Crop Analysis Platform

Overview

The Crop Analysis Platform is designed to calculate various vegetation indices for crop monitoring and analysis. 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.

Key Features

• Vegetation Index Calculations: Compute multiple indices, such as:

  • NDVI (Normalized Difference Vegetation Index)
  • EVI (Enhanced Vegetation Index)
  • NDWI (Normalized Difference Water Index)
  • MNDWI (Modified Normalized Difference Water Index)
  • SAVI (Soil-Adjusted Vegetation Index)
  • NDMI (Normalized Difference Moisture Index)
  • CI (Chlorophyll Index)
  • LAI (Leaf Area Index)

• Interactive Frontend: Built using React and Leaflet for seamless map visualization and user interaction.

• Satellite Imagery Integration: Fetch satellite imagery from Google Earth Engine using Sentinel-2 data, processed through a Python Flask backend.

• Date and Index Selection: Users can select a date range and index type to fetch relevant satellite data for crop analysis.

• Download Imagery: Users can download satellite imagery for offline use and further analysis.

Technologies Used

• Frontend:
  • React
  • Leaflet (for map visualization)
• Backend:
  • Python
  • Flask
  • Google Earth Engine API (GEE)
• Satellite Data:
  • Sentinel-2 (COPERNICUS/S2_SR_HARMONIZED)

Getting Started

Frontend Setup

1. Clone the repository.

git@github.com:SylviaT01/Crop-Analysis.git

2. Install the required frontend dependencies:

npm install

3. Run the React development server:

npm start

Backend Setup

1. Install the required Python dependencies:

pip install -r requirements.txt

2. Set up your Google Earth Engine credentials by following the instructions on the official GEE documentation
3. Start the Flask server:

python app.py

Configuration

• Google Earth Engine API: Ensure you have access to Google Earth Engine and have set up the appropriate credentials for the API.

Usage

1. Open the frontend application in your web browser.
2. Select a date range and index type to fetch relevant satellite data.
3. The map will display the satellite imagery for the selected date range and index type.
4. Users can download the satellite imagery for offline use and further analysis.

Contributions

Contributions are welcome! If you'd like to contribute to this project, please fork the repository and submit a pull request.

Author

Sylvia Chebet

License

This project is licensed under the MIT License.