WildBerryEye

WildBerryEye leverages Raspberry Pi and YOLO object detection models to monitor pollinizers like bees and hummingbirds visiting flowers. This initiative aims to enhance environmental research by automating data collection and analysis of pollinator activities, which are crucial for ecological assessments and conservation efforts. The project utilizes video data provided by Dr. Rossana Maguiña, processed through advanced machine learning techniques to accurately identify and track pollinator interactions in natural habitats.

Develop web-based user interface

• Topics: Full Stack Development React Flask
• Skills: Experience with full stack development and real time processing
• Difficulty: Moderate to Challenging
• Size: Medium or large (175 or 350 hrs)
• Mentors: Carlos Isaac Espinosa Ramirez

Develop a clean and intuitive web-based interface for WildBerryEye, ensuring ease of use for researchers and contributors. The platform should present real-time pollinator detection results, facilitate data visualization, and allow users to interact with system settings efficiently. The website must be accessible, visually appealing, and optimized for both desktop and mobile users, avoiding unnecessary complexity or intrusive elements.

Specific tasks:

• Frontend Development: Continue development to enhance the user interface using React and CSS, ensuring a responsive and user-friendly design.
• Backend Development: Expand functionality using Flask, focusing on efficient API endpoints and seamless interaction with the frontend (excluding database implementation).
• Real-Time Communication: Implement and refine real-time updates between the frontend and backend to enhance system responsiveness.
• Usability & Design Optimization: Research and propose improvements to the system’s usability, design, and overall user experience.