Launching Smart Zone Settings ⚙️

Hey Rachio community :wave:. I’m James, a machine learning engineer at Rachio, and I’m excited to tell everyone about the first machine learning (ML) feature we’re launching today… Smart Zone Settings. The TLDR is that this feature will help optimize your controller settings for Flex schedules, keeping your lawn healthy while saving you water. Keep reading for more details on how the models work and how we measure water savings.

Advanced watering schedules like Flex Daily require a variety of lawn settings to operate efficiently. Some examples include soil type, crop type, root depth, crop coefficient, sun exposure, slope, and spray head type. These settings can be applied through the mobile app, but they’re often tricky to set accurately.

Naturally, three of the most vital settings for advanced schedules also happen to be the most challenging for users to set: soil type, root depth, and crop coefficient. Since these are the most difficult, we’ve chosen to optimize these three components for advanced schedules through our new ML models. The main purpose of these ML models is to apply smart settings for a newly registered controller without requiring users to do so, making for a streamlined user experience. (Note: users are still free to modify their lawn settings at any point in time; these ML generated Smart Zone Settings are merely the default values initially set at device startup time.)

Behind the scenes, we’ve trained ML models using millions of data points. The models consume the controller’s climate region and the zone’s crop type to deliver optimal predictions for each of the three components. These updated default settings are personalized and smarter than the previous defaults and according to testing, we’ve found they will save our users water without adversely affecting crop growth.

Evapotranspiration (ET) is the process by which water is transferred from the land to the atmosphere by evaporation from the soil and by transpiration from plants. We take into account ET and precipitation to determine how much additional irrigation a lawn needs to remain healthy. Using these historic measurements we can run an irrigation simulation across Rachio controllers that estimate how much water would’ve been irrigated if the Smart Zone Settings were applied. We compare those numbers to the observed irrigation numbers using the current lawn settings and see up to 15% in water savings for Flex schedules, depending on the month.

Based on this thorough evaluation, we’re confident that these machine learned Smart Zone Settings will not only remove the challenging step of initially tuning your Flex watering schedules, but will also yield water savings without hurting your crops.

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This is so cool James.

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This sounds amazing. Can’t wait to learn more about Smart Zone Settings and hopefully try it out soon!:partying_face:

Sounds great, anyway to see the suggested settings for those of us already using flex schedules? Would recreating the schedule set the defaults based on the new models?

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I messaged him with the same question @Gene. I might have to set up one of my old controllers and create a fake yard. I assume this will work on Gen1’s?

So how do we get these new settings?

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Would it be possible to back up our existing settings, and then try the new ones? Like you could flip a switch to go Smart, but if you unflipped it, your old settings where not lost.

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Same question about existing systems. Without Rachio addressing this, it feels more like a cash-grab than a new feature for the community. Does this only work with new controllers?

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Please include guidance from local authorities in the mix. With drought-like conditions returning every summer, the local authorities restrict use of water (on days and the amount to be applier). Happy to help if any more inputs are required.

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The easiest way to see how it generates new zone defaults is to pick a zone and change the crop type. The system will render new root zone depth and crop coefficient values. For initial controller onboarding you could see different soil types. Also want to call out that there were noticeable differences in different climate regions for annuals and garden crop type root zone depths, but for the other crop types in most locations the variance was about 5%-10%. Very much impactful in the larger picture but for individuals it might not be as noticeable. Excited to start rolling machine learning technology to other aspects of our system (i.e. scheduling and other impactful areas).

Hope this helps.

:cheers:

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Aha! Just changed the crop type from
Cool season grass and back- and now see the ML values. For my grass they seem reasonable.

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Same here @Kubisuro - I saw how the crop type changes but for this time of year isn’t crop for cool season grass should be 0.95

A BIG thanks for calling this Machine Learning, and not AI — we need to see more of this!

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I see nothing that says “crop type” when editing one of my zones… using latest IOS app with R3.
Do you mean ZONE TYPE??

I just played with it. Pretty sure @franz meant to say “Zone Type”

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If the team has started moving into machine learning does this mean “finish before sunrise” is no longer “hard” to fix? :slightly_smiling_face: :beers:

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Well played @scorp508, well played.

:cheers:

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