https://github.com/kanishk3813/plantpath

PlantPath is an advanced plant pathogen detection system leveraging deep learning techniques for early identification and precise classification of plant pathogens. By analyzing images of plant leaves, it empowers farmers with timely insights, minimizing crop damage and optimizing agricultural practices for sustainable farming.
https://github.com/kanishk3813/plantpath

cnn-model deep-learning jupyter-notebook python pytorch tensorflow

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PlantPath is an advanced plant pathogen detection system leveraging deep learning techniques for early identification and precise classification of plant pathogens. By analyzing images of plant leaves, it empowers farmers with timely insights, minimizing crop damage and optimizing agricultural practices for sustainable farming.

• Host: GitHub
• URL: https://github.com/kanishk3813/plantpath
• Owner: Kanishk3813
• Created: 2024-02-25T17:41:59.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-06-12T17:39:49.000Z (8 months ago)
• Last Synced: 2024-11-24T09:30:38.404Z (2 months ago)
• Topics: cnn-model, deep-learning, jupyter-notebook, python, pytorch, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 8.07 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Universal Pathogen Detection In Plants Using Deep Learning Techniques

## Overview

PathoPlant aims to develop a deep learning-based solution for the early detection and identification of pathogens in plant species. By leveraging state-of-the-art deep learning models, the project seeks to address the challenges associated with manual plant pathogen diagnosis and monitoring, thereby improving agricultural productivity and crop yield.

## Key Features

- **Advanced Deep Learning Model:** Incorporates a state-of-the-art convolutional neural network (CNN), ResNet50 architecture, for accurate identification of various plant pathogens.

- **Multi-Class Pathogen Detection:** Develop a model capable of detecting multiple classes of plant pathogens, including bacteria, fungi, pests, viruses and also healthy plants.

- **Diverse and Comprehensive Dataset:** Utilizes a diverse dataset sourced from multiple repositories, ensuring thorough training and validation across different types of pathogens and plant species.

- **Robust Image Preprocessing Techniques:** Employs advanced preprocessing techniques such as resizing, data augmentation, and normalization to standardize and enhance the quality of input images, improving model performance.

- **Comprehensive Evaluation Metrics:** Evaluates model performance using metrics like accuracy, precision, recall, and F1-score, along with visualization tools like confusion matrices, providing detailed insights into classification capabilities.

- **Real-world Generalization Testing:** Undergoes rigorous testing with an independent dataset to assess generalization and robustness, ensuring effective deployment in practical agricultural settings.

  ## Installation

1. Clone the repository:

   ```bash

   git clone https://github.com/Kanishk3813/PlantPath.git

- Download the dataset from [Kaggle](https://www.kaggle.com/datasets/kanishk3813/pathogen-dataset) or curate your own dataset.

- Please Note : Install necessary python libraries by (`pip install {library_name}`) .

  ## Usage

1. **Training the Model:** Train the deep learning model using the provided dataset, adjusting hyperparameters as necessary to optimize performance.

2. **Model Evaluation:** Evaluate the performance of the trained model using appropriate metrics and validation techniques to ensure accuracy and reliability.

3. **Saving the Model:** Save the trained model as a .h5 file for future use and deployment in production environments.

4. **Configuration Setup:** Configure the necessary path folders in the main.py file to ensure seamless integration with your local environment.

5. **Running the Application:** Execute the application by running **'streamlit run main.py'**, allowing users to interact with the system and benefit from its capabilities.

   **Note:** The mobile application of the project is under development.

## Contributing

Contributions are welcome! If you'd like to contribute to PathoPlant, please follow these steps:

1. Fork the repository.

2. Create a new branch (`git checkout -b feature/your-feature-name`).

3. Make your changes and commit them (`git commit -am 'Add new feature'`).

4. Push your changes to your forked repository (`git push origin feature/your-feature-name`).

5. Create a pull request detailing your changes and their purpose.