AI and Robotics Platform for Precision Agriculture
Kilimo Robotics builds autonomous field systems for African farms. Kwish architected the data pipeline, the computer-vision stack trained on local crops, and the cloud control and telemetry layer behind the platform.
Client
Kilimo Robotics
Role
Data architecture, computer-vision stack and cloud control layer
Duration
In build and iterating
Thesis
Deployment
Sector
Agriculture
Outcomes
What changed.
Unified
One data layer across sensors, imagery and machines
Local
Vision models trained on African field conditions
Actionable
Telemetry surfaced as field-level decisions
Context
The setting
Precision agriculture models trained on European and North American crops fail on African smallholder fields. Kilimo Robotics needed a data and vision stack built for local crops, local field conditions and unreliable rural connectivity.
Challenge
What we had to solve
- Fragmented sensor and field data with no central store
- No detection models tuned to African crops and field layouts
- Limited rural connectivity between machines and the cloud
- Operators needing actionable output, not raw telemetry
Approach
How we delivered.
Unified data architecture
Single store for sensor, imagery and machine telemetry with consistent schemas.
Local computer vision
Detection models trained and validated on African crop and field imagery.
Edge-to-cloud pipeline
Buffered edge processing so machines keep working through connectivity gaps.
Farm management dashboard
Operator view turning telemetry into decisions on the field level.
Highlights
What was shipped
- Field-ready robotics control and telemetry platform
- Computer-vision models tuned to local crops
- Edge buffering for low-connectivity environments
- Operator dashboard over a unified data layer
Stack
Technology
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