Installation • How To Use • License

The easiest way to run this project is with Docker. The included docker-compose file sets up a database and the Flask application. This will require Docker, and docker-compose to be installed. Once docker is installed, running

sudo docker-compose up --build

will build and run the Docker containers.

This can be run natively in the shell by hosting a PostgreSQL database and running the Flask app as normal, the environment variables SQLALCHEMY_DATABASE_URI, and SECRET_KEY should be set before running the application.

$ export SQLALCHEMY_DATABASE_URI=database_uri
$ export SECRET_KEY=very_secure_secret_key
$ python3 -m pip install -r requirements.txt
$ python3 app.py

After installing the requirements, the dashboard can be run. You will be greeted with an empty homescreen. Before starting the moisture sensors, you must add a plant to the dashboard.

Once the dashboard has been setup and a plant has been added, you may setup a moisture sensor.

from sensors import sensors

database_uri: str = 'postgresql:https://postgres:password@localhost/postgres'
test_sensor: sensors.TestSensor = sensors.TestSensor(database_uri)

plant_id: int = 1 # Plant ID can be seen on the dashboard
sensors.read_moisture(plant_id)

The above program can be seen here. It is an example of how the interface can be used to read sensor data. The TestSensor class is used to generate random moisture levels. There are several premade examples of moisture sensor classes, including the ExplorerHat class which is a nice introduction to using this project with Raspberry Pi. Adding a custom moisture sensor can be accomplished by subclassing sensors.sensors.BaseSensor.

The script for reading the moisture sensors can be run with a scheduling program like cron, it's recommended to read the moisture level once a day and water the plant when appropriate.

This is an open source project written almost entirely in Python3. If you would like to contribute, please feel free to clone this project or create an issue that I can look at.

When developing this project, I recommend running the Flask development server using flask run with the database in a Docker container. The start-database.sh script runs an empty PostgreSQL database in Docker which will then be populated when the flask development server is run.

MIT