EyeCrop

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Raspberry Pi

Inspiration

We wanted to grow small fruits like raspberries and strawberries in our backyard, but birds and rodents kept eating our crops before we could harvest them. Frustrated by the loss, we decided to create a solution that combines technology and gardening to protect homegrown food.

What It Does

EyeCrop uses a Raspberry Pi with an AI-powered camera to detect small animals like birds and rodents in real-time. When an animal is detected, the system plays a sound to scare them away. Additionally, a web app allows users to monitor their backyard remotely, view detection statistics, and track activity over time.

How We Built It

Hardware: A Raspberry Pi with a camera module forms the core of the system.
AI Model: We used a model that worked on the IMX500 Camera to recognize small animals using a dataset of images.
Web App: Built with a simple, user-friendly interface, the app displays real-time footage, detection logs, and analytics.
Integration: The Raspberry Pi sends data to the web app via a local server, enabling remote monitoring.
3D Printing: One of us had a 3D printer laying around, made a simple model and printed it. Took a multiple tries to have a result we were happy with.

Challenges We Ran Into

Peripherals: Integrating the Bluetooth and AI Camera was a challenge in terms of hardware. Writing the software for the main while loop live server was difficult to optimize.
Latency: Reducing delay between detection and sound playback required optimizing the system.
User Experience: Designing a web app that was both functional and easy to use took several iterations.
Networking: Getting a stable connection was difficult that all devices can connect to. We had to bring our own router.
3D Printing: Getting the final container took multiple attempts due to small issues with the printer.

Accomplishments We're Proud Of

Successfully integrating hardware, AI, and software into a cohesive system.
Creating a full prototype of a solution that is affordable, energy-efficient, and accessible for home gardeners.
Building a web app that provides meaningful insights without overwhelming the user.

What We Learned

The importance of balancing simplicity and functionality in tech solutions.
How AI can be applied to solve real-world, everyday problems.
The challenges of sustainable food production and the role technology can play in addressing them.

What's Next for EyeCrop

Expanding the AI model to detect more types of animals and pests.
Adding features like weather monitoring and crop health tracking to the web app.
Exploring partnerships with local gardening communities to make EyeCrop more widely available.
Developing a mobile app for even more convenient monitoring and control.

EyeCrop is just the beginning of our journey to make homegrown food production smarter, easier, and more sustainable.