Azure Internet of Things Garden Project
I want to showcase a project I have been working on to reflect the interesting things you can do with Internet of Things (IoT) and Azure services at home that could be commercialised into an industrialised environment.
My latest project is to utilise Microsoft IoT in my garden, not only to provide basic monitoring but with scope to take it to the next level. I utilised Azure Machine Learning and Azure Services to create a complex reactive ecosystem controlled through cloud services and voice commands.
The mini-farm environment for this project includes two plots of garden beds, a worm farm and a chicken house. The main goal is for the IoT solution to monitor the environment and control the water and feeding systems for the plants and animals.
The Chicken Coup:
The small chicken coup for two to four chickens will require automatic watering and feeding systems. The watering system is connected to the mains supply and will refresh the watering outlet of the coup daily.
The feeding system is a 10-litre container with food pellets. The dispensing system is an Auger connected to a servo motor to feed pellets into the feeding tray daily. The level of chicken food in the 10-litre container is monitored through an ultrasonic system measuring the height of the food in the main chamber. The IoT system will monitor for enough food in the main reservoir and alert when it needs attention.
The Worm Farm:
Worms are susceptible to heat. When it gets too hot in the worm farm it may impact on the health of the worms. The project calls for monitoring the temperature of the worm farm and to send and record telemetric data to Azure cloud for storage and alerts. If excessive temperatures are measured, then Azure event systems will send instructions to the IoT device to turn on a watering system to cool down and moisten the worm farm to keep the environment cooler.
The Garden Plot:
Typical watering systems on the market today will measure moisture levels and turn on the sprinkler systems without any measurement or logical analysis behind the process.
I wanted to do something upscale so I created an IoT solution that will send telemetric data to Azure for recording long term statistics for moisture levels at the surface and underground at 10 cm. Also, to determine evaporation rates, water usage, fertiliser usage, etc.
The IoT device will monitor the environment and send telemetric data to Azure storage. Environmental measurements of temperature, humidity, light levels, soil moisture levels, water flow in LPM is sent every few minutes to Azure IoT HUB for processing.
The statistics are measured for real time alerts and stored for long term trend analysis. The evaporation rates and temperature trends are of importance because it reflects on Soil moisture health and water-retaining ability and it will impact plant growth and water usage.
Power BI is used to provide a historical graphical representation of soil moisture and evaporation rates.
Azure Machine Learning:
The IoT device sends to Azure blob storage a small telemetric message every minute. That is over 500,000 status updates a year. From all this data the goal is to determine evaporation deltas and use predictive algorithms to determine the minimum water requirements required in the current environment. The water requirements will change during the hot seasons, milder seasons and plant maturity.
I will use Azure machine learning to model an algorithm to determine evaporation rates and use reinforcement learning to determine the most efficient watering times and watering duration.
The Azure Machine learning service will send web service commands to the IoT device to control the watering system.
Just to show off I have enabled Google Home assistant to turn on and off the watering system via voice commands. "Hey Google, water the garden." The sprinklers will turn on for 10 minutes.
Figure 1: The Farm Workspace
Here is the solution on the workbench. You can see the water flow meter and water On/Off solenoids, Batteries and the Arduino MBK NB 1500 IoT device. In Part 2 I will show you how it was put together.