10.60507/FK2/TTPCNV
Halstead, Michael
Michael
Halstead
https://orcid.org/0000-0001-7185-9304
University of Bonn
Guclu, Esra
Esra
Guclu
https://orcid.org/0000-0003-1106-5566
University of Bonn
Farag, Mohamed
Mohamed
Farag
https://orcid.org/0000-0003-4301-1140
University of Bonn
Pallotta, Enrico
Enrico
Pallotta
https://orcid.org/0009-0003-9276-0540
University of Bonn
Hund, Christian
Christian
Hund
University of Bonn
Roscher, Ribana
Ribana
Roscher
https://orcid.org/0000-0003-0094-6210
University of Bonn
Bennewitz, Maren
Maren
Bennewitz
https://orcid.org/0000-0003-4343-3028
University of Bonn
Gall, Juergen
Juergen
Gall
https://orcid.org/0000-0002-9447-3399
University of Bonn
McCool, Chris
Chris
McCool
https://orcid.org/0000-0002-0577-1299
CSIRO
BUTom-ST21: Spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels
bonndata
2026
Agricultural Sciences
Güclü, Esra
Esra
Güclü
University of Bonn
2026-07-14
2026-07-17
2021-08-18/2021-09-10
Images; Numerical; Spreadsheet; Structured; Qualitative
10.48550/ARXIV.2607.14934
10.1177/02783649251379093
10.60507/FK2/TTPCNV/FR2YOW
10.60507/FK2/TTPCNV/NMMYS7
10.60507/FK2/TTPCNV/D8IYLU
10.60507/FK2/TTPCNV/6QDTMU
10.60507/FK2/TTPCNV/MQSDLS
10.60507/FK2/TTPCNV/X4R789
10.60507/FK2/TTPCNV/IYP9P2
10.60507/FK2/TTPCNV/HTZ3UI
10.60507/FK2/TTPCNV/PLCT6T
10.60507/FK2/TTPCNV/ZOFGI1
10.60507/FK2/TTPCNV/XW8ZUX
10.60507/FK2/TTPCNV/H21XF8
10.60507/FK2/TTPCNV/WJBVQZ
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10.60507/FK2/TTPCNV/KTW8AN
10.60507/FK2/TTPCNV/EYUJNI
10.60507/FK2/TTPCNV/4WMCMP
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application/yaml
application/gzip
application/yaml
application/yaml
application/octet-stream
application/octet-stream
audio/x-pn-audibleaudio
application/octet-stream
image/x-applix-graphics
application/gzip
application/octet-stream
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application/yaml
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application/pkix-attr-cert
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1.0
Creative Commons Attribution 4.0 International License.
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.