Plant-Disease-Classification-Model

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📌Table of content

• 📖Overview
• 🧰Tech Stack
• 🌟Features
• 📊Dataset
• ⚙️Installation
• 🗂️Project Structure
• 📈Results
• 🤝Contributing
• 📝Licence
• 📬Contact
• 🧠Stay Curious!

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📖Overview

This project involves training a deep learning model to detect and classify diseases in plant leaves using a well-organized training and testing dataset. The aim is to help farmers and agricultural experts diagnose plant diseases at an early stage — simply by analyzing images of leaves.

The model is built using a Convolutional Neural Network (CNN), a powerful architecture ideal for image classification tasks. It learns from thousands of labeled images of both healthy and diseased leaves and accurately identifies the type of disease in new, unseen images.

With proper training and tuning, the model achieves an accuracy of 92%, demonstrating its ability to generalize well across various types of plant diseases.

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🧰Tech Stack

• Programming Language: Python
• Libraries & Frameworks:
  • NumPy, Pandas - for data manipulation and numerical computations
  • TensorFlow - for buliding and training deep leaning models
  • Matplotlib - for visulaizing data and model performance
  • Streamlit – for creating a user-friendly web interface
  • OpenCV (cv2) - for image preprocessing and augmentation
• Utilities: os, glob - for file handling and directory operations

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🌟Features

- 🌱 Multi-class Plant Disease Detection – Supports classification across multiple crop diseases.
- 🔍 High Accuracy – Achieves robust performance using CNN-based architecture.
- 🧠 Deep Learning Powered – Built using TensorFlow and trained on real plant disease datasets.
- 🧪 Notebook for Training & Testing – Easily reproducible Jupyter notebook for model development.
- 🛠️ Customizable & Extendable – Easily train on new classes or fine-tune with your dataset.

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📊Dataset

• Dataset Name: PlantVillage Dataset

• Source: PlantVillage on Kaggle

• License: Open access (for research/non-commercial use)

• Number of Classes: 38 plant disease categories

• Image Size: 224x224 pixels

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⚙️Installation

git clone https://github.com/<your-username>/Plant-Disease-Classification-Model.git
cd Plant-Disease-Classification-Model

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🗂️Project Structure

 Plant-Disease-Classification-Model
├── frontend/
│   └── plant/               # Frontend files 
├── CODE_OF_CONDUCT.md       # Contributor behavior guidelines
├── CONTRIBUTING.md          # How to contribute to the project
├── LICENSE                  # Project license 
├── PlantDiseaseModel.ipynb  # Jupyter Notebook 
├── README.md                # Project documentation
├── SECURITY.md              # Security guidelines
├── plant_based.png          
└── web.py                   # Web app backend 

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📈Results

The model was trained for 10 epochs and showed consistent improvement in both training and validation accuracy.

Final Training Accuracy: ~92%

Final Validation Accuracy: ~90%

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🤝Contributing

Contributions are welcome! If you'd like to fix a bug,add a feature,improve documentation or raise an issue-our input makes the project better for everyone.

🛠️How to Contribute:

1. Fork the repository
2. Create a new branch
3. Make your changes
4. Commit and push your changes
5. Create a pull request

Please refer to CONTRIBUTING.md for more details.

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📝License

This project is licensed under the MIT License - see the LICENSE file for details.

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📬Contact Information

Created with 💻 by @ShikhaMaurya212402

Feel free to reach out: 📧 [email protected] 🚀

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🧠Stay Curious!

Want to dig deeper? Explore the code, raise an issue, or suggest a feature. Your ideas matter!

✨ Pro Tip: Every expert was once a beginner. Keep going — you've got this! 🚀

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