The Cotton Disease Detector is a machine learning-based application that helps detect diseases in cotton plants through image analysis. This project is built using Python and Flask.
- Introduction
- Features
- Prerequisites
- Installation
- Running the Application
- Usage
- Acknowledgements
- License
The Cotton Disease Detector leverages convolutional neural networks (CNNs) to classify and identify diseases in cotton plant leaves. This tool is designed to assist farmers and agricultural professionals in early disease detection and management.
- Image upload and disease detection.
- User-friendly web interface.
- Accurate and fast disease classification.
Before you begin, ensure you have the following installed:
- Python 3.7 or later
- pip (Python package installer)
Follow these steps to set up the project on your local machine.
-
Clone the repository:
git clone https://github.com/zeeza18/Cotton-Disease-Detector.git cd Cotton-Disease-Detector -
Create a virtual environment:
python -m venv venv
-
Activate the virtual environment:
- On Windows:
venv\Scripts\activate
- On macOS and Linux:
source venv/bin/activate
- On Windows:
-
Install the required packages:
pip install -r requirements.txt
-
Set the Flask app environment variable:
export FLASK_APP=app.py -
Run the Flask application:
flask run
By default, the application will run on
http://127.0.0.1:5000/.
-
Open your web browser and navigate to the application:
http://127.0.0.1:5000/ -
Upload an image of a cotton plant leaf:
- Click on the 'Choose File' button to select an image from your local machine.
- Click 'Submit' to upload the image for analysis.
-
View the results:
- The application will process the image and display the detected disease (if any).
Special thanks to Krish Naik for his tutorials and guidance in building machine learning projects.
MIT License