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CRC60075: NICTA Smart Trap - PhD

The main goal of this project was to undertake research that will develop technically sound sample/survey methodologies and systems to enhance the ability to capture a wide range of plant health information in an accurate and cost-effective manner both domestically and internationally.

What is the biosecurity problem?

I aimed to create the necessary tools to significantly reduce the amount of human intervention, as required in present systems. The computational techniques will be required to recognise and identified EPPs in real-time on-board automatic insect traps. The use of imaging technologies based upon hyperspectral and UV ranges to develop a statistical and computational framework for the classification and identification of selected EPPs are challenges.

The main outputs of this project were to:

  1. devlop new, better understanding of shape/image descriptors suitable for spectral imagery specially designed for biosecurity surveillance systems
  2. create a set of kernel-based, statistical methods that can be used to perform classification of emergency plant pests making use of the descriptors developed in item 1, and
  3. develop a framework that will permit the extension of the classification methods developed in item 2 to include domain knowledge. This will involve the development of semi-supervised pattern recognition and interactive computer vision methods based upon statistics that are scalable and capable of real-time analysis.

Who are the end-users of this research?

Automatic and continuous monitoring capabilities of ‘smart traps' have a high potential for commercialisation, both nationally and internationally. Such technology will most likely be adopted by state agencies and plant-based industries involved in early warning networks for emergency plant pests to increase efficiency and reduce cost of monitoring early warning insect traps. Outcomes from this project may also be applicable to a range of biosecurity issues such as semi-automated surveillance systems in quarantine facilities, index databases, building of libraries for future reference, etc.


Ms Pattaraporn Khuwuthyakorn
Student CRC60075: NICTA Smart Trap - PhD

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Dr Antonio Robles-Kelly and Dr Jun Zhou (ANU) and Dr Louise Morin (CSIRO Entomology)
Supervising Institution
Australian National University
March 2008 – March 2011