Smart Irrigation: integrating UAV soil moisture maps & variable rate sprays. This project will develop a state-of-the-art precision irrigation system for optimising water use and crop yield. Specifically, a novel UAV soil moisture mapping system based on passive microwave satellite remote sensing technology at L-band will be developed for near-surface soil moisture mapping at accuracies and spatial scales currently not attainable. These soil moisture maps will then be merged with irrigation wate ....Smart Irrigation: integrating UAV soil moisture maps & variable rate sprays. This project will develop a state-of-the-art precision irrigation system for optimising water use and crop yield. Specifically, a novel UAV soil moisture mapping system based on passive microwave satellite remote sensing technology at L-band will be developed for near-surface soil moisture mapping at accuracies and spatial scales currently not attainable. These soil moisture maps will then be merged with irrigation water delivery models to calibrate for spatial variation in soil properties and/or correct errors in spatial variation of rainfall and evapotranspiration inputs. Ultimately the water balance predictions will be used for implementation of variable rate irrigation control at scales hitherto unattainable.Read moreRead less
Improving the diagnosticity of eyewitness memory choices. Eyewitness identification error is common and costly. This project aims to improve the quality of information provided by eyewitnesses, and the ability of police officers and triers of fact (e.g., juries, judges) to evaluate this information. Laboratory investigations will determine how best to test memory and confidence to achieve this aim. A new class of cognitive models will provide a unified account of response accuracy, response time ....Improving the diagnosticity of eyewitness memory choices. Eyewitness identification error is common and costly. This project aims to improve the quality of information provided by eyewitnesses, and the ability of police officers and triers of fact (e.g., juries, judges) to evaluate this information. Laboratory investigations will determine how best to test memory and confidence to achieve this aim. A new class of cognitive models will provide a unified account of response accuracy, response time, and confidence, suitable for application to computerized testing scenarios. The models and testing methods validated in the laboratory will be refined for application in eyewitness memory settings, facilitating better evaluation of identification evidence, and potentially reducing wrongful convictions.Read moreRead less
Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultu ....Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultural issues. This project is expected to benefit industries with widely distributed multi-cultural workforces such as mining, defence and medicine.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
Three-dimensional Bayesian Modelling of Geological and Geophysical data. The project aims to develop technologies enabling rapid informed decision-making related to the management of natural resources, including critical metals, copper and water. This new technology will support a greener future, securing our energy future, our access to clean water and reduce the mining footprint. Expected outcomes include an enhanced capability in interoperable, integrated three-dimensional geological and geop ....Three-dimensional Bayesian Modelling of Geological and Geophysical data. The project aims to develop technologies enabling rapid informed decision-making related to the management of natural resources, including critical metals, copper and water. This new technology will support a greener future, securing our energy future, our access to clean water and reduce the mining footprint. Expected outcomes include an enhanced capability in interoperable, integrated three-dimensional geological and geophysical modelling in order to predictively characterise sub-surface geology. The outcome will be an open-source forecasting dashboard enabling decision making while considering underlying risk related to resource extractions and management with significant benefits to the Australian society (lower emissions, clean water).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