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Paddy Disease Detection System using Machine Learning

A comprehensive Django-based web application designed to help farmers identify and manage rice crop diseases through AI-powered image analysis and environmental data integration.

🌾 Project Overview

Paddy crop diseases can lead to substantial yield loss and impact the livelihoods of millions. This system provides an automated, real-time solution for early disease identification, focusing on the most destructive types:

  • Leaf Blast (Pyricularia oryzae)
  • Sheath Blight (Rhizoctonia solani)
  • Bacterial Blight (Xanthomonas oryzae)
  • Brown Spot (Cochliobolus miyabeanus)

✨ Key Features

  • AI-Powered Detection: High-resolution image analysis using Convolutional Neural Networks (CNN) and OpenCV.
  • Environmental Integration: Analyzes images alongside real-time data such as Temperature, Humidity, and Location for more accurate results.
  • Admin Authorization: Secure user management system where administrators must authorize new registrations before they can access the detection tools.
  • Social Connectivity: Features for searching other farmers and sending/receiving friend requests to build a supportive agricultural community.
  • Responsive UI: Modern, minimalist interface with a Glassmorphism aesthetic, optimized for both desktop and mobile devices.

🛠️ Technical Stack

  • Backend: Python, Django
  • Frontend: HTML5, CSS3 (Glassmorphism), Bootstrap 5, JavaScript
  • Machine Learning: OpenCV, NumPy, Scikit-learn (TensorFlow ready)
  • Database: SQLite (ACID compliant)

🚀 Getting Started

Prerequisites

  • Python 3.10+
  • Pip (Python Package Manager)

Installation

  1. Clone the repository: 
    git clone https://github.com/abhi340/paddy_disease_detection.git
cd paddy_disease_detection
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Run migrations: 
    python manage.py migrate
  4. Create an admin account: 
    python manage.py createsuperuser
  5. Start the development server: 
    python manage.py runserver

📂 Project Structure

  • paddy_app/: Main application logic, views, and models.
  • paddy_app/utils.py: Image processing and disease prediction logic.
  • media/: Storage for uploaded paddy images for analysis.
  • templates/: Professional Glassmorphism UI components.

📝 License

This project is developed for educational purposes as part of the Final Year Project requirements.

Note: This system is a prototype designed to demonstrate the potential of AI in agriculture. For critical agricultural decisions, always consult a certified professional.