Species extinction rates are at an all-time high in modern times and natural scientists are faced with the challenge of needing to rapidly increase their efforts to gather reliable ecosystem information at broader scales in order to mitigate threats.

Traditional methods of collecting ecological data can often be time consuming, invasive and can alter the natural habitat of the study site.

With this in mind, a James Cook University coastal ecology PhD student has developed an alternative scientific workflow to collect biological and ecological data using computer vision and machine learning to scale up data collection to required levels and improve its efficiency and utility. To do so, he used a node on QRIScloud, QCIF’s cloud, specially designed for machine learning work.

When Dr Kay Ayre* was a special education researcher at the University of Southern Queensland (USQ) she would carry a portable hard drive to regularly back up her files of data.

“Doing this was quite unsettling,” she said, “because there was always the risk of the drive being lost, left somewhere or left at home when needed at work or vice versa. Plus, it was easy to forget to back up regularly and that could be problematic.”

Fortunately, she attended an information session at USQ in which QCIF’s USQ-based eResearch Analyst Dr Francis Gacenga introduced academic staff to QRIScloud, QCIF’s cloud computing service, and Nextcloud, an application designed to facilitate access, sharing and collaboration across data sets.

Through QCIF, a Griffith University renewable energy researcher has taken his work to the next level with all-important access to national high-performance computers.

Before contacting QCIF, Dr Yun Wang of Griffith’s Centre for Clean Environment and Energy was facing the very real possibility of having to stop computational research due to a lack of HPC resources. This would have been a major setback as supercomputers are essential to his research, using large-scale computations in order to understand the properties of complicated materials at the atomic level.

Thankfully, friends at the University of Queensland recommended Yun contact QCIF for help.

Summary

CQUniversity-developed FruitMaps is a data tool that takes real-time data from multiple sources and displays them visually to provide a simple, free, online decision support tool adapted for use by farmers to assess fruit maturity and assist harvesting planning.

The data collections that underpin the tool are stored on QRIScloud, QCIF’s cloud computing service.

FruitMaps’ pilot users, farm managers in Queensland and the Northern Territory, have welcomed the data tool to help minimise crop loss and plan for resources, such as labour, at peak times.

FruitMaps is currently being developed for other uses, such as assessing soil quality, and quality control of coral trout exported to China, and for use with other crops important to Australia’s economy, such as citrus, bananas and avocadoes.

Summary

CQUniversity-developed DataMuster is a groundbreaking animal monitoring Web application that links biology with technology, combining automated livestock management hardware, such as weighing systems, with cutting-edge software so that beef producers have a precise and real-time understanding of individual animal performance.

With DataMuster, farmers can make informed decisions to improve cattle genetics, cattle management and supply chain management.

After a two-year pilot trial involving six farms and Queensland’s Belmont Research Station, DataMuster is now being offered as a commercial service.

QRIScloud, QCIF’s cloud computing service, provides the server to run the app and hosts the data automatically collected from DataMuster-using farms.