Health, home and travel decision

PlantMD: Plant Disease Scanne

A technical user can build a useful web-based scan+diagnose workflow and email reminders, but reproducing the native iPhone experience, curated dataset/model quality, and subscription polish of the paid app is larger work and product risk.

Visit website
You pay

Not priced

No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.

You’d pay instead

$100one-off44 h to build

$50/mo6 h/mo upkeep

No published price to break even against.

No open-source build does this yet

Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.

What a replacement has to do

  • User uploads a photo → run plant ID & disease inference → map inference to a named diagnosis and treatment steps → store plant and schedule recurring care reminders → deliver reminders (email/push).

What it still won’t have

  • Native iPhone app with lock-screen push reminders (only web/email reminders in the DIY)
  • Proprietary training dataset and any curated diagnosis model the vendor uses
  • Built-in premium subscription flow and any server-side rate/limit handling the vendor provides
  • Brand, app-store discoverability, and user reviews

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

PlantMD: Plant Disease Scanne does not publish a price we could read, so there is nothing to compare against. What building costs is below.

Money you would actually spend

Keep paying
—

Subscription price × seats × 12

Build it
—

AI build —APIs + hosting —

Time you would spend

—

—

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal PlantMD replacement as a PWA using React (frontend), Node.js/Express (backend), Postgres for data, and a hosted vision API (e.g., Replicate or similar) for image inference. Core features in scope: photo upload and preview, server-side image upload endpoint, call to vision model to return species and disease labels with confidence, a diagnosis-to-treatment lookup engine that returns human-readable steps, store plants and user (email) in Postgres, a scheduler worker (Bull or cron) that enqueues weekly care tasks, and send reminders via SendGrid. Out of scope: native iOS lock-screen push notifications, paid subscription billing, and training custom ML models. Include error handling for failed uploads and model calls, input validation, and unit tests for API endpoints and scheduler logic. Provide Dockerfiles and a Terraform or Docker Compose deployment manifest for one small VM and managed Postgres.
How we checked3 sources · 2/3 runs agreed · evidence score 55

How the score was reached

  • Partly verdict base52
  • 3 cited sources+3
  • Evidence score55

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 3

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded