This is like a weather forecast, but for maize harvests: it uses past data about fields, farming practices, and climate to predict how much grain farmers are likely to harvest under conservation agriculture methods.
Reduces uncertainty about future maize yields in conservation agriculture systems so that farmers, NGOs, and governments can plan inputs, storage, and food security interventions more effectively without waiting for harvest-time measurements.
Domain-specific agronomic and regional data from conservation agriculture systems in Southern Africa, plus feature engineering and calibration to local soils, climate, and practices that are hard for generic ML providers to replicate quickly.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data availability and quality for different regions and seasons; model performance will degrade if deployed in areas with different climate, soils, or practices than the training data.
Early Majority
Focus on conservation agriculture in Southern Africa, where tillage, residue management, and soil conservation practices differ from conventional systems, requiring specialized data and calibration rather than generic global yield models.