Inspiration

Our inspiration for this project is the many endangered species and the many species that became extinct due to poor management and care. And also in the biologists, caretakers, and scientists who are in charge of caring, protecting, and studying endangered animals, and that's why we want to give them more tools to do a better job. Because of this, we felt the need to create an all-in-one app that would detect endangered species, their status, location, alerts, and much more to protect them and their environment.

What it does

AnimalAID gives biologists, zookeepers, and even ordinary people a way to help endangered species. Our app allows you to manage, monitor, and alert research and protection teams about your protected animals, automating tasks, saving time, and much more. From monitoring an animal's movement patterns to alerting that an observation point is in trouble. This way we prevent accidents, give greater control, and most importantly help wildlife and captive animals to continue to be protected in a better way.

Features

  1. SPA with multiple users authentication powered by Netlify and Firebase auth.
  2. Animals management system powered by Firebase Firestore.
  3. An automatic alert system powered by Twilio.
  4. An AI-powered computer vision model for detect animals patterns and analytics powered by Vertex AI and GCP Compute Engine.
  5. An animal tracking map powered by Google Maps.

How we built it

The main part of the project (the object detection model) was trained using Vertex AI's GCP notebooks, which made it way easier for us to train and evaluate if our Tensorflow model was working correctly. We also used Google Maps for the Map section on our website. The Tensorflow model also sends notifications with Twilio to all the registered numbers whenever it sees something dangerous happen to the animals, especially for places where the internet connection is not stable, like jungles and forests, this will alert all caretakers to things like hardware problems alerts, animal sightings and more. The frontend was built using React Redux and the authentication was done with Firebase.

Accomplishments that we're proud of

Getting the custom object detection model to work was one of the most challenging tasks that we overcame. We are also proud of implementing the Map section on our web application with Google Maps.

What we learned

Getting the custom object detection model to work was one of the most challenging tasks that we overcame. We are also proud of implementing the Map section on our web application with Google Maps.

What's next for AnimalAID

We believe that this application can help improve the animal care and review process, improve scientific research, help protect animals and their environment, and have better research results.

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