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AgriTechVisionDiagnose a leaf

model v4.2 · live · 214 diseases

Show me the leaf. I’ll tell you what’s wrong.

Photograph a sick plant with the phone already in your pocket. You get the disease name, how fast it spreads, and exactly what to buy — in under ten seconds, on 2G, in the language you speak.

1.2M
leaves, photographed here
214
diseases named
43kB
average request
₦0
to use

Covered crops: cassava, maize, tomato, cowpea and plantain.

Try it on a real leaf.

Pick one of the five sick leaves below and watch the whole thing run — the same four steps that happen on your phone. Nothing is hidden.

no sign-up · no app install
works in the browser

camera_in · 4032×3024idle

Pick a leaf above

Or drag a photo of your own into this box.

  1. ·Reading your photo
  2. ·Finding the leaf
  3. ·Comparing to 1.2M local leaves
  4. ·Naming the disease
PROCESSING SPEED0.00s

What you'll get back

  • The name of the disease, and what causes it
  • How serious it is and how many days you have
  • Exactly what to do, step by step, with the price
  • An honest score — and a human if the score is low

Sample photographs are illustrative. Diagnosis text, thresholds and treatment costs reflect real field guidance for each disease.

A farmer sitting at the edge of a field, reading from a handheld device
network · EDGE 2G

Answer returned in 9.4 seconds

43 kB up · 2 kB down · ₦0.31 of data

Built for the phone you already have.

Most farm software assumes a new phone and cheap data. That assumption is why it never reaches the field. We sized every request against a metered prepaid bundle on a 2G signal, and the compression happens on the phone before anything is sent.

data used per answer

  • One diagnosis here43 kB
  • Typical crop app screen2.1 MB
  • Video call to an agronomist18 MB

A diagnosis costs about a third of a naira in data. A farmer on a ₦100 bundle can diagnose roughly 320 plants.

Android 6 and up
Runs on a phone from 2015 with 1 GB of RAM. No new handset needed.
Offline pack · 38 MB
Download once on wifi. All five crops diagnose with the network switched off.
WhatsApp, no install
Send the photo to one number. The answer comes back as a message you can forward.
No account
No email, no password, no data collected about you. Your photo is deleted after the diagnosis.

Trained on our fields, not somebody else’s.

Nearly every open crop-disease model is trained on single leaves photographed against a white sheet in a lab, mostly in North America and Europe. Put that model in a Nigerian field and it collapses: different cultivars, harsher light, red soil in the frame, two diseases on the same plant at once.

So we collected our own. 1.2 million leaves, photographed on the phones farmers actually own, in the fields they actually farm, labelled with agronomists in each state.

accuracy on 8,400 held-out photos taken in real fields

  • Model trained on public foreign datasets41.2%

    PlantVillage-style lab photos, plain backgrounds

  • Same architecture, trained on our field imagery92.6%

    real backgrounds, real light, real co-infection

1,240

A100-hours per full retrain

6 weeks

between retrains, tracking the season

214

disease classes across 5 crops

8,400

held-out field photos in the test set

collection_sites · 2021–20261,204,880 leaves
Kano141kKaduna168kOyo223kBenue132kOgun97kEnugu154kCross River88k

37

states covered

610

trained collectors

11

languages labelled

Collectors are paid per verified photograph and keep contributing through the season, so the model sees each disease at every stage rather than only at its textbook peak.

What it knows today.

Five crops, 214 diseases. If your crop is not on this list the model will say so rather than guess at it.

accuracy measured on
held-out field photographs

Crops covered, with the number of diseases recognised and measured accuracy
cropdiseasesmost commonaccuracy
Cassavaege · rogo · akpu47Mosaic disease · Brown streak · Bacterial blight · Anthracnose94.1%
Maizeagbado · masara · ọka52Northern leaf blight · Streak virus · Grey leaf spot · Rust92.8%
Tomatokamaton · tumatis61Late blight · Early blight · Bacterial wilt · Leaf curl93.4%
Cowpeaewa · wake · agwa29Aphid damage · Brown blotch · Mosaic virus · Scab89.6%
Plantainogede · ayaba · ojoko25Black sigatoka · Panama wilt · Bunchy top · Cordana spot91.2%

Not covered yet

Photograph one of these and the model will tell you it cannot help, and pass you to an agronomist instead of inventing an answer.

  • Ricecollection running · 310k leaves so far
  • Yamcollection running · 96k leaves so far
  • Cocoastarts next dry season
  • Pepperstarts next dry season

The plant is in front of you. Ask it.

Free for every farmer, in every season, with no account and no data sold. Send one photograph and see what comes back.

or send a photo to +234 800 000 0000

Running a co-operative or an extension programme?

Bulk enrolment, shared dashboards for your officers, and diagnosis summaries by ward. We train your collectors and the imagery stays yours.

Talk to the field team