Peanut-Leaf-Disease-Detection

Peanut Leaf Disease Detection using CNN

This project implements a Convolutional Neural Network (CNN) machine learning model to detect diseases in peanut plant leaves.

Table of Contents

1. Data Preparation
2. Model Training
3. Model Testing
4. GUI Usage

Data Preparation

1. Acquire a dataset for training the model.
2. Organize the data into appropriate directories.
3. Clean and pre-process the data as necessary.

Model Training

1. Install the required dependencies:

pip install -r requirements.txt

1. Follow the instructions in the train.ipynb file step by step to train the model.

Model Testing

1. To test the performance of the trained model, run the test.py file:

python test.py

Note: Make sure to specify the image path in the test.py file before running.

GUI Usage

1. Ensure that the trained model is present in the model directory.
2. Test the model using the steps in the Model Testing section before running the GUI.
3. Launch the GUI application:

python gui.py

Requirements

All necessary dependencies are listed in the requirements.txt file. Install them using the command provided in the Model Training section.

Contributing

Contributions to improve the model or extend its functionality are welcome. Please feel free to submit pull requests or open issues for any bugs or feature requests.

License

This project is licensed under the MIT License. See the LICENSE file for details.