| # 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. | |
| --- | |
| *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.* |