Challenges
QR-codes recognition with the fewest number of cameras.
Achieving high scanning speed.
QR-codes backlighting using LED.
OTA firmware updates through mobile applications.
Our Approach
To achieve efficient QR code recognition, we utilized a Raspberry Pi 3 Model B with an Arducam Multi-Camera Adapter. This setup allows for multiple camera connections and optimized scanning performance.
We implemented a transistor switch to control the LED strip for QR code backlighting, ensuring efficient energy usage.
Our electrical engineering team developed a custom battery gauge and power PSB.
To minimize device size, the mechanical engineering team incorporated mirrors.
The system employs multithreading and async handling.
Scanned data is transmitted to the mobile application via BL connection and subsequently uploaded to the cloud for centralized management.
What Our Team Says
This project was exciting because it applied our expertise to a new field — agriculture. It's motivating to see your skills make a real-world impact. We did deep research, analysed and studied multiple articles to ensure the device is efficient, sustainable, and improves the workflow for farmworkers.
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