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Wild life SOS

💡 Inspiration

We all have heard about bushfires that happened in Australia a year ago. According to news sources, more than 60k koalas were killed or hurt in the 2020 bushfire alone. The worst losses were on Kangaroo Island in South Australia, where the conservation group estimates more than 41,000 koalas were killed or harmed by the ferocious fires.

Casualties like these happen every year. Many times people even don't know how to help animals and there is a slight possibility that they might give something wrong to the animal in distress. Every animal has different needs and we should try to cater to that. To solve this problem we came up with Wild life SOS where users can learn how to help the animals with just one click. On our website, you can donate money using Coil to support animal rescue and adoption. On Wild life SOS you can identify the animal and their injury and it will tell you what you should do and should not to help the distressed animals. This application was inspired by animals and all humane people worldwide who were unable to help animals because of a lack of knowledge.

💻 What it does

Identifying the animals
Identifying the degree of injury and distress.
First aid required for these animals.
Donation for animal rescue and adoption with the help of coil.

⚙️ How we built it

ML: Python, MATLAB
Frontend: React Js
Web monetization: Coil
Styling: Tailwind CSS

💲 Best Web Monetization Project

We also use Coil to create micropayments for the user to support animal rescue and adoption. The coil is a free, open-source, and open-source-compliant web monetization platform that allows you to monetize your website for a small fee.

🤖 Best Use of MATLAB

We used MATLAB to build the image classification model. The model was trained on the dataset of various images of various animals and their injured status. The model was used to predict the injured status of an animal, and also to identify the animal in an image. MATLAB helped us in the following ways:

We used the MATLAB image processing toolbox to read the images and convert them to grayscale.
We used the MATLAB neural network toolbox to train the model.
We used the MATLAB statistics toolbox to evaluate the model.

🧠 Challenges we ran into

Using MATLAB to build the model was a challenge.
Completing the project under the given time frame.
It was very difficult to get the dataset for the project, and we had to contact various organizations to collect the data.