Offline crop intelligence

AI - Crop disease diagnosis device

Works offline.

Point CropVox at an affected leaf, stem or fruit. The handheld device uses on-device computer vision to identify likely disease, pest damage, nutrient deficiency or visible crop stress, then gives practical guidance in a supported local language.

10 initial cropsOn-device inferenceLocal-language speech
Farmer using CropVox on a damaged maize leafField use · maize
CropVox AI handheld device
Likely issueFall armywormConfidence 92%
offline
No signal required for the core scan.The device keeps diagnosis, guidance, speech and scan history available locally.
What CropVox does

Everything needed for a useful first crop assessment.

The workflow stays simple enough for field use while preserving the information a farmer or extension worker needs to decide what to do next.

Guided crop scan

The camera workflow helps capture a useful view of leaves, stems or fruit before analysis begins.

Visual problem detection

Computer vision estimates the crop, visible problem category, likely condition and confidence.

Structured guidance

Curated instructions explain immediate actions, monitoring steps and when expert review is appropriate.

Offline operation

Core inference and the local guidance library are designed to run without a live cloud request.

Spoken local language

Supported guidance can be read aloud so the device is useful beyond text-first interaction.

Sync when connected

When a connection returns, scan records, model updates and difficult-case escalation can synchronize.

The main differentiator

No network?
Keep scanning.

That is the point.

Many crop problems are discovered far from dependable mobile coverage. CropVox keeps the essential workflow on the device, so the farmer does not have to wait for a signal before getting a useful first assessment.

CropVox being used in a cassava field
CropVox being used in a maize field
The hardware

A dedicated field device built around repeated scanning.

Camera, touchscreen, physical scan control, speaker, microphone, battery and embedded compute sit in one rugged body.

Initial crop library

Starting with ten widely grown Nigerian crops.

Each crop gets its own disease, pest, nutrient-deficiency and visible-stress catalogue so the system can be trained and validated deliberately.

Who it serves

One device can serve one farm or an entire local network.

View target market ↗
Maize farmer, CC0 public-domain photograph
Smallholder farmers

Guidance at the moment a problem is noticed.

Useful where specialist support and dependable mobile data cannot be assumed.

Farmer using CropVox in maize field
Extension services

A repeatable inspection workflow across many farms.

One device can move between farms while retaining structured scan records.

CropVox in cassava field
Cooperatives & agro-dealers

Shared crop-health access close to farmers.

Community diagnostic points can give many farmers access to the same hardware.

Responsible guidance

The device reports likelihood and confidence, not certainty.

CropVox is designed to present likely visual findings, confidence and alternative possibilities. Low-confidence cases can ask for another image or recommend expert review. Treatment guidance should remain curated and locally appropriate.

Read about the technical approach ↗
CropVox AI

Bring useful crop-health guidance closer to the farmer.

See it in the field