FloraFy

Inspiration: The degradation of 100 million hectares of productive land from 2015-2019 urged us to develop FloraFy, addressing the urgent call to combat biodiversity loss and land degradation outlined in the UN Sustainable Development Goals.

What it does: FloraFy employs AI and image recognition, trained on a diverse dataset, to instantly identify flowers from user-submitted images. Users can mark the flower's location on a global map using the Google Maps API, creating a valuable biodiversity database accessible to all.

How we built it: We built FloraFy by training our image recognition model using a comprehensive dataset. The integration with Google Maps API enabled seamless mapping functionalities, allowing users to geotag their flower discoveries.

Challenges we ran into: A significant challenge was testing the model during its training phase, leading to extended waiting times. Balancing accuracy and efficiency was crucial to delivering a seamless user experience.

Accomplishments that we're proud of: We're proud of creating FloraFy, a tool that bridges technology and environmental conservation. Building a functional system integrating AI and geolocation services was a significant achievement, fostering community engagement in preserving biodiversity.

What we learned: Developing FloraFy taught us the complexities of training AI models for real-time applications. We gained valuable insights into optimizing model accuracy and learned to navigate challenges associated with integrating external APIs for interactive user experiences.

What's next for FloraFy: In the future, we aim to enhance FloraFy by refining the AI model for even faster and more accurate flower identification. We plan to expand our database, collaborate with botanists for expert validation, and incorporate more geospatial features. Additionally, we aspire to create educational outreach programs, fostering a deeper understanding of biodiversity conservation.