Wanted to combine passion for Canadian wildlife, biological research and conservational efforts to make a tool that can help researchers and nature enthusiasts alike!

What it does

Provides platform for nature enthusiasts to aid in conservational efforts using just their smartphone camera. provides a series of machine-learning libraries to assist users in identifying the animals before their eyes. Users use their smartphone to snap pictures of wildlife they spot; findings are then automatically identified and catalogued, or if necessary overriden by the user for a further increase in collection accuracy. Spotted animals can be catalogued by users which contribute to the overall collection of local habitat knowledge generated by the app.

How we built it

Developed a series of custom AI training models using Clarifai's API to form calculated predictions of the photographed animal. We used Android's XML layout to form intuitive frontends, seamless Maps API integration to add a geographical basis to spottings, and finally a way to save your contributions on a Firebase server.

Challenges we ran into

Interacting with Clarifai's Java API was helpful yet challenging due to our team needing an asynchronous workaround. Handling Android permissions was also a challenge due to the ever-increasing prompts given to the user.

Accomplishments that we're proud of

  • Successfully implementing machine-learning to identify animals
  • Learned about the value of asynchronous vs. synchronous methods
  • Happy to contribute to conserving Canadian wildlife

What we learned

Learned the value of taking time to write down team goals, planning/allocating workloads and not being afraid to ask for help from coding experts

What's next for

There's never too big a training model!

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