Shroomnify

Model's output
Model's output
Model's output
Model's output
Model's output
Model's output
Model's output
Model's output
Model's output
Model's output
Model's output
Model's output
Model's output

Inspiration

Our team decided to do the topic of healthcare, then we were thinking about how we can survive in the jungle. Finally, we bought down to how we can eat mushrooms safely because not only do they have loads of nutrition but they also hard to tell whether it is poisonous or not.

What it does

It differentiates if the mushroom is poisonous or not and gives its name. Moreover, it gives information about how to handle the situation when you ate a poisonous mushroom.

How we built it

We use image processing to recognize the mushrooms using TensorFlow, OpenCV, and Numpy, and we also built a web using ReactJS and Firebase.

Challenges we ran into

The most challenging is the limited time which we have got only around 36 hours (not including sleep and rest time) so we try our best and work so hard to make our project perfectly done. Another challenge is learning new things. Most of our members are very new to website development so we need to keep some time for learning to do it. By the way, those challenges are kind of fun to do and they also improve our teamwork skill.

Accomplishments that we're proud of

We can finish our project and submitted it on time. Moreover, we have learn lots of new things during workshops and hackathons.

What we learned

We learn how to code a website and use Azure services from Microsoft. But, the most important thing we have learned is working with teammates.

What's next for Shroomnify

We think that if we have a chance to work on it more we might add more species of mushrooms and develop a website to be a mobile application because it is more comfortable to use and more varied. At last, we will improve the detection to be more accurate.