PlantLab Blog

A Plant Health Diagnosis API: One Image In, Structured JSON Out

Wed, 16 Sep 2026 07:47:42 +0000

You have a product that sees plants: a grow camera, a controller, a garden app, a greenhouse dashboard. You want it to say something useful about plant health. The obvious path is to train your own vision model, which means collecting labelled images, standing up training infrastructure, and then owning the much harder second job forever - knowing when your model is wrong. Most teams do not want that job. They want an answer they can trust and a JSON response they can build on.

That is what a plant health diagnosis API is for. You POST an image, you get a structured result back, and the hard part is somebody else’s problem: the model, the calibration, the honesty about being unsure.

AI Plant Health Check: How to Read the Answer (and Spot a Confident Guess)

Wed, 16 Sep 2026 07:47:07 +0000

An AI plant health check is easy to build and easy to trust too much. Point a phone at a plant, and half a dozen apps will name a disease in a second, with a confidence percentage that looks reassuring. The naming is the easy part. Knowing when the answer is wrong, and admitting it out loud, is the part almost nobody ships. So here are the five questions that separate a real diagnosis from a confident guess, and how to take the photo so the check holds up.

A Plant Lab, One Crop at a Time

Tue, 15 Sep 2026 00:00:00 +0000

PlantLab had its name from day one, and the name was the plan: a diagnosis lab for plants, all of them, eventually. One crop is live today. Tomato, apple, grape, and leafy greens sit on the public roadmap. The long game is a lab that carries what it learns from one crop into the next.

I'm Building Tomato Diagnosis Next

Tue, 15 Sep 2026 00:00:00 +0000

Tomatoes are next. I’ve picked the second crop, and it’s tomato: the most-grown, most-photographed, most-argued-about plant in the garden and the field. Starting now, I’m building tomato diagnosis the same way I built the first one.

How to Photograph Cannabis Plants for an Accurate Diagnosis

Fri, 14 Aug 2026 00:00:00 +0000

How to Photograph a Cannabis Plant for Diagnosis

Frame the whole plant so it fills most of the shot, and use even light that isn’t blown out. Don’t zoom in on the damaged leaf, and don’t shoot from across the tent. If your lights are blurple, that matters far less than whether the photo is overexposed.

The mistakes that cost you most aren’t the ones people expect.

The Cannabis Plant Diagnosis API: What One Photo Gets You Back

Fri, 14 Aug 2026 00:00:00 +0000

What It Does

The PlantLab API takes one image of a cannabis plant and returns structured JSON describing what’s wrong with it: one of 30 conditions and pests (or healthy), a growth stage, a confidence score on every call it makes, and a bounding box per plant when the photo has more than one. It answers in about 18 milliseconds. Auth is a single X-API-Key header, and the free tier is 3 diagnoses a day without a card.

If you already have a camera pointed at your tent, this is the piece that turns a JPEG into something your automation can branch on.

What Shipped This Summer: A PlantLab Progress Report

Fri, 14 Aug 2026 00:00:00 +0000

The Short Version

Between June and August 2026, PlantLab learned to diagnose several plants in one photo, started naming the family of a condition when it isn’t sure of the specific name, made model upgrades visible to your code, and fixed an upload failure that took three tries to actually kill.

The middle two change what comes back in your JSON.

Fungus Gnats on Cannabis: The Wet-Soil Pest That Looks Like Root Rot

Tue, 21 Jul 2026 00:00:00 +0000

Every time you water, a little cloud of dark flies lifts off the soil, drifts around, and settles back down. They do not bite, they do not swarm the plant, and every forum thread calls them harmless. So you ignore them.

Meanwhile the plant is drooping. Lower leaves are yellowing, growth has slowed, and nothing about your feed or pH explains it. You start treating for overwatering, maybe root rot - and the real cause is still hovering over the pot.

Thrips on Cannabis: The Silver Streaks Growers Blame on Light

Tue, 21 Jul 2026 00:00:00 +0000

A leaf catches the grow light and something looks off. Not yellow, not spotted - silver. Scratched, almost metallic, like someone dragged fine steel wool across the surface. The first guess is usually the lamp: too close, too bright, a little light burn on the tops.

Then you notice the same sheen on a lower leaf that never gets direct light.

Potassium Deficiency in Cannabis: Burnt Edges and Rust Spots

Mon, 20 Jul 2026 00:00:00 +0000

You backed off the feed. The runoff numbers looked high, the tips looked crispy, so you did what every guide says to do when a plant looks overfed: you gave it less. A week later the leaf edges are worse - browner, drier, curling up like burnt paper.

That is the trap with potassium. The classic symptom, scorched leaf margins, looks exactly like the thing you would treat by feeding less. But a potassium-deficient plant is starving, not overfed, and cutting the feed pushes it further down.

Build a Scheduled Plant Health Monitor with Home Assistant

Mon, 13 Jul 2026 00:00:00 +0000

What You’ll Build

A camera watches your plants on a schedule. Each photo goes to an AI trained only on cannabis, and Home Assistant turns the answer into sensors you can automate against. Something looks off, your phone buzzes with the diagnosis and the photo. Everything’s fine, it stays quiet.

Setup runs about 20 minutes. The cost is a camera you probably already own plus PlantLab’s free tier - three diagnoses a day, no card. No soldering, no standalone Python scripts. A Home Assistant integration and some YAML.

PlantLab Now Diagnoses Multiple Plants in One Photo

Mon, 29 Jun 2026 00:00:00 +0000

The short version

PlantLab can now analyze more than one plant in a single uploaded photo. Instead of forcing the whole image into one diagnosis, the API finds the separable plants, diagnoses each one on its own, and returns a results[] array with one entry per plant.

This is a breaking API change. The response schema is now 3.0.0. Fields like is_healthy, growth_stage, conditions, pests, and reliability_score moved out of the top level and into results[]. Image-level fields such as is_cannabis and cannabis_confidence stay top-level.

If your code already treats a diagnosis as “the answer for this plant,” the migration is simple: iterate results[]. Single-plant photos still return exactly one result.

Honest AI for Plant Health Diagnosis: What a Research Month Taught Me

Fri, 26 Jun 2026 00:00:00 +0000

The Short Version

AI plant health diagnosis is having a moment - grow cameras with “AI” on the box, phone apps that name a deficiency from one photo, controllers that promise to read your plants for you. Most of them report a confidence number they haven’t earned, because the hard part of AI plant diagnosis isn’t producing an answer. It’s knowing when the answer is wrong, and proving the accuracy you claim on photos the model has never seen. June at PlantLab was a research-and-hardening month spent almost entirely on that second problem: catching my own model being wrong before a grower could. This is what that looks like from the inside, with the numbers.

Confidence Is Not Reliability: Trust Signals for Automated Plant Diagnosis

Wed, 10 Jun 2026 00:00:00 +0000

The Short Version

Most plant diagnosis tools show you a confidence number. Confidence tells you how strongly the model picked an answer - not whether you should act on it. Those are different questions, and on a hard photo they get different answers: a model can be very confident and flat wrong. If you’re feeding a diagnosis into an automation - a Home Assistant flow, a grow-room controller, a dashboard alert - the signal you actually want is reliability: how trustworthy is this answer, on this specific image? PlantLab returns both. Here’s how to use each, and why automation should gate on reliability, not confidence.

Why PlantLab Runs on European Infrastructure

Sat, 06 Jun 2026 00:00:00 +0000

Building on American cloud is the easy choice, which is exactly why almost everyone makes it. One account at Amazon, Google, or Microsoft and you get the whole stack in one place: compute, database, storage, DNS, email, all of it wired together, billed on one invoice, documented to death. It is a genuinely excellent product. It is also where PlantLab started.

It doesn’t run there anymore. The diagnosis API, the database that holds your history, and the services around them now run on European infrastructure, and the live path a request travels - upload, inference, response, storage - never leaves the EU. That was not the easy choice. I want to explain why I think the easy choice and the right one were two different things here.

What Growers Actually Need From AI: Field Notes After WCCBE Frankfurt

Tue, 02 Jun 2026 00:00:00 +0000

The Short Version

I spent two days in May chairing and speaking at World Class Cannabis Business Europe (WCCBE) in Frankfurt, on the subject of visual AI for cannabis cultivation. The biggest takeaway wasn’t about the technology. It was about the gap between what the AI industry likes to talk about - autonomy, end-to-end automation, “AI runs the grow” - and what growers in the room actually asked for, which was something much more grounded. They want a tool that helps them decide, not one that decides for them. They want it to be specifically good at plants, not generically good at everything. Here’s what I heard, and what it means for where PlantLab goes next.

A Plant Photo Says More Than You Think: Privacy by Design at PlantLab

Sun, 31 May 2026 00:00:00 +0000

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.

Why a Plant Diagnosis Should Have History

Sun, 10 May 2026 00:00:00 +0000

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.

How PlantLab Knows When It Might Be Wrong: The reliability_score Field

Thu, 30 Apr 2026 00:00:00 +0000

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.

Node-RED + PlantLab: Scheduled Plant Health Checks

Fri, 24 Apr 2026 00:00:00 +0000

What You’ll Build

A Node-RED flow that captures a photo on a schedule, sends it to PlantLab for diagnosis, and routes the result wherever you want it. Push notifications, dashboard updates, 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: 30 possible conditions, a growth stage, nutrient antagonism hypotheses, and confidence scores, all ready to feed into whatever comes next.

Bud Rot and Root Rot in Cannabis: Detection Before It's Too Late

Sun, 12 Apr 2026 00:00:00 +0000

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.

Calcium vs Magnesium Deficiency in Cannabis: A Visual Comparison

Tue, 07 Apr 2026 00:00:00 +0000

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.

Spider Mites on Cannabis: Early Detection Before the Damage

Tue, 31 Mar 2026 00:00:00 +0000

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.

Powdery Mildew on Cannabis: Visual Detection and Prevention

Mon, 23 Mar 2026 00:00:00 +0000

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.

What's Wrong With My Cannabis Plant? A Visual Diagnosis Guide

Fri, 13 Mar 2026 00:00:00 +0000

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.”

Nutrient Antagonism in Cannabis: Why Chasing Deficiencies Makes Things Worse

Tue, 10 Mar 2026 09:00:00 +0000

The Short Version

Nutrient antagonism is when excess of one nutrient physically blocks another from being absorbed - your plant has enough of what it needs, it just can’t get to it. Adding more of the blocked nutrient usually makes things worse. A 1953 agricultural chart called the Mulder’s Chart maps all of these interference relationships; PlantLab’s diagnosis now applies that same logic automatically, flagging the most likely excess nutrient in every analysis.

What this post covers:

Why “add more” is sometimes the exact wrong answer
The Mulder’s Chart: what it shows and how to read it
The four antagonism traps cannabis growers hit most often
How to tell antagonism from a true deficiency
What to actually do once you’ve identified the likely excess

You’re three weeks into flower. New growth is showing interveinal chlorosis - yellowing between the veins. Classic iron deficiency. You’ve seen it before. You adjust your pH, add some chelated iron, wait a few days. Nothing. You add more. The leaves get worse. Two weeks of this and your runoff EC looks completely normal. What is going on?!

Here’s the thing: your plant probably has plenty of iron. The problem is that something else is blocking it from getting in. You’re chasing a deficiency that isn’t really there.

Nitrogen Deficiency in Cannabis: A Visual Guide

Fri, 23 Jan 2026 00:00:00 +0000

Nitrogen deficiency in cannabis appears as yellowing of lower, older leaves that progresses upward from the bottom of the plant. Because nitrogen is a mobile nutrient, the plant moves it from old growth to support new leaves. The key diagnostic marker is that yellowing includes the veins - unlike iron or magnesium deficiency where veins stay green.

Quick checklist:

Yellowing starts on BOTTOM leaves
Yellowing includes veins (not just between veins)
New growth at top still green
Leaves may cup upward before falling off

If yellowing appears on top/new growth first, it is NOT nitrogen deficiency.

Why I Built PlantLab

Tue, 06 Jan 2026 00:00:00 +0000

The Problem

Let’s say you are a new or aspiring cultivator. You got some seeds, soil, nutrients, and the rest of your setup. You plant the seed and in a few days a little sprout starts growing. You hope that all goes according to the instructions you read or videos you watched. But at some point, your plant looks a little off. Perhaps the leaves are curling or its color changes. Something doesn’t seem right. You search online, post photos on growing forums. You adjust pH, nutrients, lights. Sometimes the plant bounces back and sometimes it doesn’t. You wish you could get a clear signal about what’s going on, so you can actually address it.

Or perhaps you are an advanced grower, with a large tent or greenhouse. Daily, you walk and examine your plants, checking for signs of stress or pests. The more plants you have, the more chances something goes amiss and needs your attention. Your systems for lights, pH, water may be automated but they still need you: your vision and your experience.