Bird_Up! 🐓
To protect our chickens!
Inspiration ✨
People eat a lot of eggs, in fact, according to statistics in 2019, consumption of eggs in the United States was estimated at 293.6 eggs per person. Thus on average, a person spends about 411$ per year on eggs as the average price was 1.4$ in 2019.
Due to inflation, many may choose to raise their own chickens. However, that requires commitment, we aim to help automate this process.
What it does 🗿
Automatic light sensor door 🚪
No longer do you have to get up early in the mornings, or panic when you are away from home and you forgot to lock your chickens up for the night. The automatic light sensor detects when the sun goes up and down ensuring your birds sleep sound.
Remote viewing of chickens 🐔
You can remotely connect to your chicken coop and move the camera as you need to make sure all your lovely ladies are doing fine.
Predator Detection 🦊
Chickens are often prone to predators but using machine learning we can detect exactly where a predator is and fire our patented predator cannon to keep predators away.
How we built it 🛠
We built a basic structure prototype using Lego Mindstorms and electrical tape to hold things together.
To power our vision processing we used a Jetson Nano which has enough built-in computer power to stream our raspberry pi camera and process an object detection model in real-time.
For controlling motors and sensors we used an Arduino Nano. We remotely collaborated on the Arduino code using a web IDE found here: https://wokwi.com/arduino/projects/320888181826257490
For the camera mount, we have two servos controlled manually using a joystick or can be controlled automatically moving towards predators using the GPIO output from the Jetson Nano (currently in development). The automatic door uses a photoresistor and a servo to open the pen when the sun comes up and closes it when the sun goes down.
How to run 💻
- Setup arduino as per pin diagram found on
- Object Dection Image Stream:
- pip3 install -r requirements.txt
- python3 ~/Bird_Up/yolo/live_yolo_opencv.py
- ROS Image Stream:
- install ros melodic
- install tmuxinator
- tmuxinator start jetbot_stack -n jetbot_stack -p ~/Bird_Up/workspace/scripts/stack.yml
Challenges we ran into 🎢
We weren't able to interact with the hardware equipment in person which led to real limitations. We tackled this by using an SSH server to connect to the jetson nano and this allowed us to access the camera to test the object detection model. Furthermore, we remotely collaborated on the Arduino code using a web IDE found here
Quick communication was also one of the challenges as we had to reboot the server a couple of times so we were always active on discord and ensured we rebooted the server when no one was in the midst of coding.
Accomplishments that we're proud of 🎇
This was a hardware project in a fully online setting so we are really proud of being able to procure the parts needed and assemble them in quick time as well as incorporating, testing, and debugging the object detection model.
What we learned 📑
We learned how to use OpenCV and CUDA it was a steep learning curve as we were not familiar with it.
What's next for Bird_Up! 🎯
We are planning on adding more predator and hazards classes to our model to make the system more robust. Additional features as heat control and egg detection would also be useful!
Before
After
Log in or sign up for Devpost to join the conversation.