The AI said foxtailing. Three growers said nothing was wrong.

What happened, in three hours

On a Sunday morning in September a grower posted two photos to r/microgrowery. Bright green new growth in the colas, two different strains, and he could not tell whether he was looking at a problem. He wrote, in full:

Questioning the bright green new growth in the colas, both are different strains, the first pic could be just the plant is not as far along... but the second one ... I was guessing light stress .. ai thought start of foxtailing but 🤔 I am not convinced.

Three growers answered inside three hours. All three said there was nothing wrong. Looks perfectly fine and no foxtailing to see. I see no issues. I don't see a problem. Stacking nicely and lots of frost. Stay the course.

So a model looked at a healthy plant and named a specific thing that was not happening to it. And the grower had already discounted it before a single human replied.

I have the mirror image of that on my own bench, and it is not flattering to us.

Earlier this year our camera looked at one of my plants every hour for nine days — 174 times — over a real pattern on the margins and tips of the new growth. Every single time it came back “no finding”, and it was about as confident as it gets. I diagnosed the same plant myself, from a phone photo, in a few seconds.

So one model invents a disease that is not there, and ours missed one that was. Those look like opposite failures and they are the same failure: a machine producing an answer with more confidence than its evidence supports. The difference is only which direction the error runs, and which one embarrasses you in public.

This is not the complaint people usually make about AI

The usual complaint on grow forums is that chatbots are vague. Generic advice, no sources, true of every plant and useful for none. That is a quality problem, and it is annoying rather than dangerous.

This is the other failure, and it is worse in a way that is easy to miss: the answer was specific, confident and checkable. It named a real phenomenon. It was about his canopy, not about cannabis in general. If he had trusted it, he would have gone looking for a cause, changed something in a tent that did not need changing, and possibly created the problem he was trying to solve.

The distinction matters because it is the one that decides how you build the thing. Two questions that look alike:

• "What is foxtailing?" — a textbook question. Any chatbot can answer it. Refusing it makes a cultivation product look broken.
• "Is this foxtailing?" — a claim about the grower's own plant. Inventing here is not a quality problem. It is a false statement about something he owns and can act on.

So the assistant on the box has two lanes, and they are labelled

Start with what the chat apps are actually built for, because most of the disappointment comes from a mismatch about that. They are built to generate, to produce text the world has not seen before. Making things up is not a bug in that design, it is the design. People expected an extra-smart Google, something that retrieves. They got something that composes. It can behave like the first one, but only if you make it: restrict it to a corpus of known-valid data and tell it to answer only from there.

That is the whole of what we built. The general question gets answered from a knowledge pack that ships on the device, with its sources attached, and the model is not permitted to answer without a reference, so it can only give credentialed answers. The question about your grow gets answered only from what the hub has actually measured: your readings, your dates, your events. When there is no fact to answer from, it says so instead of producing something plausible.

The honest limit, worth stating because the usual version of this claim is too strong: this stops it inventing a source. It does not stop it being wrong. A correctly cited fact can still be the wrong fact for your plant, and if you feed the thing misinformation it will be wrong in the details right along with you. "It cannot be wrong" is a claim any thread can disprove in an afternoon. "It cannot invent a source" is one we can actually keep.

The answers are labelled with which lane they came from, because the honest fix was never to make the guardrail stricter. We tried strict first and it was worse: a question about the difference between indica and sativa got refused, with the assistant explaining it could not offer general growing advice. A textbook question, answerable by any free chatbot, turned down by a cultivation product. That is not caution, it reads as a broken box.

The fix was to split the input by what could go wrong with it rather than to clamp down harder. Claims about your plants keep the strict contract. General knowledge gets its own lane with its own narrower rule, and the two get told apart in the answer rather than one being sacrificed for the other.

IG-034Two questions that look alike

'What is foxtailing?''Is this foxtailing?'Kind of questionTextbook. Any chatbot can answer it; refusing it makes a product look broken.A claim about the grower's own plant.The failure that mattersVagueness — annoying, not dangerousA specific, confident, checkable statement about something he owns and can act onHow the box answersFrom the knowledge pack, with its sources attached; it cannot answer without a referenceOnly from what the hub has measured — readings, dates, events; when there is no fact, it says soWhat that promisesNot that it is rightNot that it is right — that it will not invent a source

Growers who answered, in three hours 3 all three: nothing wrong

Our camera, over one real problem 174looks nine days, hourly: 'no finding' every time

Our camera, the same week as the thread 79looks no finding on all of them

Our classifier on real canopy ≈ 7 in 10 right — nowhere near the clean-photograph numbers

r/microgrowery, 2026-09-20, no participant named. The camera counts are our own measurements, first-party, as the post says.

The part where I have to be careful

None of that means our model would have got this particular plant right.

It might well have said nothing at all. In the same week as that thread, our own camera looked at our own tent 79 times and returned no finding on every one of them. Earlier this year, over nine days and 174 hourly looks, it did the same thing over a pattern on new growth that I diagnosed myself from a phone photo in about four seconds. On real canopy images, under a real light, our classifier is right about seven times in ten — nowhere near the numbers you see published against clean laboratory photographs.

A model that says nothing is not thereby correct. It is quiet. So the claim I am willing to make is narrow and it is about permission, not eyesight:

A general chatbot will invent a plausible disease on your plant. This one is built so that it answers your grow questions from what it measured, labels general knowledge as general knowledge, and declines the rest. That is a constraint on the product. It is not a claim that its eye is better than yours.

I would rather publish that than have you find it out for yourself.

The grower was right, and that is the uncomfortable part

Go back to his sentence: I am not convinced.

He wrote that before anyone answered him, and he was correct. Not merely cautious — correct. Three experienced growers then confirmed it.

There is a lot of scepticism about AI on the grow boards, and I think it is earned rather than reactionary. It is earned by exactly this: a tool doing the thing it was built to do, which is compose a fluent answer, in a situation that called for retrieving a true one. A grower who has been burned once and now believes none of it has drawn a correct lesson from accurate evidence. He has just drawn it about a whole category rather than about a design choice.

So I do not think the job is to talk anyone out of that. Treating the distrust as a marketing obstacle gets it backwards, because the distrust is the accurate reading. The job is to build something that earns a different answer, and then be specific about which part of it you are asking anyone to trust. Not "our AI is good." Something narrower: it will not make up a source, and where it has nothing to go on it will say so.

Why I built it this way

Because the failure above is cheap to produce and expensive to catch. A model that will guess is useful right up to the first time it guesses confidently about something you cannot check, and then you have to verify everything it says, which is most of the work you were trying to avoid.

The way out was not a better model. It was deciding, in the code, what the model is permitted to assert — which questions it may answer from its own head, which it may answer only from the hub's own record, and what it must do when it has neither. Those are rules with tests behind them, not a system prompt asking nicely.

If a chatbot has already told you something confident and wrong about a plant you were worried about, you do not need convincing that this matters. What you need to know is what a thing is allowed to say, before you let it near the tent.

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Sources

• r/microgrowery, Light stress? Genetics?, 2026-09-20 — the thread. Quotes verbatim; no participant named.
• Nelson, J.A. & Bugbee, B. (2015), Economic Analysis of Greenhouse Lighting and the leaf-temperature measurements, PLOS ONE 10(10):e0138930 — cited elsewhere on this site for the leaf-versus-air argument, not for this post's claims.
• Our own measurements: 79 camera analyses 2026-09-14 to 2026-09-20, no finding on all of them; 174 hourly analyses over nine days in an earlier run, same result. First-party, not citations.
• The two-lane design and its labelling: ADR-0034 and its 2026-07-21 amendment, in our own architecture record.

Also read: The growers are right about the chatbots · Not a chatbot: what the assistant is allowed to say · The whole run, as the device recorded it