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.

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