| --- |
| task_categories: |
| - object-detection |
| language: |
| - en |
| tags: |
| - maize |
| - seedling |
| - detection |
| pretty_name: MSDD |
| size_categories: |
| - 1K<n<10K |
| --- |
| # MSDD: Maize Seedling Dataset for Stand Counting |
|
|
| ## Overview |
| The MSDD (Maize Seedling Dataset) is designed for plant detection and stand counting tasks in agricultural environments. |
| It contains aerial imagery of maize genetic nurseries with annotations for early-stage seedling detection and classification. |
|
|
| The dataset is provided in YOLO format and supports training with modern object detection frameworks such as YOLOv9, YOLOv10, and YOLOv11/12. |
|
|
| --- |
|
|
| ## Directory Structure |
|
|
| ``` |
| yolo_format/ |
| ├── training/ |
| │ ├── images/ |
| │ └── labels/ |
| ├── validation/ |
| │ ├── images/ |
| │ └── labels/ |
| ├── test/ |
| │ ├── images/ |
| │ └── labels/ |
| ├── yolov9_data.yaml |
| ├── yolov11-v12_data.yaml |
| └── README.md |
| ``` |
|
|
| ### Description |
|
|
| - `training/images/` — training images |
| - `training/labels/` — YOLO annotations for training images |
|
|
| - `validation/images/` — validation images |
| - `validation/labels/` — YOLO annotations for validation images |
|
|
| - `test/images/` — test images |
| - `test/labels/` — YOLO annotations for test images |
|
|
| - `yolov9_data.yaml` — configuration file for YOLOv9 training |
| - `yolov11-v12_data.yaml` — configuration file for YOLOv11/YOLOv12 training |
|
|
| --- |
|
|
| ## Annotation Format |
|
|
| Annotations follow the standard YOLO format. |
|
|
| Each label file contains one object per line: |
|
|
| ``` |
| <class_id> <x_center> <y_center> <width> <height> |
| ``` |
|
|
| - Coordinates are normalized to the range `[0, 1]` |
| - Each `.txt` file corresponds to one image with the same filename |
|
|
| --- |
|
|
| ## Classes |
|
|
| | Class ID | Description | |
| |--------|------------| |
| | 0 | single | |
| | 1 | double | |
| | 2 | triple | |
|
|
| --- |
|
|
| ## Dataset Usage |
|
|
| ### Example (YOLOv9) |
|
|
| ```bash |
| yolo task=detect mode=train model=yolov9-c.pt data=yolov9_data.yaml |
| ``` |
|
|
| ### Example (YOLOv11 / YOLOv12) |
|
|
| ```bash |
| yolo task=detect mode=train model=yolo11.pt data=yolov11-v12_data.yaml |
| ``` |
|
|
| --- |
|
|
| ## YAML Configuration Files |
|
|
| ### yolov9_data.yaml |
| ```yaml |
| train: <path_to_dataset>/training/images |
| val: <path_to_dataset>/test/oblique/images |
| test: <path_to_dataset>//test/all/images |
| |
| nc: 3 |
| names: |
| 0: Single |
| 1: Double |
| 2: Triple |
| ``` |
| |
| ### yolov11-v12_data.yaml |
| ```yaml |
| path: <path_to_dataset>/ |
| |
| train: training/images |
| val: test/shadow/long/images |
| test: test/all/images |
| |
| names: |
| 0: Single |
| 1: Double |
| 2: Triple |
| ``` |
|
|
| --- |
|
|
| ## Citation |
|
|
|
|
| This dataset is associated with a manuscript under review and is currently available as a preprint. |
|
|
| If you use this dataset, please cite: |
|
|
| Dewi Endah Kharismawati and Toni Kazic. |
| *Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN*. |
| arXiv preprint arXiv:2509.15181, 2025. |
| DOI: 10.48550/arXiv.2509.15181 |
|
|
| ### BibTeX |
|
|
| ```bibtex |
| @article{kharismawati2025msdd, |
| title={Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN}, |
| author={Kharismawati, Dewi Endah and Kazic, Toni}, |
| journal={arXiv preprint arXiv:2509.15181}, |
| year={2025}, |
| doi={10.48550/arXiv.2509.15181} |
| } |
| ``` |
|
|
| --- |
|
|
| ## License |
|
|
| Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
|
|
| https://creativecommons.org/licenses/by-nc-sa/4.0/ |
|
|
| --- |
|
|
| ## Contact |
|
|
| Dewi Endah Kharismawati, Ph.D |
| kharismawati.2@osu.edu |
