Leaf Disease Detection with Deep Learning

This repository contains code for a leaf disease detection project using deep learning techniques. The project aims to automatically classify plant leaves as healthy or infected based on input images.

Dataset

The dataset used for this project is the Plant Disease Recognition Dataset from Kaggle. It consists of images of plant leaves categorized into three classes: "Healthy", "Powdery", and "Rust".

Project Structure

• Dataset/: Directory containing the dataset divided into train, test, and validation sets.
• main.ipynb: Jupyter Notebook containing the code for data preprocessing, model training, evaluation, and prediction.
• README.md: Markdown file providing an overview of the project, dataset, and instructions for running the code.

Getting Started

To run the code and reproduce the results:

1. Clone this repository to your local machine:

git clone https://github.com/ReapredatoR/Code_Alpha_Leaf_Disease_Detection.git

2. Navigate to the project directory:

cd Code_Alpha_Leaf_Disease_Detection

3. Install the required dependencies:

pip install -r requirements.txt

4. Run the Jupyter Notebook main.ipynb to train the model, evaluate its performance, and make predictions.

Contributors

• Malinga Rajapaksha