AI Crop Monitoring

AI crop monitoring, disease detection, and yield prediction for Kenya

Detect crop diseases early, predict yields accurately, and monitor soil health with AI-powered analytics built on verified field data.

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AI crop disease diagnosis with photo evidence

AI yield prediction from verified planting and growth records

AI soil health analytics for precision agriculture

Capabilities

Core capabilities

Designed to deliver evidence-based farm management with workflows that match real field teams.

AI pest and disease detection

Identify crop diseases and pest infestations early using photo-based AI diagnosis combined with field crew observations and GPS-stamped location data.

AI yield prediction and crop analytics

Forecast harvest volumes by combining planting records, weather data, input usage, and growth-stage observations into AI yield prediction models.

AI soil health analytics

Monitor soil condition trends across fields using sensor data and field observations to guide fertilizer application, crop rotation, and precision agriculture decisions.

Use Cases

What teams achieve with this solution

Practical workflows that bring proof, coordination, and measurable progress.

Early disease intervention

Field crews photograph suspicious symptoms; AI flags likely diseases and recommends treatment protocols before spread.

Precision input application

AI soil health analytics guide fertilizer and pesticide rates per field block, reducing waste and improving crop performance.

Harvest planning with AI yield prediction

Use AI-generated yield forecasts to coordinate harvest labor, transport, and market commitments with confidence.

Outcomes

Proof-led results for modern agribusinesses

Replace manual tracking with verified outputs and measurable performance.

30%

Earlier detection

AI identifies disease symptoms days before manual scouting.

±5%

Yield accuracy

AI yield prediction stays within 5% of actual harvest volumes.

20%

Input savings

Precision agriculture reduces fertilizer and chemical overuse.

FAQs

Questions teams ask before getting started

How does AI crop disease diagnosis work?

Field teams photograph affected plants. The AI model compares images against a disease library and provides likely diagnoses with recommended treatments.

What data does AI yield prediction use?

The model combines planting dates, GPS-verified growth observations, weather records, and historical yield data for the same field and crop variety.

Does AI soil health analytics require sensors?

Sensors improve accuracy, but the system also works with field crew observations and lab test records entered through the mobile app.

Take Action

Start verifying field work in days, not months. [Request a Demo] [Talk to Sales]

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