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Precision Agriculture

Leveraging AI, IoT sensor networks, and satellite imagery to build sustainable farming solutions that strengthen food security across Africa.

Precision Agriculture

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.

Remote Sensing IoT Sensor Fusion Satellite Imagery Crop Modeling Computer Vision RL

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

Ensemble learning framework combining satellite phenology, weather station data, and farmer survey inputs to produce district-level crop yield forecasts for staple crops across 8 African countries.…

Research · 2025–2028
XGBoost LSTM Google Earth Engine BigQuery Dash
CropSense: Smallholder Crop Health Monitoring

End-to-end platform ingesting multi-spectral drone imagery and soil sensor data to map crop stress, nutrient deficiency, and yield potential at 10 cm resolution across smallholder plots.…

Active · 2024–2026
PyTorch QGIS Sentinel-2 FastAPI React Native

Related Publications

2024 · IEEE/CVF Conference on Computer Vision and Pattern Recognition
Precision Agri-ML: Multi-Modal Crop Health Assessment

Kofi Boateng, Elena Rodriguez

IEEE/CVF Conference on Computer Vision and Pattern Recognition

2023 · Agri-Tech Intelligence Review
Satellite-Derived Yield Forecasting for Sub-Saharan Smallholders

Thomas Mueller, Anya Chen

Agri-Tech Intelligence Review

Interested in Collaborating?

We welcome partnerships with institutions, NGOs, and researchers working at the frontier of AI and this domain.