🥔 Potato Disease Classification (Deep Learning + Streamlit)

This project is a Potato Disease Classification Web App built using TensorFlow/Keras and deployed with Streamlit.
The model can classify potato leaf images into three categories:

• Healthy
• Early Blight
• Late Blight

The app allows users to upload an image or take a picture using their camera, and the system will predict the disease along with the model confidence score.

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📂 Dataset

The dataset used for training comes from the PlantVillage Dataset on Kaggle.
For this project, only the following subsets were used:

• Potato___healthy
• Potato___Early_blight
• Potato___Late_blight

This dataset contains thousands of high-quality, labeled plant leaf images widely used for agricultural disease detection research.

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🧠 Model Training

The model was built using TensorFlow Keras with the following setup:

• Architecture: Convolutional Neural Network (CNN)
• Optimizer: Adam (chosen for stable and fast convergence)
• Loss Function: Categorical Crossentropy
• Epochs: 15
• Final Training Performance:
  • Accuracy: 0.9730
  • Loss: 0.0514

These results indicate that the model is well-optimized and performs strongly on the classification task.

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🌐 Web Application (Streamlit)

The web interface is built with Streamlit, providing a simple and clean user experience.

Live Demo : https://styfie-potato-disease-classification-app-f88a8h.streamlit.app/

✔ Features

• Upload a potato leaf image
• Take a picture using webcam
• Model predicts whether the leaf is:
  • Healthy
  • Early Blight
  • Late Blight
• Displays prediction confidence
• Responsive, minimalistic UI

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🚀 How to Run Locally

1. Clone the repository:

   git clone https://github.com/your-username/repo-name.git

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

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📁 Project Structure

📦 Potato Disease Classification
 ┃ 1.keras
 ┣ app.py
 ┣ requirements.txt
 ┗ README.md

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📜 License

This project is for educational and research purposes.