Koic is a connected scarecrow which is equipped with cameras that film the terrain in real time.
The filmed images are then analysed. Depending on the animal that is hit, the scarecrow will choose a suitable way to repel it in a natural way.
You have to clone the repo on the Raspberry Pi and your computer.
git clone git@github.com:PoCInnovation/Koic.git
- Node
- Yarn
- Docker
- Docker-compose
- Have a Raspberry Pi and its camera module (PiCamera)
The Expo App will load the build bundle of the Expo CLI and allow you to test our app without deploying it or building with Android Studio.
Download links:
If you are new to raspberry I advise you to follow this tutorial:
Open the raspi-config
tool from the terminal:
sudo raspi-config
Select Interfacing Options
then Camera
and press Enter
. Choose Yes
then Ok
. Go to Finish
and youโll be prompted to reboot.
Your raspberry pi and your computer must be on the same network.
Get your ip address like this:
hostname -I | cut -d' ' -f1
Once you have the address, establish the ssh connection with your raspberry pi:
ssh pi@<ip_address_raspberry_pi>
- Run launch programm
./launch.sh
- Create Kafka Cluster on http://localhost:9000/addCluster
- Run Kafka Producer (raspberry pi or on your computer)
- Run Mobile-App
REPLACE_BY_YOUR_IP
by your computer address ip in:
- koic-app/.env
- RPIProducer/manager.py
./launch.sh
Create kafka cluster on your http://localhost:9000/addCluster
And save.Check that the above steps have been carried out.
If you would test without pi run his command:
./RPIProducer/tests/fake_producer.py
Run the kafka producer on raspberry pi:
Put the script on raspberry pi with this command:
./rpi.sh {ip_of_your_raspi}
The programm was copied in Dowloads
folder
# Kafka Producer / on rasberry pi
python3 run.py
In koic-app/.env change ip by your ip address.
cd koic-app/
npm install --global expo-cli
yarn install
yarn start
Scan the QR code
displayed in your terminal with your phone
The application is divided by a tab that allows you to navigate between the different features like :
-
Be able to view the activity on the field in real time thanks to the cameras placed on it.
-
View and graph the data collected and analyse the behaviour of the pests.
-
Make adjustments to the entire application and the cameras.
-
Report a problem and able to contact support.
- Cameras that can film the field in real time and send information that is processed by an API and sent to the application
Thomas Michel |
Inรจs Maaroufi |
Coline Seguret |
---|
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