🌴 Palm Leaf Disease Detection using Deep Learning

A Streamlit-based web application that detects palm leaf diseases using a deep learning model (EfficientNetB0).
The application allows users to upload palm leaf images and predicts the disease class with confidence.

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🛠️ Technologies Used

🔹 Programming Language

• Python – Core language used for model loading, preprocessing, and app logic

🔹 Deep Learning

• TensorFlow – Deep learning framework
• Keras – High-level API for model development and inference
• EfficientNetB0 – Pretrained CNN architecture for image classification

🔹 Web Framework

• Streamlit – Used to build the interactive web application

🔹 Image Processing

• Pillow (PIL) – Image loading and preprocessing
• NumPy – Numerical operations on image arrays

🔹 Model & Data Handling

• .keras Model Format – Saved trained deep learning model
• JSON – Used for class label mapping (class_labels.json)

🔹 Deployment & Environment

• Streamlit Cloud – Cloud deployment platform
• Linux-Compatible TensorFlow – For cloud execution
• Virtual Environment (venv) – Dependency isolation

🔹 Version Control

• Git – Source code version control
• GitHub – Repository hosting and collaboration

🚀 Features

• Upload palm leaf images
• Deep learning–based disease classification
• EfficientNetB0 pretrained model
• User-friendly Streamlit interface
• Fast and accurate predictions
• Streamlit Cloud deployment ready

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🧠 Model Used

• Architecture: EfficientNetB0
• Framework: TensorFlow / Keras
• Model File: EfficientNetB0_palm_disease_model.keras

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📁 Project Structure

├── app.py ├── EfficientNetB0_palm_disease_model.keras ├── class_labels.json ├── requirements.txt ├── .python-version ├── .gitignore └── README.md