Datasets:
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: objects | |
| struct: | |
| - name: bbox | |
| list: | |
| list: float64 | |
| - name: categories | |
| list: | |
| class_label: | |
| names: | |
| '0': single_seedling | |
| '1': double_seedling | |
| '2': triple_seedling | |
| - name: flight_date | |
| dtype: string | |
| - name: source_video | |
| dtype: string | |
| - name: split | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 15545420923 | |
| num_examples: 3152 | |
| download_size: 15545391709 | |
| dataset_size: 15545420923 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: unknown | |
| task_categories: | |
| - object-detection | |
| size_categories: | |
| - 1K<n<10K | |
| # Maize Seedling Msdd Detection | |
| The Maize Seedling Detection Dataset (MSDD) is a stand-counting dataset of maize genetic nurseries at the University of Missouri in Columbia, Missouri, made from RGB video frames that DJI Phantom 4 and Mavic 2 Pro drones recorded over the 2019-2022 growing seasons. It covers seedlings from growth stage V2 to V12 under varied lighting, wind, soil backgrounds, and nadir or oblique camera angles. Each seedling is boxed as a single plant, or as a double or triple cluster when two or three plants emerge together. The flight_date and source_video columns record which flight and video each frame patch came from, and the split column keeps the authors' train/val/test split. The dataset contains 3,152 images with 163,921 bounding box annotations across 3 categories. | |
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. | |
| The original train/test/val split has been preserved in the `split` column. | |
| ## Citation | |
| ```bibtex | |
| @article{kharismawati2026maize, | |
| 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={Agronomy}, | |
| volume={16}, | |
| pages={1605}, | |
| year={2026}, | |
| publisher={MDPI} | |
| } | |
| ``` | |
| *This dataset was reformatted from its original format to match HuggingFace standards.* | |