# Plant Species Classification Dataset

A comprehensive dataset containing 64 different plant species with high-quality images for machine learning and computer vision applications.
Model Trainig code and trainned model with detailed performance analysis is present on the Github
https://github.com/jameelkhalidawan/Plant-Detection-Model-using-Yolo

## Dataset Overview

This dataset is designed for plant species classification tasks and contains images of various plant species organized in a structured format suitable for training deep learning models.

### Key Statistics

- **Total Classes**: 64 plant species
- **Total Images**: 152,042 images
- **Image Format**: JPG
- **Dataset Split**:
  - Training: 106,395 images
  - Validation: 22,779 images  
  - Test: 22,868 images

## Dataset Structure

```
Plants_Datadet/
├── train/                    # Training data (106,395 images)
│   ├── [Plant Species 1]/    # Each folder contains images of one species
│   ├── [Plant Species 2]/
│   └── ...
├── val/                      # Validation data (22,779 images)
│   ├── [Plant Species 1]/
│   ├── [Plant Species 2]/
│   └── ...
├── test/                     # Test data (22,868 images)
│   ├── [Plant Species 1]/
│   ├── [Plant Species 2]/
│   └── ...
├── train_split/              # Additional split for training (generated by training code)
│   ├── train/
│   ├── val/
│   ├── train.cache
│   └── val.cache
└── image_counts.xlsx         # Detailed image count statistics
```

## Plant Species Included

The dataset contains 64 diverse plant species including:

### Trees and Shrubs
- Acacia dealbata Link
- Liriodendron tulipifera L
- Nandina domestica Thunb
- Pyracantha coccinea M.Roem
- Schefflera arboricola (Hayata) Merr
- Smilax aspera L
- Trachelospermum jasminoides (Lindl.) Lem
- Zamioculcas zamiifolia (Lodd.) Engl

### Herbs and Wildflowers
- Aegopodium podagraria L
- Anemone alpina L
- Anemone hepatica L
- Anemone hupehensis (Lemoine) Lemoine
- Anemone nemorosa L
- Angelica sylvestris L
- Barbarea vulgaris R.Br
- Cirsium arvense (L.) Scop
- Cirsium vulgare (Savi) Ten
- Cymbalaria muralis P.Gaertn., B.Mey. & Scherb
- Dryopteris filix-mas (L.) Schott
- Epipactis helleborine (L.) Crantz
- Fragaria vesca L
- Helminthotheca echioides (L.) Holub
- Humulus lupulus L
- Hypericum androsaemum L
- Hypericum calycinum L
- Kniphofia uvaria (L.) Hook
- Lactuca serriola L
- Lamium album L
- Lamium galeobdolon (L.) L
- Lamium maculatum (L.) L
- Lamium purpureum L
- Lapsana communis L
- Lupinus polyphyllus Lindl
- Melilotus albus Medik
- Mercurialis annua L
- Nymphaea alba L
- Ophrys apifera Huds
- Pancratium maritimum L
- Papaver rhoeas L
- Papaver somniferum L
- Perovskia atriplicifolia Benth
- Trifolium incarnatum L

### Succulents and Sedums
- Sedum acre L
- Sedum album L
- Sedum rupestre L
- Sedum sediforme (Jacq.) Pau

### Ornamental Plants
- Anthurium andraeanum Linden ex André
- Fittonia albivenis (Lindl. ex Veitch) Brummitt
- Lavandula angustifolia Mill
- Lavandula stoechas L
- Pelargonium graveolens L'Hér
- Pelargonium inquinans (L.) Aiton
- Pelargonium zonale (L.) L'Hér
- Pelargonium zonale (L.) L'Hér. ex Aiton
- Punica granatum L
- Tagetes erecta L
- Tagetes patula L

### Vegetables and Fruits
- Cucurbita maxima Duchesne
- Cucurbita pepo L

### Tradescantia Varieties
- Tradescantia fluminensis Vell
- Tradescantia pallida (Rose) D.R.Hunt
- Tradescantia spathacea Sw
- Tradescantia virginiana L
- Tradescantia zebrina Bosse

## Image Characteristics

- **Format**: JPG
- **Naming Convention**: Images use hash-based filenames (e.g., `0038b49e0352646885a8899be350813d927f34a5.jpg`)
- **Quality**: High-quality images suitable for detailed plant identification
- **Content**: Various parts of plants including flowers, leaves, stems, and full plant views

## Usage

This dataset is suitable for:

1. **Plant Species Classification**: Train models to identify and classify different plant species
2. **Computer Vision Research**: Develop and test image classification algorithms
3. **Botanical Studies**: Analyze plant characteristics and features
4. **Educational Applications**: Create learning tools for plant identification
5. **Agricultural Applications**: Assist in crop and weed identification


## Dataset Splits

The dataset is pre-split into training, validation, and test sets to ensure proper model evaluation:

- **Training Set**: Used for model training and parameter optimization
- **Validation Set**: Used for hyperparameter tuning and model selection
- **Test Set**: Used for final model evaluation and performance assessment

## Additional Files

- `image_counts.xlsx`: Contains detailed statistics about the number of images per class
- `train_split/`: Contains additional splits generated during the training process with cache files for faster data loading

## Citation

If you use this dataset in your research or projects, please cite it appropriately and acknowledge the contributors.

## License

Please check the license terms before using this dataset for commercial purposes.

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*This dataset provides a comprehensive collection of plant species images suitable for various machine learning and computer vision applications in botany, agriculture, and environmental studies.*