Features

• Disease Detection : Upload a leaf image to identify crop diseases using YOLO.
• AI Chatbot : Ask questions about the detected diseases, powered by LangChain and ChatGroq.
• Interactive UI : User-friendly interface built with React.

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Project Structure

.
├── backend/                # Backend server and AI processing logic
│   ├── app.py              # Flask API for handling image uploads and chatbot queries
│   ├── chat.py             # Chatbot logic using LangChain and plant disease information
│   ├── inference.py        # YOLO inference for disease detection
│   ├── best.pt             # YOLO model weights
│   ├── yolotrain.ipynb     # Notebook for YOLO model training
├── frontend/               # Frontend React application
│   ├── public/             # Static files
│   │   ├── index.html      # Main HTML file
│   ├── src/                # React components and logic
│   │   ├── App.js          # Main React component
│   │   ├── App.css         # Styles for the application
├── README.md               # Documentation (this file)

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Backend Components

1. chat.py

• Implements a chatbot to guide users about crop diseases.
• Uses LangChain with the ChatGroq model for natural language understanding.
• Contains a dictionary (class_info_dict) with detailed information about healthy and diseased leaves.

Key Functionality:

def chatbot(info, history, message):
    # Generates chatbot responses using the LangChain library

2. inference.py

• Handles disease detection using a YOLO model (best.pt).
• Outputs detected classes and visualizes bounding boxes on the image.

Key Functionality:

def inference(image):
    # Performs YOLO inference and returns processed image and detected classes

3. app.py

• Flask server to connect the backend with the frontend.
• Two main endpoints:
  • /upload: Accepts leaf images, detects diseases, and returns results.
  • /chat: Processes user queries based on detected diseases.

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Frontend Components

1. App.js

• The main React component for the frontend.
• Provides the following features:
  • Image upload and preview.
  • Disease detection using the /upload backend endpoint.
  • Chat functionality using the /chat backend endpoint.
  • Displays processed image and detected labels.

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Installation

Prerequisites

• Python 3.8+
• Node.js and npm

Backend Setup

1. Navigate to the backend/ directory.
2. Create a .env file with the following content:

   GROQ_API_KEY=<your-groq-api-key>
   

3. Install dependencies:

   pip install flask flask-cors opencv-python numpy langchain-groq ultralytics python-dotenv

4. Run the server:

   python app.py

Frontend Setup

1. Navigate to the frontend/ directory.
2. Install dependencies:

   npm install

3. Start the development server:

   npm start

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Usage

1. Start the backend and frontend servers.
2. Open the React app in your browser (typically at http://localhost:5000).
3. Upload a leaf image and process it to detect diseases.
4. Use the chatbot to ask questions about the detected diseases.

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Technologies Used

• Backend :
  • Flask
  • YOLO (Ultralytics)
  • LangChain + ChatGroq
• Frontend :
  • React.js
  • Bootstrap (for styling)

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Future Enhancements

• Add more robust error handling.
• Improve chatbot's conversational capabilities.
• Expand the dataset for YOLO training to include more crop diseases.

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