🌿 Anjaneya Plant Disease Detection & Cure

• Welcome to our innovative Plant Disease Detection system, a vital tool for modern agriculture. We've developed an advanced solution using Deep Learning technology that enables farmers to identify plant diseases quickly and accurately. Our system leverages Convolutional Neural Networks built with PyTorch to classify plant leaf images into 39 distinct disease categories. The model was trained on the comprehensive Plant Village dataset, which you can find linked in our Blog section.

🚀 Getting Started

Prerequisites

1. Make sure you have Python 3.8 installed on your system
   • You can download it from Python's official website
   • Verify installation by running python --version in terminal

Installation Steps

1. Clone this repository

   git clone https://github.com/astromanu007/Leaf-Disease-Detection.git
   cd Leaf-Disease-Detection
   

2. Set up Python Virtual Environment

   python -m venv venv
   

   • For Windows: venv\Scripts\activate
   • For Linux/Mac: source venv/bin/activate

3. Install Required Dependencies

   pip install -r requirements.txt
   

4. Download Pre-trained Model

   • Get plant_disease_model_1.pt from this link
   • Place it in the Flask Deployed App directory

5. Run the Application

   cd Flask\ Deployed\ App
   python app.py
   

6. Access the Application

   • Open your web browser
   • Go to http://localhost:5000
   • You're ready to start detecting plant diseases!

Optional: Explore in Jupyter Notebook

• Navigate to the Model directory
• Launch Jupyter Notebook to explore the model implementation

🤝 Join Our Community

• We welcome contributions from the developer community!
• Help us enhance the UI, improve the Deep Learning model, or add informative documentation
• When modifying the Deep Learning components, please update related documentation (.md, .pdf, .ipynb)
• Ensure your code is error-free and thoroughly tested
• Follow the standard fork and pull request workflow
• Learn about creating pull requests: https://opensource.com/article/19/7/create-pull-request-github

🔍 Test Image Library

• Access our curated test images in the test_images folder
• Each image is labeled with its corresponding disease for easy verification
• Perfect for validating the model's accuracy

📚 Learn More

Discover Our CNN Implementation with PyTorch

🌐 Live Demo

Try Our AI-Powered Detection Tool

📸 Application Preview:

Homepage

Image Upload Interface

Analysis Results

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Created by Manish Dhatrak 🌱