WILLY070/HydroSense-Kenya

# HydroSense-Kenya **Automated Agricultural Monitoring and Optimization System** ## Project Overview HydroSense-Kenya is a data-driven, mathematically optimized irrigation scheduling system designed to combat agricultural water waste. By leveraging continuous soil moisture dynamics, historical meteorological data, and predictive algorithms, this system calculates the mathematical minimum water required to prevent crop stress across a multi-zone farming environment. --- ## Core Scientific Features This system abandons naive, daily watering schedules in favor of advanced numerical methods: * **Differential Equation Engines:** Solves continuous soil moisture decay rates using both 1st-Order Euler and highly stable **4th-Order Runge-Kutta (RK4)** methods. * **Stochastic Weather Modeling:** Utilizes **Monte Carlo simulations** (1,000+ scenarios) to model rainfall volatility and calculate statistical probabilities of crop stress. * **Resource Optimization:** Deploys a forward-looking algorithm to withhold irrigation until the precise mathematical threshold is reached, maximizing natural rainfall capture. * **Linear Systems Distribution:** Uses **Matrix Algebra** to safely and efficiently allocate limited tank resources across interconnected farm zones. --- ## Repository Architecture ```text HydroSense-Kenya/ │ ├── data/ # Data layer │ ├── raw/ # Unprocessed sensor and meteorological CSVs │ └── processed/ # Cleaned, merged, and analysis-ready datasets │ ├── notebooks/ # Interactive Jupyter environments for analysis │ ├── Level_4_Data_Analysis.ipynb │ ├── Level_5_Simulation_and_Optimization.ipynb │ └── Level_6_Final_Integration.ipynb # Executive Dashboard & Master Schedule │ ├── src/ # Object-oriented core modules │ ├── data_cleaning.py # Outlier detection and missing value imputation │ ├── numerical_methods.py # Matrix solvers and root-finding algori …