PestPlantDiseaseID-Taiwan

Introduction

This project involves a Jupyter notebook for detecting plant diseases using a deep learning model. The notebook leverages a pre-trained Faster R-CNN model fine-tuned on a dataset of images of common plant diseases and pests in Taiwan.

Link to research: https://docs.google.com/document/d/1n786LkKaFmz_qqqdL6AAu1BLEJ1duCOAj83fhd8zKeY/edit?usp=sharing

Features

• Data Preprocessing: Steps to clean and prepare image datasets for training.
• Model Training: Fine-tuning a pre-trained Faster R-CNN model with custom datasets.
• Disease Detection: Real-time identification of pests and plant diseases from input images.
• Evaluation: Assess the model's performance and accuracy.

Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.8 or higher
• Jupyter Notebook
• Required Python libraries (listed in requirements.txt)

Installation

Access via google colab: https://drive.google.com/file/d/1wzNaimKnKTA57igvEvFaxiaASHD6P73m/view?usp=sharing

Or

1. Clone the repository:

   git clone https://github.com/yourusername/plant_disease_detection.git

2. Navigate to the project directory:

   cd plant_disease_detection

3. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

4. Launch Jupyter Notebook:

   jupyter notebook

5. Open the notebook Plant_Disease_Detection.ipynb and follow the instructions within.

Notebook Structure

1. Introduction: Overview of the project and objectives.
2. Data Preprocessing: Steps to load and preprocess the dataset.
3. Model Training: Code to fine-tune the Faster R-CNN model.
4. Disease Detection: Using the trained model to detect diseases in new images.
5. Evaluation: Assessing the model's performance.

Dataset

The dataset should be organized as follows:

data/
├── raw/
│   ├── aphid/
│   │   ├── image1.jpg
│   │   ├── image2.jpg
│   │   └── ...
│   ├── leafworm/
│   │   ├── image1.jpg
│   │   ├── image2.jpg
│   │   └── ...
│   └── ...

Usage

1. Data Preparation:

   • Place your training images in the data/raw directory.
   • Follow the notebook steps to preprocess the data.

2. Model Training:

   • Run the cells in the notebook to train the model using the prepared dataset.

3. Disease Detection:

   • Use the notebook cells to detect diseases in new images.

Results and Evaluation

• The notebook includes sections for evaluating the model's performance and visualizing the results.

Contributing

If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a pull request.

License

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

Acknowledgments

• Special thanks to the Taoyuan District Agricultural Research and Extension Station for providing the dataset.
• Inspired by various open-source projects and research papers on plant disease detection.