This project aims to integrate hardware IoT sensors (some RPi Zero's with I2C probes) and a Deep Neural Network to predict how much water an outdoor garden will need every day.
The ML Model uses the last 24hrs of collected weather data to make a prediction around mow much water needs to be dispensed to keep the plants alive.
This approach hopes to reduce the overall water use by preventing excessive watering.
The project uses three Raspberry Pi’s in this configuration to collect and store data. The Master and Satellite Pi nodes are in charge of collecting local garden data. The internet enabled master Pi runs an http server and collects data from the distant
satellite Pi via a LoRa command to return to the client. The DC Pi runs API calls every 60 minutes to the OpenWeather API and the master Pi to collect air temp and humidity, rain, wind, cloud and soil temp and humidity.
Every 24 hours, the master Pi requests the latest water prediction from the DC Pi, the DC Pi uses the latest collected data and a trained Machine Learning model to predict the last day’s watering needs.
For more information on the process flow, visit this project’s GitHub page.
The basic watering index can be calculated with a weighted equation considering all the factors affecting plant evapotranspiration we dropped soil humidity because the sensor was not working correctly: