Potato Leaf Classification using Deep Learning

• This project classifies healthy and diseased leaves of potato using deep learning (CNN).

Technologies Used

• Frontend: React.js
• Backend: FastAPI
• Deep Learning: CNN

Features

• Leaf Classification: Classifies potato leaves as healthy or diseased.
• Accuracy: Achieves an accuracy of 94% in classifying potato leaves.

Project Structure

• The project is organized into the following folders and files:

api: Contains the backend API files.

• main.py: Main FastAPI application file.
• model_pickle: Folder containing the pickled model files.
• potatoes.h5: Deep learning model file.
• requirements.txt: File listing the Python dependencies.

potato-frontend: Contains the frontend React.js files.

Untitled.ipynb: Jupyter notebook containing the deep learning model development code.

How to Run

• Clone the repository: git clone https://github.com/harshitadhikari-1122/Potato_Classification-app.git

Set up the backend:

• Navigate to the api folder.
• Install dependencies: pip install -r requirements.txt
• Run the FastAPI server: uvicorn main:app --reload

Set up the frontend:

• Navigate to the potato-frontend folder.
• Install dependencies: npm install
• Start the React development server: npm start
• Access the application at http://localhost:3000 in your web browser.

Note

• Ensure that you have the necessary Python and Node.js environment set up before running the project.