Precision Agriculture
Leveraging AI, IoT sensor networks, and satellite imagery to build sustainable farming solutions that strengthen food security across Africa.
Precision Agriculture
Our precision agriculture research applies machine learning and remote sensing to transform smallholder and commercial farming. By integrating drone imagery, soil sensors, and weather data, we build predictive models for crop health, yield estimation, and optimal resource allocation — reducing input costs while increasing output quality.
Key Research Topics
Crop Health Monitoring
Multi-spectral image analysis to detect pest infestation, nutrient deficiency, and water stress at field-parcel resolution.
Drone-Based Sensing
Autonomous UAV systems for rapid aerial scouting, 3D canopy mapping, and targeted agrochemical application.
Weather & Climate Modeling
Integration of satellite climate data with local sensor networks to produce short-term crop-growth and drought-risk forecasts.
Yield Prediction
Ensemble learning models that predict per-hectare yields from multi-seasonal data, enabling pre-harvest planning and financing.
Current Projects
YieldCast: Pan-Africa Yield Prediction Network
Pan-Africa yield prediction network leveraging satellite and ground data to forecast harvests across diverse agro-climatic zones.
CropSense: Smallholder Crop Health Monitoring
End-to-end crop health monitoring platform using multi-spectral drone imagery and soil sensors for smallholder farmers.
Related Publications
Interested in Collaborating?
We welcome partnerships with institutions, NGOs, and researchers working at the frontier of AI and this domain.