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BUTom-ST21: Spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels

Name: BUTom-ST21: Spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels
Published: 2026-07-17
License: http://creativecommons.org/licenses/by/4.0

Open this dataset in the live repository

Important usage condition: Use and redistribution are governed by CC BY 4.0. Review and comply with the license before using the data.

Persistent identifier: doi:10.60507/FK2/TTPCNV
Published version: 1.0
Publication date: 2026-07-17
License: CC BY 4.0

Description

The BUTom-ST21 alleviates the data paucity issue within the horticulture and tomato domain. We employed BUTom21 still image dataset and generated spatial-temporal tomato dataset using a neural-radiance field based approach. In total we have 217 video sequences (123 train/ 72 valid/ 22 eval) can be used for image-based/video-based detection and segmentation, and multi object tracking. The train and validation sets are weakly labeled(pseudo-labeled), and evaluation set is hand-labeled ground-truth. BUTom-ST21 has 7749, and 4536 pseudo-labeled images across the training and validation set, and 1386 images hand-labeled in the evaluation set. Additionally it has camera poses per sequence, enabling the use of 3D reconstruction purposes in horticultural field.

Creators

Halstead, Michael
Guclu, Esra
Farag, Mohamed
Pallotta, Enrico
Hund, Christian
Roscher, Ribana
Bennewitz, Maren
Gall, Juergen
McCool, Chris

Keywords

Multi-object tracking, video instance segmentation, Spatial-temporal dataset, sweeterno, Tomato Segmentation, Tomato Detection

Files

Before downloading: Use and redistribution are governed by CC BY 4.0. Review and comply with the license before using the data.

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camera_parameters.yaml	application/yaml	412	MD5 afe19eadced5bf8a300c17a1beb603ae
butomst21_rgb.tar.gz.ac	application/pkix-attr-cert	4294967296	MD5 d214efd517a976dc7542867c27d9b72f
train_val_eval_splits.yaml	application/yaml	1760	MD5 e871ff2417f953460f7c932ae3d6c4b9
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butomst21_eval_annotations.tar.gz	application/gzip	50518190	MD5 d7ef688a68e68ad43bbf69c154a0905c
butomst21_rgb.tar.gz.af	application/octet-stream	4294967296	MD5 09f2755f290b9218d8733c6f9df23a7a
butomst21_depth.tar.gz.aa	audio/x-pn-audibleaudio	4294967296	MD5 76942c234f11c012b3d51998cd847ada
annotated_seq_frame_list.yaml	application/yaml	408077	MD5 764146b9b56510094b8bbf78b357b821
butomst21_depth.tar.gz.ab	application/octet-stream	2413047198	MD5 3f2ed6b02c893428e4031adddf1fbe65
butomst21_rgb.tar.gz.ag	image/x-applix-graphics	679102940	MD5 72f3dfcbecedf65dd81a08467065a1ce
butomst21_pseudo_labels_Yolo26.tar.gz	application/gzip	388118162	MD5 414d09b95367ff18ab0d655ab5519a41
pagnerf_seq_frames.yaml	application/yaml	397689	MD5 6f494696cf67f37b4aac48ac1e831f41
butomst21_pseudo_labels_m2f.tar.gz	application/gzip	358440411	MD5 e2ddc2fea9e696fa530b2881ea2930a4
butomst21_poses.tar.gz	application/gzip	3302790601	MD5 1f68acb8c9588717b50289a8646efdea
image_camera_row_identity.yaml	application/yaml	7539	MD5 799ed334f9109aa8071d27d1fb033076
butomst21_rgb.tar.gz.ad	application/octet-stream	4294967296	MD5 3e4cbeea311bdbe8c65bf305e329a516
Halstead_BUTomST21_ReadMe.txt	text/plain	7445	MD5 9ce78db5d4989c3735ac02aab9983cf0
BUTomST21_structure.md	text/markdown	9869	MD5 9e9d44559707091786b71fb73054f008

Citation

Halstead, Michael; Guclu, Esra; Farag, Mohamed; Pallotta, Enrico; Hund, Christian; Roscher, Ribana; Bennewitz, Maren; Gall, Juergen; McCool, Chris, 2026-07-17, BUTom-ST21: Spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels, doi:10.60507/FK2/TTPCNV, V1.0

Additional Dataverse fields

Id	785
Dataset Type	dataset
Internal Version Number	26
Latest Version Publishing State	RELEASED
Release Time	2026-07-17T11:56:00Z
Create Time	2026-07-14T22:13:33Z
Citation Date	2026-07-17
File Access Request	True

Export metadata

Static metadata exports available for this published dataset version:

Complete Dataverse metadata

Citation Metadata

Title title

BUTom-ST21: Spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels

Alternative URL alternativeURL

https://github.com/Agricultural-Robotics-Bonn/BUTom21-ST21

Author author

Author Name
Halstead, Michael
Author Affiliation
University of Bonn
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
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https://orcid.org/0000-0001-7185-9304
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Person Name
Halstead, Michael
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Scheme
ORCID
@type
https://schema.org/Person
Author Name
Guclu, Esra
Author Affiliation
University of Bonn
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
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False
Type Class
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Value
https://orcid.org/0000-0003-1106-5566
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Person Name
GUCLU, ESRA
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https://orcid.org/0000-0003-1106-5566
Scheme
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@type
https://schema.org/Person
Author Name
Farag, Mohamed
Author Affiliation
University of Bonn
Author Identifier Scheme
ORCID
Author Identifier
Type Name
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Person Name
Farag, Mohamed
@id
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Scheme
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@type
https://schema.org/Person
Author Name
Pallotta, Enrico
Author Affiliation
University of Bonn
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
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False
Type Class
primitive
Value
https://orcid.org/0009-0003-9276-0540
Expandedvalue
Person Name
Pallotta, Enrico
@id
https://orcid.org/0009-0003-9276-0540
Scheme
ORCID
@type
https://schema.org/Person
Author Name
Hund, Christian
Author Affiliation
University of Bonn
Author Name
Roscher, Ribana
Author Affiliation
University of Bonn
Author Identifier Scheme
ORCID
Author Identifier
Type Name
authorIdentifier
Multiple
False
Type Class
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https://orcid.org/0000-0003-0094-6210
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Person Name
Roscher, Ribana
@id
https://orcid.org/0000-0003-0094-6210
Scheme
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@type
https://schema.org/Person
Author Name
Bennewitz, Maren
Author Affiliation
University of Bonn
Author Identifier Scheme
ORCID
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False
Type Class
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Value
https://orcid.org/0000-0003-4343-3028
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Person Name
Bennewitz, Maren
@id
https://orcid.org/0000-0003-4343-3028
Scheme
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@type
https://schema.org/Person
Author Name
Gall, Juergen
Author Affiliation
University of Bonn
Author Identifier Scheme
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Person Name
Gall, Juergen
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Author Name
McCool, Chris
Author Affiliation
CSIRO
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McCool, Chris
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Dataset Contact datasetContact

Dataset Contact Name
Güclü, Esra
Dataset Contact Affiliation
University of Bonn
Dataset Contact Email
egueclue@uni-bonn.de

Ds Description dsDescription

Ds Description Value
The BUTom-ST21 alleviates the data paucity issue within the horticulture and tomato domain. We employed BUTom21 still image dataset and generated spatial-temporal tomato dataset using a neural-radiance field based approach. In total we have 217 video sequences (123 train/ 72 valid/ 22 eval) can be used for image-based/video-based detection and segmentation, and multi object tracking. The train and validation sets are weakly labeled(pseudo-labeled), and evaluation set is hand-labeled ground-truth. BUTom-ST21 has 7749, and 4536 pseudo-labeled images across the training and validation set, and 1386 images hand-labeled in the evaluation set. Additionally it has camera poses per sequence, enabling the use of 3D reconstruction purposes in horticultural field.
Ds Description Date
2026-07-14

Subject subject

Agricultural Sciences

Free Keyword freeKeyword

Free Keyword Value
Multi-object tracking
Free Keyword Value
video instance segmentation
Free Keyword Value
Spatial-temporal dataset
Free Keyword Value
sweeterno
Free Keyword Value
Tomato Segmentation
Free Keyword Value
Tomato Detection

Publication publication

Publication Relation Type
IsSupplementTo
Publication Citation
Halstead, Michael; Guclu, Esra; Farag, Mohamed; Pallotta, Enrico; Hund, Christian; Roscher, Ribana; Bennewitz, Maren; Gall, Juergen; McCool, Chris, 2026, "Still image and spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels", https://arxiv.org/abs/2607.14934, arXiv
Publication IDType
doi
Publication IDNumber
10.48550/arXiv.2607.14934
Publication URL
https://doi.org/10.48550/arXiv.2607.14934
Publication Relation Type
IsSupplementTo
Publication Citation
Guclu, Esra; Halstead, Michael; Denman, Simon; McCool, Chris, 2025, "BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data", The International Journal of Robotics Research (IJRR), 2025
Publication IDType
doi
Publication IDNumber
10.1177/02783649251379093
Publication URL
https://doi.org/10.1177/02783649251379093

Depositor depositor

Güclü, Esra

Date Of Deposit dateOfDeposit

2026-07-14

Date Of Collection dateOfCollection

Date Of Collection Start
2021-08-18
Date Of Collection End
2021-09-10

Kind Of Data kindOfData

Images; Numerical; Spreadsheet; Structured; Qualitative

Related Datasets relatedDatasets

Halstead, Michael; Guclu, Esra; Farag, Mohamed; Pallotta, Enrico; Hund, Christian; Roscher, Ribana; Bennewitz, Maren; Gall, Juergen; McCool, Chris, 2026, "BUTom21: Still image tomato dataset for detection and segmentation", https://doi.org/10.60507/FK2/DHTEH1, bonndata
Guclu, Esra; Halstead, Michael; Denman, Simon; McCool, Chris, 2025, "BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data", https://doi.org/10.60507/FK2/NUMVO1, bonndata

Computational Workflow Metadata

Workflow Type workflowType

Bash Script
Other Python-based workflow

Workflow Code Repository workflowCodeRepository

https://github.com/Agricultural-Robotics-Bonn/BUTom21-ST21

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