CropX Prediction System

A sophisticated deep learning crop recommendation system that provides personalized crop recommendations based on soil conditions and environmental factors.

Features

• Advanced deep learning model:
  • CropX: Custom neural network with multi-branch architecture optimized for crop prediction
• Interactive Streamlit web interface with intuitive design
• Command-line prediction tool for batch processing
• RESTful API endpoints for integration with other applications
• Weather and location data integration via external APIs
• Detailed crop information and growing recommendations
• Visual representation of model performance and predictions

Setup Instructions

1. Install Dependencies

pip install tensorflow scikit-learn pandas numpy matplotlib streamlit flask python-dotenv requests opencage

2. Train the Model

python train_model.py

This will:

• Load the crop dataset
• Preprocess the data with feature scaling and encoding
• Train the CropX deep learning model
• Evaluate model performance
• Save the model, preprocessor, and label encoder

3. Make Predictions via Command Line

python predict_crop.py

Options:

• --model: Only 'cropx' is supported (default)
• --input: Path to CSV file with input data (default: use sample data)

4. Run the Web Application

streamlit run crop_prediction_app.py

The Streamlit application will automatically open in your default web browser.

How to Use

Command Line Interface

1. Run the prediction script:

   python predict_crop.py

2. For custom input data, prepare a CSV file with the required columns and use:

   python predict_crop.py --input your_data.csv

Web Interface

1. Open the Streamlit application in your web browser
2. Adjust Parameters:
   • Set soil parameters (pH, N, P, K, organic matter, soil type)
   • Set environmental parameters (rainfall, temperature, humidity, etc.)
   • Select region type
3. Get Recommendations:
   • Click "Predict Suitable Crops" button
   • View the recommended crops with probability scores
   • Review detailed crop information and growing tips
   • View model performance metrics

API Endpoints

The system provides RESTful API endpoints for integration with other applications:

1. Run the Flask API server:

   python app.py

2. Prediction API:

   • Endpoint: /api/predict
   • Method: POST
   • Content-Type: application/json
   • Request Body:

     {
       "soil_ph": 6.5,
       "nitrogen": 80,
       "phosphorus": 70,
       "potassium": 200,
       "organic_matter": 1.5,
       "rainfall": 1200,
       "temperature": 25,
       "humidity": 80,
       "sunlight": 8,
       "soil_type": "Loam",
       "region": "Temperate",
       "elevation": 1000
     }

   • Response:

     {
       "prediction": "Rice",
       "confidence": 0.92,
       "recommendations": [
         {"crop": "Rice", "probability": 0.92},
         {"crop": "Wheat", "probability": 0.05},
         {"crop": "Maize", "probability": 0.02}
       ],
       "input_parameters": {...}
     }

3. Weather Data API:

   • Endpoint: /get_weather
   • Method: POST
   • Content-Type: application/json
   • Request Body:

     {
       "lat": 37.7749,
       "lon": -122.4194
     }

4. Location Details API:

   • Endpoint: /get_location_details
   • Method: POST
   • Content-Type: application/json
   • Request Body:

     {
       "lat": 37.7749,
       "lon": -122.4194
     }

Dataset

The model is trained on a comprehensive dataset containing the following features:

• Soil pH (acidity/alkalinity)
• Nitrogen (N) content in kg/ha
• Phosphorus (P) content in kg/ha
• Potassium (K) content in kg/ha
• Organic Matter percentage
• Rainfall in millimeters
• Temperature in Celsius
• Humidity percentage
• Sunlight in hours per day
• Soil Type (Clay, Loam, Sandy, Silt)
• Region Type (Arid, Temperate, Tropical)
• Elevation in meters

Technologies Used

• Machine Learning:

  • TensorFlow/Keras for deep learning (CropX model)
  • Scikit-learn for preprocessing and evaluation
  • Pandas and NumPy for data processing

• Web Interface & API:

  • Streamlit for interactive web application
  • Flask for RESTful API endpoints
  • OpenWeatherMap API for weather data
  • OpenCage API for geocoding and location data
  • Matplotlib for data visualization
  • Python for backend processing

• Model Architecture:

  • Multi-branch neural network with batch normalization
  • Advanced feature preprocessing pipeline
  • Model evaluation framework

Model Details

CropX (Deep Learning)

CropX is a custom neural network with a multi-branch architecture:

• Input layer accepting preprocessed soil and environmental features
• Two parallel branches:
  • Deep branch with 256→128 neurons
  • Wide branch with 512 neurons
• Merged representation with 128→64 neurons
• Batch normalization for training stability
• Dropout layers to prevent overfitting
• Adaptive learning rate with ReduceLROnPlateau

Future Enhancements

• Crop rotation recommendations
• Seasonal planting calendar
• Soil amendment suggestions
• Integration with weather forecast data
• Mobile application version
• Ensemble methods combining all model predictions
• Region-specific model fine-tuning

License

This project is licensed under the MIT License - see the LICENSE file for details.