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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.
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.
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.
Links:
[1] mailto:U4420081@anu.edu.au
[2] http://legacy.crcplantbiosecurity.com.au/content/khuwuthyakorn