---

🌽 Corn Leaf Disease Classification

This repository contains a project that uses a pre-trained machine learning model to classify diseases on corn leaves. The project includes a Streamlit-based web application for easy interaction and deployment.

---

📜 Table of Contents

1. Introduction
2. Features
3. Project Structure
4. Setup
5. Usage
6. Deployment
7. License

---

🌟 Introduction

Corn is a vital crop worldwide, and early detection of leaf diseases is critical for maximizing yield. This project focuses on:

1. Providing an interactive web application using Streamlit.
2. Enabling predictions using a trained classification model (corn_leaf_classifier.h5).

The app predicts the condition of a corn leaf (e.g., Blight, Common Rust, Gray Leaf Spot, or Healthy) with confidence scores.

---

✨ Features

• Streamlit Web App: User-friendly interface for uploading and classifying leaf images.
• Pre-trained Model: Efficient classification with corn_leaf_classifier.h5.
• Error Handling: Robust validation for image uploads and model predictions.

---

📁 Project Structure

.  
├── .devcontainer/                # Development container configuration (optional)  
├── corn_leaf_classifier.h5       # Pre-trained model for classification  
├── new_streamlit.py              # Streamlit app script  
├── requirements.txt              # Dependencies for the project  

---

🛠️ Setup

1. Clone the Repository:

   git clone https://github.com/nnatureall/Corn_Leaf_Diseases_Classifier_With_CNN.git  
   cd corn-leaf-disease-classification  

2. Install Dependencies:
   Ensure Python is installed on your system (Python 3.8+ recommended). Install dependencies using:

   pip install -r requirements.txt  

3. Run the App:
   Launch the Streamlit app locally:

   streamlit run new_streamlit.py  

---

🚀 Usage

1. Open the Streamlit app in your browser (usually at http://localhost:8501).
2. Upload an image of a corn leaf (supported formats: .png, .jpg, .jpeg).
3. Click on the Classify button to predict the condition of the leaf.

---

🌐 Deployment

Deploy on Streamlit Community Cloud

1. Push the repository to GitHub.
2. Visit Streamlit Community Cloud.
3. Select your repository and set the entry point as new_streamlit.py.
4. Deploy and share the app link!

Example URL:

https://cornleafdiseasesclassifierwithcnn-iqbkasqkm53kgctun5m2vj.streamlit.app/```  
---

## 📝 License  
This project is licensed under the [MIT License](LICENSE).  

Feel free to use, modify, and deploy this project for educational or research purposes.  

---