PlantLab.ai | Blog

AI based plant health diagnosis

Healthy cannabis plant thriving inside a grow facility with the surrounding equipment anonymized for privacy

The Short Version

When you send a plant photo to a diagnosis API, you are not just sending a picture of a leaf. You are sending a signal about what you grow, roughly where, and sometimes at what scale. PlantLab treats that as sensitive data. Diagnosis history is kept only if you opt in, only for a bounded window, and the sensitive parts are encrypted at rest. Analytics are cookieless, the supporting infrastructure is moving toward EU providers, and your API key is shown once and never emailed back to you in full. None of this is glamorous. All of it is the difference between an API you can hand real grow-room data and one you can't.

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A row of green leaf markers along a thin timeline, with one larger and glowing brighter than the rest, suggesting a pattern recognized over time

A single diagnosis tells you what's wrong now. A history of them tells you whether you're getting better.

That's the gap PlantLab's new /history endpoint closes.

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The Short Version

PlantLab's API now returns a reliability_score field on every diagnosis. A number from 0 to 1 telling you how likely the answer is to be correct on this specific image. It replaces the old diagnostic_confidence and safety_classification fields, which were rule-based guesses that I never trusted. The new score is much better at flagging the diagnoses that turn out to be wrong – especially on the hard cases, which is where you actually need it. Schema bumped from 1.x to 2.0.0. If you're integrating with PlantLab today, the migration is a one-line change.

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The short version

Most plant diagnosis tools give you a paragraph to read. PlantLab gives your automation system something to act on.

The model covers 31 cannabis conditions and pests at 99.1% balanced accuracy. Balanced means every class counts equally – a system that nails common deficiencies but misses rare pests does not score well. The output is structured JSON that Home Assistant, Node-RED, or a custom controller can read and act on without a person in the loop.

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What You'll Build

A Node-RED flow that captures a photo on a schedule, sends it to PlantLab for diagnosis, and takes action based on the result. Push notifications, dashboard updates, MQTT messages to your controller, log lines into InfluxDB, or whatever combination you want. No Python. No YAML. Nodes and wires.

Setup runs about 25 minutes on a Node-RED instance that's already up. The cost is whatever camera you own plus PlantLab's free tier at 3 diagnoses a day. The output is a structured JSON result: 31 possible conditions, a growth stage, nutrient antagonism hypotheses, and confidence scores, all ready to feed into whatever comes next.

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Cannabis plant showing multiple deficiency symptoms - yellow bottom leaves, brown edges, and spotted new growth

Start Here

Something looks wrong. Maybe the bottom leaves are yellowing. Maybe the tips are curling. Maybe you walked into your tent and something just looked off in a way you can't articulate but your gut knows isn't right.

So you did what every grower does: you took a photo, posted it online, and got twelve different answers. Someone said CalMag. Someone said flush. Someone said “two more weeks.” None of them agreed on what the actual problem is.

This guide won't do that. It walks through a systematic process: look at where the damage is, what it looks like, and narrow it down to a specific cause. No guessing, no bro science, no “could be anything, hard to tell from the photo.”

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Spider mites on cannabis - by the time you see webbing, you're already losing

You adjusted your cal-mag for two weeks. The yellowing got worse. Then you saw the webbing.

That's how most growers discover spider mites – not when the problem starts, but when it's already out of control. The early damage looks so much like a nutrient deficiency that your first instinct is to adjust the feed. Meanwhile, a single female mite is producing thousands of descendants in a month.

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Bud rot and root rot in cannabis - two diseases that spread faster than growers react

You won't smell it at first. By the time you do – that damp, musty sweetness coming off a cola that looked fine yesterday – you've already lost that bud and probably the ones touching it. You cut it open, and the inside is grey mush. A week from harvest.

Bud rot. It colonizes from the inside out, hiding in the densest parts of your canopy where airflow is worst and humidity is highest. By the time the exterior shows damage, the interior has been decomposing for days.

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Calcium vs magnesium deficiency in cannabis - two leaves showing distinct symptom patterns

Something's wrong with your plant. The leaves look off. You post a photo to a growing forum and within minutes, three people reply: “CalMag.”

You could have posted a picture of your dog and someone would have said CalMag.

It's the universal answer to every cannabis problem, the “have you tried turning it off and on again” of indoor growing. Yellowing? CalMag. Spots? CalMag. Weird leaf curl? Believe it or not, CalMag. And hey – sometimes it works. But when it doesn't, most growers just add more CalMag, which can make things actively worse.

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Powdery mildew on cannabis leaf - white powdery patches spreading across upper leaf surface

It looks like your plant is getting frosty. White powder spreading across the leaves, that pale shimmer catching the grow light. Then you touch it, and your finger comes away white.

That's not trichome development. That's powdery mildew – and if you're seeing it now, the infection has been active inside your plant for up to two weeks already.

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