New technologies for detecting, counting and identifying pollinators in the field

Summary

This project will leverage advances in image processing and machine learning to develop new techniques for high throughput sampling of pollinator populations in crops.  
The supervisory team for this project also includes Dr Chuong Nguyen (Data61) and Dr Sarina Macfadyen (CSIRO). 
A complimentary scholarship for this project is available. To find out more, refer to the Postgraduate Research Stipend and Supplementary Scholarship in Digital Agriculture Data61.

Supervisor(s)

Dr Tanya Latty

Research Location

School of Life and Environmental Sciences

Program Type

PHD

Synopsis

Our goal is to produce an inexpensive system that would allow researchers and growers to automatically identify and quantify floral visitors. There are ~2200 species of native bee in Australia alone, and an estimated 30,000 species of fly, many of which are pollinators. Potential avenues for automated pollinator counting and identification include (but are not limited to) one or a combination of the following: acoustic cues from wing beat frequencies, using machine vision to classify images from camera traps, thermal imaging to identify pollinators by their heat signatures, use of scanning polarised LIDAR to detect species-specific wing beat frequencies, and/ or machine vision to identify pollinators from videos using species-specific flight patterns. Depending on collaborator/student interest, we can explore any or all of these potential techniques.

Additional Information

The supervisory team for this project also includes Dr Chuong Nguyen (Data61) and Dr Sarina Macfadyen (CSIRO).
A complimentary scholarship for this project is available. To find out more, refer to the Postgraduate Research Stipend and Supplementary Scholarship in Digital Agriculture Data61.

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include: 

  • Confidential disclosure and registration of a disability that may hinder your performance in your degree; 
  • Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.); 
  • Ability to perform independently and/or with minimal supervision; 
  • Ability to undertake certain physical tasks (e.g. heavy lifting); 
  • Ability to undertake observatory, sensory and communication tasks; 
  • Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden); 
  • Ability to work in confined spaces or at heights; 
  • Ability to operate heavy machinery (e.g. farming equipment); 
  • Hold or acquire an Australian driver’s licence; 
  • Hold a current scuba diving license; 
  • Hold a current Working with Children Check; 
  • Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.) 
You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

Want to find out more?

Contact us to find out what’s involved in applying for a PhD. Domestic students and International students

Contact Research Expert to find out more about participating in this opportunity.

Browse for other opportunities within the School of Life and Environmental Sciences .

Keywords

digital, agriculture, Data61, CSIRO, Life and Environmental, pollination, swarm intelligence, social insects, native bees, honey bees, ants, entomology, applied entomology

Opportunity ID

The opportunity ID for this research opportunity is: 2353

Other opportunities with Dr Tanya Latty