An intelligent web application providing comprehensive agricultural solutions through AI-powered crop management, disease detection, and farming assistance.

GreenGuard AI is a Flask-based web application designed to empower farmers and agricultural enthusiasts with cutting-edge AI technology. The platform combines machine learning models, real-time data integration, and intelligent chatbot assistance to provide end-to-end agricultural support - from crop selection to disease management and market analysis.

Target Users: Farmers, agricultural researchers, gardening enthusiasts, and agribusiness professionals seeking data-driven farming solutions.

• AI-powered image analysis using MobileNet deep learning model
• Support for 40+ plant diseases across multiple crop types (Apple, Bell Pepper, Corn, Tomato, etc.)
• Real-time diagnosis with confidence scoring
• Treatment recommendations and symptom analysis for identified diseases

• ML-based crop prediction using soil and environmental parameters
• Input parameters: Nitrogen, Phosphorus, Potassium levels, temperature, humidity, pH, rainfall
• 22 crop varieties supported including Rice, Maize, Cotton, Coffee, and more
• Data-driven suggestions for optimal crop selection

• Decision tree-based fertilizer suggestions tailored to specific crop and soil conditions
• Comprehensive input analysis: soil color, NPK levels, pH, rainfall, temperature, crop type
• Personalized recommendations with detailed fertilizer information and application guidelines

• Real-time weather data integration via OpenWeather API
• Current and forecast weather for location-based farming decisions
• Commodity price tracking using government data sources (data.gov.in API)
• Price prediction models for 23+ agricultural commodities
• Market trend analysis for informed selling decisions

• RAG-powered chatbot using LangChain and Ollama/Groq LLMs
• Agriculture-specific knowledge base with vector embeddings
• Natural language queries for farming advice and troubleshooting
• Context-aware responses limited to 10-30 words for quick insights

• Farmer community forum for knowledge sharing
• Image and text posting capabilities
• Real-time message exchange among registered users
• Experience sharing and collaborative problem-solving

• Automated news scraping from agricultural news sources
• Region-specific updates (Tamil Nadu agriculture focus)
• Latest market trends and government policy updates

Backend Framework:

• Flask 2.x - Python web framework
• SQLite - Database for user management and community features
• Gunicorn - WSGI HTTP Server for production deployment

Machine Learning & AI:

• TensorFlow/Keras - Deep learning models for disease detection
• scikit-learn - Crop recommendation and fertilizer prediction models
• LangChain - RAG framework for AI chatbot
• Ollama - Local LLM deployment (Llama 3.1)
• ChromaDB - Vector database for document embeddings

Data Processing:

• NumPy & Pandas - Data manipulation and analysis
• BeautifulSoup4 - Web scraping for news aggregation
• Requests - API integration for weather and price data

Frontend Technologies:

• HTML/CSS/JavaScript - Responsive web interface
• Bootstrap 4 - UI framework
• jQuery - Frontend JavaScript library
• Chart.js - Data visualization

Security & Authentication:

• bcrypt - Password hashing
• Flask Sessions - User session management
• CSRF Protection - Secure form handling

GreenGuardAPP-master/
├── app.py                    # Flask application entry point
├── config.py                 # Configuration settings and API keys
├── main.db                   # SQLite database
├── requirements.txt          # Python dependencies
├── runtime.txt              # Python version specification
│
├── routes/                   # Blueprint route modules
│   ├── auth_routes.py       # Authentication (login/register/logout)
│   ├── crop_routes.py       # Crop recommendation functionality
│   ├── disease_routes.py    # Disease detection and analysis
│   ├── fertilizer_routes.py # Fertilizer recommendation
│   ├── weather_routes.py    # Weather data integration
│   ├── price_routes.py      # Commodity price prediction
│   ├── chat_routes.py       # AI chatbot functionality
│   ├── community_routes.py  # Community forum features
│   ├── news_routes.py       # Agricultural news aggregation
│   └── general_routes.py    # General pages (index, about)
│
├── models/                   # Pre-trained ML models
│   ├── crop_recommendation/ # Crop prediction models and scalers
│   ├── disease_detection/   # MobileNet disease classification model
│   └── fertilizer_recommendation/ # Decision tree and transformer
│
├── utils/                    # Utility modules
│   ├── authentication.py   # Password hashing and login decorators
│   ├── database.py         # Database connection and table creation
│   └── chatbot/             # AI chatbot components
│       ├── models.py        # LangChain model initialization
│       └── ingest.py        # Document processing for RAG
│
├── services/                 # Business logic services
│   ├── disease_detection.py # Image preprocessing and prediction
│   └── crops.py             # Crop-related data processing
│
├── resources/               # Data files and configurations
│   ├── disease.py          # Disease information database
│   └── fertilizer.py       # Fertilizer details and recommendations
│
├── data/                    # Historical crop price data
│   └── crop_data/          # CSV files for 23 commodities
│
├── templates/               # HTML templates
│   ├── index.html          # Dashboard homepage
│   ├── login.html          # User authentication
│   ├── disease-detection.html
│   ├── crop-prediction.html
│   ├── fertilizer-recommendation.html
│   ├── chat-assistant.html
│   ├── community.html
│   └── [other templates]
│
└── static/                  # Static assets
    ├── css/                # Stylesheets
    ├── js/                 # JavaScript files
    ├── images/             # Application images
    └── uploads/            # User-uploaded files

• Python 3.10.8 (specified in runtime.txt)
• pip package manager
• Git for version control

git clone https://github.com/PGV-da/Green-Guard-AI.git
cd Green-Guard-AI

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

pip install -r requirements.txt

Create a config.env file in the root directory:

SECRET_KEY=your_secret_key_here
DATABASE_URL=main.db
WEATHER_API_KEY=your_openweather_api_key
GROQ_API_KEY=your_groq_api_key
PORT=4000

For Local Ollama Setup:

# Install Ollama (https://ollama.ai/)
ollama pull llama3.1
ollama pull tazarov/all-minilm-l6-v2-f32

Alternative: Use Groq API (recommended for production)

• Sign up at Groq Console
• Add your API key to config.env

python app.py
# This will create the SQLite database and required tables

# Activate virtual environment
venv\Scripts\activate  # Windows
# source venv/bin/activate  # macOS/Linux

# Run the application
python app.py

The application will be available at http://localhost:4000

# Using Gunicorn (included in requirements.txt)
gunicorn --bind 0.0.0.0:4000 app:app

# Or with environment variables
PORT=4000 gunicorn --bind 0.0.0.0:$PORT app:app

FROM python:3.10.8-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
EXPOSE 4000

CMD ["gunicorn", "--bind", "0.0.0.0:4000", "app:app"]

1. User Registration/Login

   • Navigate to /register to create an account
   • Use / to login with credentials

2. Disease Detection

   • Go to /disease/diseasedetection
   • Upload a plant image (JPEG/PNG)
   • Verify disease prediction and confidence score

3. Crop Recommendation

   • Access /crop/crop-recommendation
   • Input soil parameters (N, P, K, temperature, humidity, pH, rainfall)
   • Check recommended crop output

4. AI Chatbot

   • Visit /chatassistant
   • Ask agriculture-related questions
   • Verify contextual responses from Bloom AI

5. Weather Integration

   • Test /currentweather with location coordinates
   • Verify real-time weather data display

# Test commodity prices API
curl -X GET "http://localhost:4000/api/commodity-prices"

# Test chat API
curl -X POST "http://localhost:4000/chat" \
  -H "Content-Type: application/json" \
  -d '{"message": "How to prevent tomato blight?"}'

• Disease Detection: Test with sample plant images from each disease category
• Crop Recommendation: Validate with known good/bad parameter combinations
• Price Prediction: Compare predictions with historical price data

Contributing: Fork the repository, create feature branches, and submit pull requests for improvements.

License: Check the LICENSE file for project licensing information.

Support: Open issues on GitHub for bug reports or feature requests.