Lol, keep fighting the good fight @scorp508
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I thought I’d give this a go so I changed my zone type from cool season grass, to warm season grass, and back to cool season grass on all of my zones. I did see current soil moisture percentage points change a few points per zone after the flip/flop. For example if I was at 46% moisture it was something like 49-50% moisture after flipping from cool to warm to cool. It went up a small number of points in all zones.
Would all previous custom advanced settings be retained? I’m sort of curious if I should reset all of my zones to default other than zone type, spray head, soil type, exposure, and slop to see how the new smart zone settings pan out.
I’d like to know as more and more info is added will it auto adjust the settings over time should it make sense for it to do so?
I live in Tampa Fl and have St. Augustine grass. I just switched my zone type from warm season grass to cool season grass and back to warm season to see what the ML smart zone setting will change to. Originally I had my root depth set to 5 in. After changing zone type the ML setting set my root depth to 9.8 in. I think 9.8 is way too deep for St. Augustine root depth. Appreciate any input from any experience Florida landscapers or lawn care specialist.
Users will have more information specific to their lawns than our ML models do, so you should definitely use that local information to update your settings if you can. These ML models are not perfect, but they’ve proved better than our original default settings.
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If we adjust (re-train) the models to make them better as we get more information, we’ll make another announcement about it and similarly not silently apply the new settings but allow existing users to opt in.
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A couple questions
How would customers who don’t visit the forum know? Or even as someone who visits the forum how would I know which version of a ML’d model I’m using?
Maybe some in-app notifications “We’ve updated our models, go here to learn more or click here to apply new defaults.” along with some kind of revision # or date (applied vs most current) in the zone may be useful.
p.s.
When a new model is applied to a zone what setting(s) may be updated, and can the app detect custom values then prompt the user if they wish to override or retain custom values?
This whole thing is still way too complicated. It’s clever that a super geek gardener can tinker with these variables, but I have no idea what my root depth, soil type, crop coefficient, trans evaporation rate, etc., but I know when my plants aren’t getting enough water. Just give me an option to say “My plants aren’t getting enough water” and let that feed the ML algorithm.
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I’m glad to see support for this idea from the community, as we’re already planning out the details of this exact kind of feedback loop functionality now. I might follow up for more feedback on this soon.
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Awe, come on! That is where all the fun is at!!! 
I haven’t had a chance to mess with some of the new ML settings yet, but I’m far from a “super geek gardener” and I was able to follow the steps to find my soil type, root depth is pretty broad (shrubs could be anywhere from 12-18", established tree could be 20-36"), and really the other useful bit of information is the precipitation rate of your drip emitter or sprinkler nozzles. With that information, you are doing pretty good dialing in Rachio.
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Lol I don’t disagree that some people find it fun to dial in these variables. I’m the lazy, too busy user that just wants it to work (you can probably already guess that I’m an Apple fan boy also). Not trying to offend the great work that’s gone into this, but I think it can be simpler for dummies like myself.
I’m more than happy to offer more feedback. You guys are doing great work. I really like the system, I just also know it can be better.
Couldn’t agree more. There’s a lot of nice improvements happening behind the scenes, but there should also be some user interface/communication improvements.
What’s the next step in the ML process here and when should we expect to see it rolled out ?
We’re working on the next version of ML driven watering schedules right now, focusing on simplicity and efficiency. We’ll keep the community updated as we approach the Beta testing period.
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Just curious…. How is this AI or ML if it doesn’t have a feedback loop to actually learn something? Isn’t this just big data analysis? I am asking because there actually seems to be a big opportunity for AI and ML. Add soil testing and intake simple phone pictures for lawn health trends? Send everyone a soil probe and a test kit, stop pushing thrive too hard, and I’d bet you’d have a horde of loyal fans added to the ranks.
And seriously, why is “stop before sunset” hard again?
-farmers almanac or noaa for localized sunrise data
- simple time math we learned in 4th grade
- set a new start time
- umm, and a secret “hard part”?
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I think hard part is having to rewrite a bunch of legacy code to support a fixed end time, rather then a scheduled start time. The math is not hard, you are right, when you choose a stop before sunset option, rachio does exactly that, finds sunset time, substracts the maximum time a schedule can run and sets that as a scheduled time. Issue is with how zone skips occur. Currently they are simply skipped, making the run time shorter. Instead the schedule would need to be evaluated and any skipped runtime added to the starting time, so that end time would remain the same (sunset, etc).
Reason why it’s harder to do with legacy code is that there are currently certain triggers that are based on start time, such as weather / forecast evaluation an hour prior to a scheduled run. Should that evaluation indicate that zone(s) should be skipped and new start time is set, will this force a second evaluation an hour prior to the new start, what are implications from skipping this evaluation, or it returning a different result, indicating that weather has now changed and zones should not have been skipped to begin with?
As far as ML learning, I’m sure that they have feedbacks, such as what settings people end up using after a year of use, etc…
Ahh. Maybe perfect is the enemy of good here? I don’t think we are going to whip out our stopwatches and watch for the green lantern… oh wait, that’s sunset… 
At any rate, I learned something new today; I hadn’t realized this feature is there!
Any plans for sunset offset? Like my ideal would be end two hours after sunrise…
Cheers!
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