{"id":21933192,"url":"https://github.com/sylviat01/crop-analysis","last_synced_at":"2025-07-24T12:42:17.411Z","repository":{"id":261685586,"uuid":"885033006","full_name":"SylviaT01/Crop-Analysis","owner":"SylviaT01","description":"This project is a Crop Analysis platform designed to calculate various vegetation indices for crop monitoring and analysis. The application is built using React for the frontend and Python with Flask for the backend. 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These indices, including **NDVI**, **EVI**, **NDWI**, **MNDWI**, **SAVI**, **NDMI**, **CI**, and **LAI**, help in assessing crop health, moisture levels, and vegetation cover. The platform integrates **Google Earth Engine (GEE)** for fetching satellite imagery, specifically **Sentinel-2 (COPERNICUS/S2_SR_HARMONIZED)** data, to provide accurate and up-to-date information for analysis.\n\n## Key Features\n- **Vegetation Index Calculations**: Compute multiple indices, such as:\n  - NDVI (Normalized Difference Vegetation Index)\n  - EVI (Enhanced Vegetation Index)\n  - NDWI (Normalized Difference Water Index)\n  - MNDWI (Modified Normalized Difference Water Index)\n  - SAVI (Soil-Adjusted Vegetation Index)\n  - NDMI (Normalized Difference Moisture Index)\n  - CI (Chlorophyll Index)\n  - LAI (Leaf Area Index)\n\n- **Interactive Frontend**: Built using **React** and **Leaflet** for seamless map visualization and user interaction.\n- **Satellite Imagery Integration**: Fetch satellite imagery from **Google Earth Engine** using **Sentinel-2** data, processed through a **Python Flask** backend.\n- **Date and Index Selection**: Users can select a date range and index type to fetch relevant satellite data for crop analysis.\n- **Download Imagery**: Users can download satellite imagery for offline use and further analysis.\n\n## Technologies Used\n\n- **Frontend**: \n  - React\n  - Leaflet (for map visualization)\n- **Backend**: \n  - Python\n  - Flask\n  - Google Earth Engine API (GEE)\n- **Satellite Data**: \n  - Sentinel-2 (COPERNICUS/S2_SR_HARMONIZED)\n\n## Getting Started\n\n### Frontend Setup\n1. Clone the repository.\n```bash\ngit@github.com:SylviaT01/Crop-Analysis.git\n```\n2. Install the required frontend dependencies:\n```bash\nnpm install\n```\n3. Run the React development server:\n```bash\nnpm start\n```\n### Backend Setup\n1. Install the required Python dependencies:\n```bash\npip install -r requirements.txt\n```\n2. Set up your Google Earth Engine credentials by following the instructions on the official [GEE documentation](https://developers.google.com/earth-engine/guides/auth)\n3. Start the Flask server:\n```bash\npython app.py\n```\n### Configuration\n- **Google Earth Engine API**: Ensure you have access to Google Earth Engine and have set up the appropriate credentials for the API.\n\n\n## Usage\n1. Open the frontend application in your web browser.\n2. Select a date range and index type to fetch relevant satellite data.\n3. The map will display the satellite imagery for the selected date range and index type.\n4. Users can download the satellite imagery for offline use and further analysis.\n\n## Contributions\nContributions are welcome! If you'd like to contribute to this project, please fork the repository and submit a pull request.\n\n## Author\n[Sylvia Chebet](https://github.com/SylviaT01)\n\n## License\nThis project is licensed under the [MIT License](https://github.com/SylviaT01/Crop-Analysis/blob/main/LICENSE).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsylviat01%2Fcrop-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsylviat01%2Fcrop-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsylviat01%2Fcrop-analysis/lists"}