Eco Lens

The user interface
Uploading the details and fetching the data
The image dataset from which the data was retrieved
The resultant details

Inspiration

The inspiration behind Eco Lens was the urgent need to address the critical issue of endangered keystone species. The decline of these species poses a threat to entire ecosystems. We wanted to harness technology to bridge the gap between public awareness and conservation efforts. By combining HTML, CSS, JavaScript, Flask, and Python, we aimed to create an accessible platform that empowers users to contribute to the preservation of rare species.

What it does

Eco Lens is a web application designed to facilitate user engagement in species conservation. Users can upload images of endangered keystone species they've encountered, along with their location data. The integrated AI model utilizes feature extraction and cosine similarity to compare uploaded images with those in the dataset. When a match is found, the AI extracts comprehensive species details from the dataset and presents them to the user. This process not only educates users but also equips them to report sightings to organizations dedicated to species conservation.

How we built it

Eco Lens was developed using a stack that included HTML, CSS, and JavaScript for the frontend, and Flask and Python for the backend AI model. The frontend components provided an intuitive interface for users to upload images and share location information. The Python AI model, leveraging the Flask framework, performed feature extraction and cosine similarity calculations. The integration of the AI model with the frontend was achieved through Flask.

Challenges we ran into

Integrating the frontend and backend components smoothly required addressing Cross-Origin Resource Sharing (CORS) issues. We had to ensure that the Flask backend and the frontend could communicate effectively. Implementing the AI model for feature extraction and similarity calculations demanded preprocessing, integration, and rigorous testing to ensure accurate results. Managing the integration of multiple technologies was a challenge we navigated during the development process.

Accomplishments that we're proud of

We take pride in successfully creating a functional and user-friendly platform that empowers individuals to actively participate in species conservation. The seamless integration of frontend and backend technologies allowed for a smooth user experience. Our AI model's ability to accurately match images and provide detailed information showcases the potential of technology to drive meaningful environmental impact.

What we learned

Throughout the project, we gained insights into frontend development, backend integration, and AI modeling. We deepened our understanding of feature extraction, cosine similarity calculations, and API communication. Additionally, we recognized the importance of collaboration and interdisciplinary skills in tackling complex real-world challenges.

What's next for Eco Lens

Looking ahead, we envision expanding the Eco Lens dataset to encompass a wider range of species, enhancing the AI model's recognition capabilities. Collaborating with conservation organizations to contribute to real-world efforts is a key goal. Moreover, we're considering implementing advanced AI techniques, such as machine learning, to predict species migration patterns based on user-provided location data, thereby enriching ecological research. Eco Lens exemplifies the harmonious convergence of technology, public engagement, and ecological preservation. By fostering a deeper connection between individuals and their environment, we believe Eco Lens can drive positive change and contribute to the broader mission of species conservation.