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.