Truffle Crack Detection with Deep Learning This repository contains the code and data used in the research paper:

Title: [Insert paper title here] Authors: [Insert author list here] This project explores the application of Convolutional Neural Networks (CNNs) for detecting truffle cracks in land images. The model is designed to assist truffle hunters by differentiating between truffle cracks and other crack types present in the field.

Getting Started Prerequisites:

Python (version X.X or higher recommended) TensorFlow (version X.X or compatible with your Python version) Other libraries as specified in requirements.txt (install using pip install -r requirements.txt) Instructions:

Clone this repository: Bash git clone https://github.com/Azad77/CNNsTruffle.git Use code with caution. content_copy Navigate to the project directory: Bash cd CNNsTruffle Use code with caution. content_copy Install required libraries: Bash pip install -r requirements.txt Use code with caution. content_copy Data:

The dataset used for training and testing the CNN model is available in the data folder. This folder might contain subfolders for training, validation, and testing data depending on your project structure.

Code:

The core functionalities of the CNN model are implemented in the model.py script. This script likely includes functions for:

Loading and preprocessing the image data Defining the CNN architecture Training the model Evaluating the model's performance Running the Model (Optional):

A script for training and evaluating the model might be provided in a file named train.py or similar. Refer to the script's documentation (comments within the code) for specific instructions on running the training process.

Contributing:

We welcome contributions to this project! If you have improvements or extensions, please consider creating a pull request.

License This project is licensed under the [Insert license name here] license. See the LICENSE file for details.

Citation If you use this code in your research, please cite the following paper:

[Insert paper title here] [Insert author list here] [Insert publication details here]