Welcome to my webpage. I'm a Research Fellow working in Computer Vision at the Queensland University of Technology (QUT). I have a passion for bridging the gap between computer vision/machine learning research and their application to real-world problems.

I'm affiliated with the ARC Centre of Excellence for Robotic Vision (ACRV) and am working on the Strategic Investment in Farm Robotics (SIFR) project examining ways that vision can automate detection and identification of plants and crops for Agriculture. Other application areas include automation of environmental monitoring and industrial applcations.

Some ares of particular interest for me are the use deep learning methods to learn features (global and local), local feature modelling and session variability modelling.

There are several PhD positions available, more details can be found here. The deadline for an expression of interest is the 19th of September, 2015.


Our group has 4 papers accepted to ICRA 2017! We have 3 RA-L and 1 regular paper:
  • Mixtures of Lightweight Deep Convolutional Neural Networks: applied to agricultural robotics (IEEE RA-L)
  • Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Colour and 3D Information (IEEE RA-L)
  • Autonomous Sweet Pepper Harvesting for Protected Cropping Systems (IEEE RA-L)
  • Towards Unsupervised Weed Scouting for Agricultural Robotics
Best Paper Award at DICTA 2016 for "Exploiting Temporal Information for DCNN-Based Fine-Grained Object Classification"! Congratulations to everyone involved, especially ZongYuan Ge! The paper can be found here.

"DeepFruits: a fruit detection system using deep neural networks" has been published (August, 2016), a version of the paper can be found here.

Our paper "Sweet Pepper Pose Detection and Grasping for Automated Crop Harvesting" was a finalist for the 2016 ICRA Best Automation Paper Award. Congratulations to everyone involved! We also presented "Visual Detection of Occluded Crop: for automated harvesting" at ICRA 2016 in Stockholm, Sweden. You can see an example of this system working on real farm data here.

Code for mixtures of deep convolutional neural networks (MixDCNN) is available on github here.