ProfessorRichard Green

Professor Richard Green (Dept Computer Science and Software Engineering, University of Canterbury) heads UC Computer Vision with teaching and research interests in computer vision and AI including an emphasis on AI-based real-time 3D reconstruction and tracking, particularly with application to robots (land/UAVs/underwater) and precision agriculture. Prior to joining academia in 2004, he successfully led the research, development and commercialisation of branch banking software to the international banking industry from his own start up company of 50 staff in Australia and eventually sold the IP to a multi-national computer corporation.

With over 200 refereed publications, Richard heads the UC Computer Vision Research Lab (UC Vision) with an emphasis on AI and machine learning for computer vision (autonomous robots, drones, underwater robots, human movement). Richard also co-chairs the NZ AI Researchers Association and leads UC AI (Research Cluster).

Professor Green has successfully completed supervision of 18 PhD theses (all passed) and 14 MSc theses (all passed with grade A or higher), and is currently supervising 10 postgraduate students. He led (as PI) successful MBIE projects, UOCX1001 Vision Based Automated Pruning, UOCX1601 Autonomous Forest Pruning and Data Collection (using drones) – and also currently leads MBIE project RAs in UOAX1810 Decision Automation for Orchards and Vineyards, SfTI Adaptive Learning Robots, SfTI Precision Farming Technology for Aquaculture, MBIE Singapore-NZ Bridging the Gap Between Remote Sensing and Tree Modelling with Data Science



In 2021 Prof. Green won a $10 million five year MBIE grant for “Enabling unmanned aerial vehicles (drones) to use tools in complex dynamic environments.” This exciting project involves several other Universities including Auckland, Victoria and Lincoln University as well as overseas researchers and many industries.

In 2023 Professor Richard Green also won a $6 million five year MBIE grant for a project “Predicting the unseen: a new method for accurate yield estimation in viticulture/horticulture.” This is known as the “Occlusion Project” and involves Lincoln University and Lincoln Agritech as well as other universities and companies.

For a list of research that Professor Green is currently involved with, please visit the UC Vision Research page (https://ucvision.org.nz/research/) for a comprehensive list which includes everything from blood spatter pattern analysis to using autonomous audio lures to attract pests.

Other funded projects he has led in the past include:

Maaratech - a $3 million five year MBIE research project which has successfully developed an intelligent vision-based pruning
Biosecurity: AUVs autonomously inspecting ship hulls for biofouling. Monitoring bees (hi res cameras)
Scion/Tait: UAVs mapping tree locations and mapping burnt regions from rural fires
Unison: UAVs tracking/inspecting conductors and vegetation encroachment
Schneider Electric: Next generation smoke alarms (classify smoke/steam/etc)
NEC: face recognition for session based surveillance
NZ Sport HP: Recognise and quantify skills
Biomedical: gait analysis of movement disorders
Dynamic Controls: Autonomous wheelchairs
Motim: Computer vision based mobile phone corporate applications
Tait: Mobile device support of safety & efficiency for emergency services
Gesture Interactions with huge public displays