Why AI Is Ruining Your Grow

ChatGPT, Claude & Co. are not Plant Doctors: Why AI plant analyses without complete context are dangerous nonsense and growers need to learn to understand their plants themselves

A yellow leaf is photographed. Two images are uploaded to ChatGPT, Claude, Gemini or another AI system. This is accompanied by a sentence such as:

“What is my plant missing?”

A detailed answer appears just a few seconds later.

Calcium deficiency. Magnesium deficiency. Overwatering. Light stress. Incorrect pH. Nutrient lockout. Too little fertilizer. Too much fertilizer.

The AI explains the symptoms, names a presumed cause and immediately provides an action plan. It recommends CalMag, flushing, a pH correction, more fertilizer, less water or replacing the substrate.

The answer is clearly structured. It uses technical terms. It sounds logical and confident.

And it can still be complete bullshit.

That is why we consciously put it plainly at CannaSelection:

General-purpose AI systems are currently not reliable tools for diagnosing cannabis plants. Anyone carrying out specific treatments based on two images is not performing plant analysis. They are letting a statistical model guess based on incomplete information.

The danger is not only the incorrect answer. The danger is that it sounds like expert knowledge.

First, you need to understand what AI actually does

ChatGPT, Claude and comparable systems are so-called generative foundation models. They are often simplistically referred to as Large Language Models, or LLMs for short.

These models do not work like an encyclopedia in which a question is looked up and a verified expert answer is then provided.

During training, very large quantities of text, images and other content are processed. The model learns statistical relationships, structures and patterns from them. For example, it learns which terms frequently occur together, which explanations appear linguistically plausible and how a particular type of question is normally answered.

OpenAI states that its models are developed using, among other things, publicly available information from the internet, licensed content, and data provided or generated by people and users. Anthropic likewise states that Claude is developed using a mixture of publicly available internet content, data from third parties, internally generated data and other datasets.

After training, however, the model does not store this content like a searchable database. It learns mathematical relationships within the training information and uses them to generate a probably suitable answer. OpenAI describes this process, in essence, as learning relationships within very large quantities of information.

Put simply:

The AI does not know what your plant is missing. It calculates which answer is likely to sound appropriate for your input.

This is impressively powerful for many tasks.

But it is something entirely different from a confirmed biological diagnosis.

Why a plausible answer is still far from being a correct answer

A language model has been trained to generate a meaningful continuation.

If someone asks:

“My cannabis plant has brown spots. What is it missing?”

the model recognizes typical linguistic associations:

• brown spots,

• calcium,

• magnesium,

• pH,

• nutrient uptake,

• CalMag,

• overfertilization,

• light stress.

An answer is generated from these patterns.

However, the model does not automatically possess the information needed to distinguish between these possible causes.

It does not know the root ball or the water values. It does not know how the plant was watered over the past two weeks. It does not know whether the plant was recently repotted, whether CalMag has already been added or whether the presumed spots are mechanical damage.

Even so, a generative model does not necessarily have to remain silent. It may replace missing connections with a plausible narrative.

The National Institute of Standards and Technology refers to this behavior as “Confabulation”: generative AI systems can produce false content and present it with high confidence.

That is precisely what makes AI-based plant analyses so problematic for beginners.

An experienced grower may recognize that a recommendation does not fit the substrate or the previous cultivation practices.

A beginner often recognizes only:

“The AI said it was calcium.”

And then calcium is given.

Even AI with image recognition does not understand a plant

Modern systems such as ChatGPT or Claude can process images as well as text. This quickly creates the impression that they assess a plant photo in a way similar to an experienced plant pathologist.

That is wrong.

Multimodal models process visual features and connect them with linguistic patterns. They can recognize that a leaf is yellowish, has spots, is curling or has a particular shape.

But this does not mean that they automatically understand which biological process caused that appearance.

A leaf can look similar in cases of:

• an actual nutrient deficiency,

• impaired nutrient uptake,

• an overloaded root zone,

• substrate that remains too wet,

• excessively high salt concentration,

• unfavorable transpiration,

• light or heat stress,

• pest infestation,

• mechanical damage,

• natural aging,

• genetic expression.

The image shows the visible consequence.

It does not necessarily show the cause.

Even models specifically developed to detect plant diseases have considerable difficulty when real-world images differ from the conditions represented in their training datasets. Lighting, background, camera angle, cultivar, developmental stage and environment can significantly affect reliability. In research, this problem is referred to as domain shift.

These are models developed specifically using labeled plant images.

ChatGPT, Claude and other general-purpose AI systems, by contrast, are not validated cannabis diagnostic models.

The ability to describe an image is not the same as the ability to diagnose a plant.

Cannabis is a particularly poor use case for AI

Cannabis was prohibited or strictly regulated in many countries for decades.

This had a significant impact on research, data quality and the open documentation of professional cultivation.

Regulatory hurdles have demonstrably made cannabis research more difficult. Scientific institutions identify limited access to suitable plant material, complex approval procedures, limited funding and a lack of standardization as key problems, among others.

A large proportion of existing cannabis research concerns medical effects, active compounds, consumption and health consequences.

AI-assisted diagnosis of indoor cannabis plants, however, would require entirely different datasets.

A robust diagnostic model would need to link images with extensive and verified accompanying information:

• genetics,

• plant age,

• developmental stage,

• substrate composition,

• pot or bed volume,

• root condition,

• water analysis,

• watering history,

• fertilization strategy,

• temperature profile,

• humidity,

• leaf and room temperature,

• PPFD and DLI,

• pest status,

• laboratory analyses,

• confirmed cause,

• documented treatment,

• actual response to that treatment.

Thousands of comparable cases would need to be documented under controlled conditions, professionally assessed and monitored over the long term.

This exact data foundation does not exist for cannabis at the breadth and quality required for reliable general image diagnostics.

Instead, a model frequently encounters the following online:

• forum posts,

• Reddit discussions,

• grow journals,

• manufacturer texts,

• product advertising,

• blog articles,

• social media posts,

• incorrectly labeled symptoms,

• personal individual experiences,

• deficiency charts copied from one another.

This is not a scientifically validated diagnostic database.

Yes, Reddit and forums are also part of the information environment

It is important to be aware of the type of information available on the public internet.

OpenAI confirms that publicly available internet content is among its data sources. Since May 2024, there has also been an official partnership between OpenAI and Reddit through which OpenAI can access structured and current Reddit content.

This does not mean that every ChatGPT answer comes directly from a particular Reddit comment. A model does not normally simply copy an individual post and cite it as a source.

It does, however, mean that public discussions, community opinions and unverified user content can be part of the information environment from which such systems learn patterns or obtain current information.

And with grow topics in particular, the quality of this starting point needs to be examined.

In many forums, plant problems are diagnosed without knowing the water values, watering history or substrate composition. One user writes “CalMag,” the next “nitrogen,” the third “overwatered.”

No one examines the roots.

No one performs a substrate analysis.

No one reliably documents which measure actually helped in the end.

Nevertheless, these posts remain publicly available and continue to be quoted, copied and summarized.

If the source information consists of guesses, half-knowledge and incorrectly labeled images, no reliable cannabis diagnostics can emerge from it.

A model can generate a linguistically excellent summary from a large quantity of poor information.

That does not automatically make the information true.

The person before the AI is often the second problem

Even a highly capable model can work only with the information it receives.

And this is precisely where the next problem begins.

Most growers do not provide the AI with the complete cultivation history. They share what they themselves perceive.

These are two completely different things.

For example, a beginner writes:

“The plant is turning yellow even though I water normally.”

What does “normally” mean?

• How many liters?

• At what interval?

• In what pot size?

• By what criterion is watering carried out?

• Is there runoff?

• How quickly does the substrate dry?

• How thoroughly is it rooted?

• Is the entire surface watered evenly?

• Is the substrate at the bottom permanently wet?

• How heavy is the pot before and after watering?

The user may believe they are watering correctly. That is why they do not mention their watering behavior as a possible source of error.

Consequently, the AI does not receive the actual facts.

It receives the user’s subjective interpretation.

The same applies to statements such as:

• “The climate is fine.”

• “The pH is correct.”

• “The light is not too strong.”

• “The soil is high quality.”

• “I only use a little fertilizer.”

• “The plant cannot be overwatered.”

• “It must be a deficiency.”

The AI does not see the grow.

It sees the grower’s narrative about their grow.

When important information is missing, the model cannot conjure it from the image.

Instead, it may fill the gaps with typical assumptions.

More context can improve the answer, but cannot guarantee a diagnosis

Of course, an AI can produce better results when it receives extensive and precise information.

Anyone who fully documents genetics, plant age, substrate, pot volume, water analysis, lighting, climate, watering quantities, fertilization and the timeline enables a much better assessment than someone who uploads only two images.

The AI can then, for example:

• structure possible causes,

• point out contradictions in the information,

• formulate useful follow-up questions,

• build a differential diagnosis,

• compare measurements,

• summarize documentation.

However, this is not justification for blind trust.

Even with more context, a general-purpose model is not a validated cannabis diagnostician. It can set incorrect priorities, mix up different cultivation systems or develop a logically sounding but false conclusion from an incorrect user statement.

The crucial distinction is:

With good context, AI can support your thinking. But it should not take over the decision.

This is precisely where typical use fails.

Most people do not enter all relevant information. They upload a photo, expect a definitive answer and then act as if a laboratory analysis had been performed.

A plant is a system, not a collection of individual spots

Cannabis plants respond to the interaction of numerous factors.

Water affects the oxygen content in the root zone.

Root condition affects nutrient uptake.

Temperature and humidity affect transpiration.

Transpiration affects the transport of certain nutrients.

Light intensity changes water and nutrient requirements.

Substrate structure determines how water and oxygen are distributed in the pot.

A visible symptom may therefore stand at the end of a long chain of causation.

For example, a plant may show signs of a deficiency even though the relevant nutrient is sufficiently present in the substrate.

The actual cause may instead be:

1. The substrate remains too wet.

2. The root zone receives too little oxygen.

3. Root function is impaired.

4. Nutrient uptake deteriorates.

5. Apparent deficiency symptoms develop on the leaves.

The AI recognizes the symptoms and recommends additional fertilizer.

This leads to watering again, increasing the salt concentration and further intensifying the underlying problem.

The visible observation was correct.

The interpretation was wrong.

This becomes particularly dangerous in Living Soil

Living Soil is a biologically active system.

Nutrient availability, microbial activity, organic matter, moisture, oxygen supply, roots and substrate structure are directly interconnected.

Nevertheless, we regularly encounter AI recommendations such as:

• thoroughly flush Living Soil,

• immediately increase the EC value,

• apply CalMag indiscriminately when spots appear,

• add mineral fertilizer according to a schedule,

• aggressively correct the pH of every watering,

• combine several organic and mineral products,

• immediately treat presumed deficiencies with individual nutrients.

Some of these measures may be useful in certain situations.

Without knowledge of the entire system, however, they are not a diagnosis.

They are activism.

It is particularly problematic that AI frequently combines information from mineral, organic and hydroponic systems. The resulting answer may contain individual correct statements from each area and still be completely wrong as an overall recommendation.

This creates a kind of technical hybrid product:

• a little hydroponics,

• a little organic cultivation,

• a little Living Soil,

• a little manufacturer description,

• a little Reddit.

Everything fits together linguistically.

Biologically, it makes no sense.

AI can quickly turn a small problem into a fail grow

A cannabis plant needs time to respond to changes.

An already damaged leaf will not necessarily turn green again after a correction. A symptom may originate from a problem that occurred several days earlier. Some abnormalities have already stabilized and only need continued observation.

AI answers, however, almost always end with a recommendation to take action.

This creates the feeling that something must be done immediately.

The typical progression then looks like this:

1. The AI identifies a presumed calcium deficiency.

2. CalMag is added.

3. After two days, the old leaf looks unchanged.

4. A second AI suspects overfertilization.

5. The substrate is flushed.

6. A pH problem is then suspected.

7. The pH is corrected.

8. Additional fertilizer is applied again.

Within a few days, every relevant variable has been changed.

Afterward, no one can determine:

• what originally happened,

• which measure was useful,

• which measure caused new damage,

• whether any intervention was necessary at all.

A significant proportion of failed grows are not caused by the initial problem, but by the frantic reaction to it.

AI accelerates this activism.

If you only ask the AI, you will never get to know your plant

The larger problem is not technical.

It is human.

Since legalization, we at CannaSelection have increasingly observed that growers no longer provide a cultivation history.

They send two images and expect a ready-made solution.

What is missing includes:

• genetics,

• age,

• pot volume,

• substrate,

• water values,

• light values,

• climate,

• watering quantity,

• fertilization,

• development over time,

• measures already carried out.

Follow-up questions are often met with confusion.

Not because this information is being deliberately withheld, but because many growers have never considered it.

They do not know exactly how much water they give.

They do not know their water values.

They cannot explain why they use a product.

They have not documented the progression.

They do not observe which leaves were affected first.

They do not want to understand a plant.

They want an answer.

But that is not how cultivation works.

Growing means understanding the why

Anyone cultivating a plant should be able to explain the following for every measure:

• Why am I using this substrate?

• How does it hold and distribute water?

• How does oxygen reach the roots?

• Where do the nutrients come from?

• How do they become available to the plant?

• Why am I watering today?

• Why am I giving this amount of water?

• What is the objective of this input?

• How do I recognize whether the measure is working?

• What side effects can it have?

If someone cannot answer these questions, they should not implement the next AI recommendation.

They should first understand their own system.

An AI can formulate an expert-sounding justification for almost any idea.

But good wording does not make a bad measure sensible.

If you enter bullshit, provide incomplete information or do not know the decisive factors yourself, you will often receive nothing more than better-formulated bullshit in return.

Focus on plants instead of prompts

Anyone who wants to grow reliably needs to engage with the fundamentals:

• plant physiology,

• plant symptoms,

• root health,

• irrigation,

• substrate structure,

• soil biology,

• light,

• transpiration,

• temperature,

• humidity,

• water chemistry,

• nutrient cycles.

This sounds more complicated than uploading a photo.

But it is the only way to make good decisions consistently.

A grower does not need to memorize every biochemical reaction. But they do need to understand that a symptom can have multiple causes and that a plant must never be considered independently of its system.

They need to learn to read their plant:

• How is the leaf positioned?

• How does it feel?

• How is the new growth developing?

• How quickly does the plant drink?

• How does it respond after watering?

• Is the problem static or spreading?

• Does it affect old or young leaves?

• Is only one plant affected, or the entire crop?

This information does not come from a prompt.

It comes from daily observation.

Our clear recommendation

We expressly advise against using ChatGPT, Claude, Gemini or other general-purpose AI systems as the basis for diagnosing and treating cannabis plants.

In particular, no one should, based on an AI response:

• immediately add more fertilizer,

• flush Living Soil,

• use several inputs at the same time,

• mix organic and mineral strategies,

• change the pH hastily,

• completely change processes that are working,

• place individual measurements above the plant.

An AI cannot lift your pot.

It cannot feel whether the substrate is compacted.

It cannot smell an anaerobic root zone.

It does not know how the plant has developed over the past few days.

It cannot see how the plant behaves during the dark period.

It knows only what you tell it and what can be recognized in a limited image section.

It does not know your grow. Therefore, it should not control it either.

Why the Grow Doctor works differently

The Grow Doctor from CannaSelection was deliberately not designed as a supposed AI Plant Doctor.

It is not intended to invent a spectacular diagnosis from two images.

Instead, it guides growers systematically through symptoms and relevant influencing factors.

Among other things, it helps narrow down:

• which part of the plant is affected,

• whether old or young leaves show symptoms,

• how the symptoms are structured,

• which causes are generally possible,

• which conditions also need to be checked,

• which causes can be systematically ruled out.

That is the decisive difference.

The Grow Doctor does not promise a magical instant answer.

It encourages the grower to look more closely and check the right variables.

Because plant diagnostics are not a picture quiz.

They are a process of elimination.

Back to the Roots

Good home growing does not begin with ChatGPT, Claude and Co.

It begins with curiosity, observation and a willingness to take responsibility for a living system.

Anyone who wants only a finished product but is unwilling to understand processes or invest time in their plants should honestly question whether home growing is the right project at all.

Provided the legal and medical requirements for access are met, going through a pharmacy may ultimately be more affordable, controllable and reliably high-quality than a poorly understood indoor grow.

And we say this even though, from our perspective, standardized pharmacy quality should not be the goal of home growing, but at most its lowest benchmark for comparison.

Home growing can achieve considerably more.

But only if the grower is willing to engage with the plant.

Our conclusion

ChatGPT, Claude and other generative models are impressive tools.

They can structure information, prepare texts, compare measurements and, when provided with complete context, organize possible hypotheses.

But they are not cannabis Plant Doctors!

They were not trained on a comprehensive, scientifically validated database of cannabis symptoms. They know neither your individual setup nor your cultivation history. Their answers are based on statistical patterns from very different sources—including potentially forums, community discussions, manufacturer content and incorrectly classified symptoms.

The result may sound plausible and still be completely wrong.

That is why our advice is:

Stop having your plants diagnosed by general-purpose AI systems.

Get to know your plants.

Understand your soil.

Know your water.

Document your measures.

Observe changes.

And question every time why you are doing something.

A grow does not improve because an answer appears within ten seconds.

It improves when the grower understands what is actually happening in their system.