README.md

metadata

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)

yolo task=detect mode=train model=yolov9-c.pt data=yolov9_data.yaml

Example (YOLOv11 / YOLOv12)

yolo task=detect mode=train model=yolo11.pt data=yolov11-v12_data.yaml

---

YAML Configuration Files

yolov9_data.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

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

@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