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High resolution health assessment of Antarctic plants as climate changes. Declines in terrestrial ecosystem health as a result of a drying climate have been observed in some areas of East Antarctica. This project aims to determine if such changes are widespread. Since mosses, the dominant plants of Antarctica, preserve a record of past climate down their shoots they can be used as surrogates to study how both ecosystems and climate are changing at remote polar sites. Outcomes will include improv ....High resolution health assessment of Antarctic plants as climate changes. Declines in terrestrial ecosystem health as a result of a drying climate have been observed in some areas of East Antarctica. This project aims to determine if such changes are widespread. Since mosses, the dominant plants of Antarctica, preserve a record of past climate down their shoots they can be used as surrogates to study how both ecosystems and climate are changing at remote polar sites. Outcomes will include improved climate data for Antarctica, enabling more robust analysis of regional climate change, and development of ultrahigh-resolution techniques capable of non-destructively monitoring Antarctic ecosystem health. This research will advance ecosystem science and inform best practice in management of Antarctic biodiversity.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.Read moreRead less
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less