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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 !!