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.
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Advanced Deep Learning Model: Incorporates a state-of-the-art convolutional neural network (CNN), ResNet50 architecture, for accurate identification of various plant pathogens.
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Multi-Class Pathogen Detection: Develop a model capable of detecting multiple classes of plant pathogens, including bacteria, fungi, pests, viruses and also healthy plants.
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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.
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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.
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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.
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Real-world Generalization Testing: Undergoes rigorous testing with an independent dataset to assess generalization and robustness, ensuring effective deployment in practical agricultural settings.
- Clone the repository:
git clone https://github.com/Kanishk3813/PlantPath.git
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Download the dataset from Kaggle or curate your own dataset.
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Please Note : Install necessary python libraries by (
pip install {library_name}) .
- Training the Model: Train the deep learning model using the provided dataset, adjusting hyperparameters as necessary to optimize performance.
- Model Evaluation: Evaluate the performance of the trained model using appropriate metrics and validation techniques to ensure accuracy and reliability.
- Saving the Model: Save the trained model as a .h5 file for future use and deployment in production environments.
- Configuration Setup: Configure the necessary path folders in the main.py file to ensure seamless integration with your local environment.
- 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.
Contributions are welcome! If you'd like to contribute to PathoPlant, please follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature/your-feature-name). - Make your changes and commit them (
git commit -am 'Add new feature'). - Push your changes to your forked repository (
git push origin feature/your-feature-name). - Create a pull request detailing your changes and their purpose.