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My Azure watering system has been running for almost seven month now, after a long period of analysis I got all elements stable early january 2018.
The Azure data recorded since then shows the following behavior:
The top graph indicates watering time in value of 100ms, big bars correspond to manual watering.
Bottom graph shows daily measurement for three pots (Red, Green, Blue), plus local temperature (Orange).
We can draw some conclusions from this graph:
1 - Plants don't need much water in winter...
2 - Humidity measurement is definitely correlated to Temperature (red pot got frozen in march)
3 - Each pot has a specific behavior, and therefore needs a special algorithm
It is now worth pushing this data to Azure Machine Learning to draw the watering laws - before summer !!
My watering system monitored withAzure is now fully operational for three plants.
The connected computer is an Educake from ICOP, it measures three sensors and is able to pump water individually in each pot.
The system is connected to a timer socket progammed to wake up once a day. The information reported to Azure by the Educake contains humidity and watering time for each pot.
This configuration has been running for a week and Azure data allows me to calibrate the watering parameters. Unplanted sensor (dry) gives a 1024 value, and fully dipped sensor gives a 400 value.
As I'm checking humidity, I decide to reverse the scale : 600 = watered - 0 = dry, one week of recording shows the following graph:
Now Let's go to vacation!...
Re: Azure Sphere - Hands on!!
-- David Jones
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