clifbrown/AgriRisk-AI

Explainable Machine Learning Early Warning System for Predicting County-Level Maize Yield Risk in Kenya. # 🌾 AgriRisk-AI ## An End-to-End Agricultural Risk Assessment and Machine Learning Platform > **AgriRisk-AI is an end-to-end data science and machine learning project designed to transform heterogeneous agricultural, climate, environmental, and socioeconomic data into reliable agricultural risk insights and decision-support outputs.** --- # 📌 Project Overview Agriculture is highly sensitive to environmental and climatic variability. Changes in rainfall, temperature, vegetation conditions, drought patterns, and socioeconomic conditions can significantly influence agricultural productivity and the livelihoods that depend on it. However, the challenge is not simply the availability of data. Large amounts of agricultural and environmental data exist across different providers, formats, spatial resolutions, temporal resolutions, and access mechanisms. Turning these datasets into a reliable machine learning system therefore requires much more than training a model. **AgriRisk-AI** is being developed as a complete data-to-decision platform that demonstrates how a professional machine learning system can be engineered from the ground up. The project brings together: - Data engineering - Data validation - Dataset provenance - Automated testing - Feature engineering - Machine learning - Model evaluation - Explainable AI - Interactive visualization - Reproducible workflows - Software engineering practices The project is being developed incrementally using a sprint-based methodology, with each sprint producing a tested and documented improvement to the system. --- # Problem Statement Agricultural decision-making is affected by uncertainty arising from climate variability, rainfall fluctuations, environmental conditions, and socioeconomic factors. Although valuable datasets exist for these domains, they are often: distributed across different sources; provided in different formats; updated at different frequencies; structured according to different standar …