Plant Disease Classification with Deep Learning 🌱🤖

Welcome to the Plant Disease Classification project! This repository contains a Jupyter Notebook that leverages Convolutional Neural Networks (CNNs) to classify plant diseases using image datasets.

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Table of Contents 📖

1. Introduction
2. Features
3. Dataset
4. Model Details
5. Installation
6. Usage
7. Results
8. Future Work
9. Acknowledgments

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Introduction 🌾

This project focuses on identifying various plant diseases using machine learning techniques. By training a CNN model, we aim to accurately classify images of plants into their respective disease categories.

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Features ✨

• Deep Learning with TensorFlow/Keras: Utilizes state-of-the-art libraries for efficient model training.
• Data Augmentation: Enhances the dataset with transformations to improve model generalization.
• GPU Acceleration: Leverages available GPUs for faster computations.
• Multi-class Classification: Handles 38 distinct plant classes, including healthy plants.

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Dataset 📊

The dataset contains images of plants categorized into 38 classes, including healthy and diseased plants.

• Train Set: 70,295 images
• Validation Set: 17,572 images

The images are sourced from the "New Plant Diseases Dataset (Augmented)" and organized into training and testing directories.

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Model Details 🧠

The notebook implements a Convolutional Neural Network (CNN) using TensorFlow and Keras. Key features include:

• Input Size: 224x224 pixels
• Data Generators: ImageDataGenerator for real-time data augmentation
• Layers: Multiple convolutional layers followed by fully connected layers

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Installation ⚙️

To set up the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/Abdelrhman-Ellithy/Plant-Disease-CNN-Deep-Learning.git
   

2. Launch the Jupyter Notebook:

   jupyter notebook
   

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Usage 🚀

1. Dataset Preparation: Ensure the dataset is structured into train and valid directories.
2. Run the Notebook: Execute the cells step by step to train the model.
3. Results: View accuracy and loss plots, and evaluate the model on test images.

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Results 📈

• Accuracy: Achieves high accuracy on the validation set (details in notebook).
• Confusion Matrix: Visualizes model performance across classes.
• Sample Predictions: Displays model predictions on sample images.

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Future Work 🔮

• Model Optimization: Experiment with architectures like ResNet or EfficientNet.
• Hyperparameter Tuning: Adjust learning rates, batch sizes, and optimizers.
• Deployment: Create a web app or mobile app for real-time disease detection.

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Acknowledgments 🙏

Special thanks to:

• Dataset Providers: For making the plant dataset available.
• TensorFlow/Keras Community: For robust tools and support.

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License 📜

This project is licensed under the MIT License. Feel free to use and modify the code as per your needs.

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Happy Coding! 🌟