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https://github.com/biswadeep-roy/plant-health-classifier
Plant Health Classifier uses deep learning to identify plant health from leaf photos. Achieves 98.39% accuracy, now open for contributions.
https://github.com/biswadeep-roy/plant-health-classifier
python python3 tensorflow
Last synced: 3 days ago
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Plant Health Classifier uses deep learning to identify plant health from leaf photos. Achieves 98.39% accuracy, now open for contributions.
- Host: GitHub
- URL: https://github.com/biswadeep-roy/plant-health-classifier
- Owner: biswadeep-roy
- Created: 2021-11-10T17:44:10.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-01T06:09:09.000Z (about 1 year ago)
- Last Synced: 2024-11-08T10:26:45.111Z (about 2 months ago)
- Topics: python, python3, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 4.97 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Plant Health Classifier
This project is a deep learning model that identifies whether a plant is healthy or not by analyzing photos of its leaves. It's trained to classify plants into various categories based on their health status.
## Dataset
The model was trained on the PlantVillage dataset, which includes the following classes:
- Pepper__bell___Bacterial_spot
- Pepper__bell___healthy
- Potato___Early_blight
- Potato___Late_blight
- Potato___healthy
- Tomato_Bacterial_spot
- Tomato_Early_blight
- Tomato_Late_blight
- Tomato_Leaf_Mold
- Tomato_Septoria_leaf_spot
- Tomato_Spider_mites_Two_spotted_spider_mite
- Tomato__Target_Spot
- Tomato__Tomato_YellowLeaf__Curl_Virus
- Tomato__Tomato_mosaic_virus
- Tomato_healthy
## Dataset Link
```bash
https://www.kaggle.com/datasets/arjuntejaswi/plant-village
```
## How to Use
1. Clone this repository:
```bash
git clone https://github.com/biswadeep_roy/Plant-Health-Classifier.git
```
Install the required libraries:
```bash
pip install -r tensorflow
pip install matplotlib
```
Open the Jupyter Notebook (Plant_Health_Classifier.ipynb) to see the code and run the model.
Follow the instructions in the notebook to train or use the pre-trained model.
## Results
The model achieved an accuracy of 98.39% on the validation dataset.
## Project Structure
/data: Directory for data sources and preprocessing scripts.
/notebooks: Contains Jupyter Notebook for model development and evaluation.
/models: Model checkpoints and saved models.
/scripts: Additional Python scripts if necessary.
## Data Source and Preprocessing
To obtain the PlantVillage dataset, visit the data source. In the data directory, you can find preprocessing scripts and instructions to prepare the dataset for training.
## Hyperparameter Tuning
If hyperparameter tuning was performed, the best hyperparameters and the reasons for choosing them are documented in the Jupyter Notebook.
## Model Checkpoints
You can download pre-trained model checkpoints from the /models directory. Use these checkpoints for inference or further training.
## Error Handling
The code includes error handling and informative error messages for common issues users might encounter.
## Testing
To ensure that the code is working as expected, unit tests have been implemented. You can find them in the /tests directory.
## Citation
If you use existing research or datasets, provide proper citations in your work to give credit to the original authors and sources.
## Visualization
The Jupyter Notebook contains various visualizations, including sample predictions, training curves, and data analysis to help users understand the project better.
## Performance Comparison
Consider adding a section that compares your model's performance with other existing models or research in the field, providing users with context for your results.
## Model Deployment
It is a real-world application and can be deployed to GCD, AWS or any other cloud service
## Contributing
If you would like to contribute to this project, please fork this repository, make your changes, and submit a pull request.
## License
This project is licensed under the MIT License.