over the course of my PhD as well as some general information about who I am. My PhD is 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. This direction of research, is what I feel will allow agricultural robotic weeding to become more widespread than it is now, enabling new systems to be rapidly deployed anywhere in the world without need for a laboratory to pre-define all conditions, giving farmers more flexibility. My main goal with any of my research or work is to be useful to people. I try to think in terms of how the end-product would work and how it could best be used by other people. 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. This website contains a summary of my current PhD status, my areas of knowledge, and a link to my CV, followed by information about my current published works.

Publications

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: https://authors.elsevier.com/a/1WjsBcFCSBk1Q

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