🌍 Agroforestry Smart Decision Support System
AI for climate-smart agriculture and sustainable land use in Kenya
📌 Overview
This project transforms machine learning research into a real-world decision support system that recommends optimal tree species for agroforestry systems. It integrates remote sensing, climate data, and terrain features to support sustainable farming and climate resilience.
🚀 Key Capabilities
- Location-based tree species recommendation
- Integration of satellite (Sentinel) and environmental data
- Species distribution modeling using ML
- Interactive web-based deployment
⚙️ Methodology
- Extract environmental predictors (climate, elevation, indices)
- Train ML models (RF, SVM, GBM, MaxEnt)
- Generate habitat suitability maps
- Deploy via Shiny application
📊 Results
- Random Forest achieved AUC > 0.99
- High recall and specificity
- Reliable ecological predictions
🛠 Tech Stack
Python, R (Shiny & Plumber API), Google Earth Engine, Remote Sensing, Machine Learning