Critterpedia and CSIRO's Data61

The Critterpedia platform uses an algorithmic solution to identify the species of spider or snake submitted by users.

‘Is it venomous?’ is arguably one of the most common questions asked in Australia, home to 170 species of snakes and 2,000 species of spider.

Parents of two and with regularly visiting British relatives, Nic and Murray Scarce know the benefit of being able to accurately identify some of Australia’s most notorious wildlife.

“During one of her trips to Australia, my mother-in-law acted as a magnet for all of our country’s big-name snakes, spiders and insects,” jokes Murray. “The questions relating to their identification and danger levels were relentless, and the fact that we didn’t have all the answers simply exacerbated the situation.”

After a lengthy online search failed to provide all of the necessary facts in one easily accessible place, the idea to create an instant creature identification app was born, much to the relief of a particular visitor.

Dubbed Critterpedia, the platform will enable the user to take a photo of a snake or spider from any smart device before a trained algorithmic system classifies it, providing information on the family, genus or species.

One of the images used to train Critterpedia’s machine learning system | Tiger snake – Notechis scutatus | Photo by Adam Brice

To do this, the platform required hundreds of thousands of images of snakes and spiders to be fed into the system to accurately establish Australia’s plethora of species, a sizeable and manual task uniquely suited to an artificially intelligent (AI) solution.

A collaboration with CSIRO’s Data61 is seeing the construction of a machine learning engine for automated species identification embedded within the Critterpedia app to rapidly and seamlessly identify, sort, and certify large volumes of complex information.

“The visual differences between two species can sometimes be quite subtle, and so a great deal of training data is needed to adequately identify critters,” explains project lead and Data61 researcher, Dr Matt Adcock.

“We’ve started off with an enormous amount of images sourced from zoological experts collaborating with Critterpedia, and have developed a suite of tools to help semi-automatically label these images, verify the information, and cross check with other data sources.”

“The AI platform we are developing for the Critterpedia system considers not only these images, but also additional information, such as GPS location.”

One of the images used to train Critterpedia’s machine learning system | Sydney Funnel-web – Atrax Robustus (Male) | Photo by Scott Johnson, The Funnel-web Hunter (Sydney)

Critterpedia users can contribute to this system by opting to submit their photos to further train the machine learning engine, with the more differences and variables provided ensuring the robustness of the identification and information system.

“The intent is to form (consensual) user generated images into datasets of all animals and to extend our AI training with the team to eventually include many more species,” says Nic.

This adoption of AI ensures Critterpedia’s search engine and recognition system is maximised for accuracy, a critical consideration when it comes to pinpointing the finer differences between species which may or may not be deadly.

As a wildlife safety, awareness and education hub, Critterpedia aims to provide better education and awareness for all Australians across a number of sectors and could ultimately save human and animal lives.

“By utilising disruptive, top-end technology, Critterpedia can guide people to gain a deeper understanding of our misunderstood wildlife by providing the tools and experiences they need for a great education, the key to positive change,” explains Nic.

You can currently sign up to become a Phase 1 tester, which lets you download a beta version of the platform and submit wildlife photos to keep training the algorithm.

The company were introduced to Data61 researchers through our CSIRO KickStart team, who assist start-ups find research and development (R&D) capabilities while providing dollar-matched funding for projects. According to Murray, incorporating ground-breaking technology and data science has been instrumental to this project.

One of the images used to train Critterpedia’s machine learning system | Red-headed Mouse Spider – Missulena granulosa (male) | Photo by Paul Irvine | PWI Photography

Critterpedia plans to expand their dataset and use the AI-powered platform, also incorporating other exciting new technological capabilities, to share safety and educational tools to train individuals, businesses and government on how to coexist with Australian wildlife, especially the venomous variety.

“The problems that the environment is currently facing leaves you with a real desire to engineer solutions,” explains Murray.

“Educating people on our wildlife in a fun, and interactive way, especially focussing on our venomous friends, and delving into the reasons as to why people harbour so many fears, is the key to delivering a platform that can really make a difference to peoples’ lives.”

“Critterpedia can create a world where people of all ages, backgrounds and status can appreciate and respect our environment, and where we and animals can peacefully coexist.”


The Critterpedia team have been fortunate enough to align with CSIRO/Data61, Ignite Alliance, SingularityU, Josephmark, Slipsteam Commercialisation, Advance Queensland, and Startup Onramp, who have guided them through their journey so far. Also, with a special thanks to their passionate snake and spider advisors, including Robert Whyte, Scott Johnson, Narelle Murphy, Ben Shoard, Kane Durrant and Scott and Tie Eipper, their business mentor, Lewe Atkinson, and business confidant, Megan Avard (SurePact). Not to mention the 30 incredible snake and spider experts, including
Iain Macaulay, Paul Irvine, Adam Brice, Mick Fullerton and John and Tina Mostyn, who have shared their images to aid in the training of the machine learning AI algorithm.