This file was generated on 2026-07-14 by Esra Guclu


A GENERAL INFORMATION

1. Title of the dataset: BUTom21: Still image tomato dataset for detection and segmentation

2. Brief description of the research project and its aims:

BUTom21 dataset is a fully hand-annotated still image dataset of tomatoes. It contains 123, 72, 98 images respectively in the training, validation, and evaluation subsets.
The dataset contains RGB and noisy depth images captured across 2 capture days and two different RealSense D435i cameras. This creates a fully pixel-wise annotated dataset
for tomato detection and segmentation in a real-world robotics scenario. (2026-07-14)

3. Author Information
	A. Investigator Contact Information
		Name: Michael Halstead
		Institution: Institute of Agricultural Engineering
		Email: michael.halstead@uni-bonn.de

		Name: Esra Guclu
		Institution: Institute of Agricultural Engineering
		Email: egueclue@uni-bonn.de

	B. Project Supervisor (Principal Investigator) Contact Information
		Name: Chris McCool
		Institution: CSIRO
		Address: Australia
		Email: Chris.Mccool@csiro.au

		Name: Cyrill Stachniss
		Institution: University of Bonn
		Address: Bonn
		Email: cyrill.stachniss@igg.uni-bonn.de

		Name: Juergen Gall
		Institution: University of Bonn
		Address: Bonn
		Email: gall@cs.uni-bonn.de

		Name: Maren Bennewitz
		Institution: University of Bonn
		Address: Bonn
		Email: maren@cs.uni-bonn.de

		Name: Ribana Roscher
		Institution: University of Bonn
		Address: Bonn
		Email: ribana.roscher@uni-bonn.de

	C. In case of questions related to this dataset, please contact:
		Name: Esra Guclu
		Institution: Institute of Agricultural Engineering
		Email: egueclue@uni-bonn.de

4. Date of data collection: 2021-08-18 - 2021-09-10

5. Information about funding sources that supported the collection of the data: -

6. Language of the dataset: English

7. Geographic location of data collection: Commercial Glasshouse at Campus Klein-Altendorf (CKA), University of Bonn



B DATA & FILE OVERVIEW

1. File List:

BUTom21/
├── CKA_Tomato_2021_images/
│   ├── 2021_08_18/                 # capture day 1
│   │   ├── row_1/
│   │   │   ├── rgb/                # RGB images,   <frame_id>.png
│   │   │   └── depth/              # depth images, <frame_id>.png
│   │   ├── row_2/
│   │   └── ...                     # rows 1–6
│   └── 2021_09_10/                 # capture day 2
│       └── ...
├── CKA_tomato_2021.json            # COCO-format annotations
├── CKA_tomato_2021.yaml            # class definitions + train/valid/eval splits + statistics
├── image_camera_row_identity.yaml  # which image came from which camera, per row
└── BUTom21_structure.md

2. Are there multiple versions of the dataset?: No

3. Relationship between files: frame ID

4. Additional related data collected that was not included in the current data package: -



C SHARING/ACCESS INFORMATION

1. Was data derived from another source?: No

2. Licenses/restrictions placed on the data: CC BY 4.0

3. Links to publications that cite or use the data: If you use this dataset, please cite both the associated paper and the dataset itself as below:

## Paper citation ##
@misc{halstead2026imagespatialtemporaltomatodata,
      title={Still image and spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels},
      author={Michael Halstead and Esra Guclu and Mohamed Farag and Enrico Pallotta and Christian Hund and Ribana Roscher and Maren Bennewitz and Juergen Gall and Cyrill Stachniss and Chris McCool},
      year={2026},
      eprint={2607.14934},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2607.14934},
}

## Dataset citation ##
@data{FK2/DHTEH1_2026,
  author    = {Halstead, Michael and Guclu, Esra and Farag, Mohamed and Pallotta, Enrico and Hund, Christian and Roscher, Ribana and Bennewitz, Maren and Gall, Juergen and McCool, Chris},
  publisher = {bonndata},
  title     = {BUTom21: Still image tomato dataset for detection and segmentation,
  year      = {2026},
  version   = {V1},
  doi       = {10.60507/FK2/DHTEH1},
  url       = {https://doi.org/10.60507/FK2/DHTEH1}
}

4. Links to other publicly accessible locations of the data <when relevant; best via persistent identifiers, eg. DOI>:

5. Links/relationships to ancillary datasets <when relevant; best via persistent identifiers, eg. DOI>:



D METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data:

The dataset was collected using a mobile robotic platform equipped with RGB-D cameras in a commercial glasshouse.
Images were recorded while the robot moved row-by-row through the crop field, capturing synchronized RGB and depth images across multiple sessions.

2. Methods for processing the data:
293 images are uniformly selected and than annotated by a human operator using "coco annotator tool".

3. Instrument- and/or software-specific information needed to interpret the data:
The dataset was processed using Python-based workflows, including PyTorch for segmentation model

4. People involved in sample collection, processing, analysis and/or submission:
Michael Halstead, Esra Guclu, Chris McCool, Patrick Zimmer, Christian Hund, Yan Wang, Sina Raufi, Efe Incir,
Nelson Pinheiro, Julian Rosbach, Rafay Aamir, Moein Taherkhani, Sicong Pan, Gokul Krishna Gandhi Chenchani

5. Describe any quality-assurance procedures performed on the data: