kduffuor/precision-cropland-machine-learning

Machine Learning classification of cropland vs non-cropland using Sentinel-2 satellite imagery and vegetation indices. Achieving 100% accuracy through spectral analysis in Ghana's Brong-Ahafo region. Built with Python, Scikit-learn, and Google Earth Engine. # Satellite-Enabled Precision Agriculture: Machine Learning Classification of Cropland Using Sentinel-2 Imagery A machine learning project that classifies agricultural cropland versus non-cropland areas using Sentinel-2 satellite imagery and spectral analysis techniques. This project demonstrates the application of remote sensing and machine learning techniques to classify land use patterns in the Brong-Ahafo region of Ghana. Using Sentinel-2 satellite data, the analysis achieves 100% accuracy in distinguishing between agricultural cropland and non-cropland areas through spectral band analysis and vegetation indices. ## Technologies & Tools ### **Remote Sensing & Data Collection** - **Google Earth Engine (GEE)** - Cloud-based platform for satellite data processing - **Sentinel-2 Satellite Imagery** - European Space Agency's multispectral satellite data - **JavaScript** - GEE scripting for data extraction and preprocessing ### **Data Analysis & Machine Learning** - **Python 3.8+** - Primary programming language - **Jupyter Lab/Notebook** - Interactive development environment - **Pandas** - Data manipulation and analysis - **NumPy** - Numerical computing - **Scikit-learn** - Machine learning algorithms and evaluation metrics ### **Visualization & Analysis** - **Matplotlib** - Static plotting and visualization - **Seaborn** - Statistical data visualization - **PCA** - Principal Component Analysis for dimensionality reduction ## Dataset - **Source**: Sentinel-2 Level-2A surface reflectance data via Google Earth Engine - **Study Area**: Brong-Ahafo Region, Ghana - **Time Period**: 2023 - **Sample Size**: 600 data points (300 crop, 300 non-crop) - **Features**: 6 spectral bands + 3 derived vegetation indices - **Balance**: Perfectly balanced dataset (50% crop, 50% non-crop) **For data collection (optional)** - Open Google Earth Engine Code Editor - Copy and Run script: brong_ahafo_crop_classification.js file ### Spectral Bands Used: - **B2** (Blue): Atmospheric a …