<codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5" xml:lang="en"><docDscr><citation><titlStmt><titl xml:lang="en">BUTom-ST21: Spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels</titl><IDNo agency="DOI">doi:10.60507/FK2/TTPCNV</IDNo></titlStmt><distStmt><distrbtr source="archive">bonndata</distrbtr><distDate>2026-07-17</distDate></distStmt><verStmt source="archive"><version date="2026-07-17" type="RELEASED">1</version></verStmt><biblCit>Halstead, Michael; Guclu, Esra; Farag, Mohamed; Pallotta, Enrico; Hund, Christian; Roscher, Ribana; Bennewitz, Maren; Gall, Juergen; McCool, Chris, 2026, "BUTom-ST21: Spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels", https://doi.org/10.60507/FK2/TTPCNV, bonndata, V1</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl xml:lang="en">BUTom-ST21: Spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels</titl><IDNo agency="DOI">doi:10.60507/FK2/TTPCNV</IDNo></titlStmt><rspStmt><AuthEnty affiliation="University of Bonn">Halstead, Michael</AuthEnty><AuthEnty affiliation="University of Bonn">Guclu, Esra</AuthEnty><AuthEnty affiliation="University of Bonn">Farag, Mohamed</AuthEnty><AuthEnty affiliation="University of Bonn">Pallotta, Enrico</AuthEnty><AuthEnty affiliation="University of Bonn">Hund, Christian</AuthEnty><AuthEnty affiliation="University of Bonn">Roscher, Ribana</AuthEnty><AuthEnty affiliation="University of Bonn">Bennewitz, Maren</AuthEnty><AuthEnty affiliation="University of Bonn">Gall, Juergen</AuthEnty><AuthEnty affiliation="CSIRO">McCool, Chris</AuthEnty></rspStmt><prodStmt/><distStmt><distrbtr source="archive">bonndata</distrbtr><contact affiliation="University of Bonn" email="egueclue@uni-bonn.de">Güclü, Esra</contact><depositr>Güclü, Esra</depositr><depDate>2026-07-14</depDate></distStmt><holdings URI="https://doi.org/10.60507/FK2/TTPCNV"/></citation><stdyInfo><subject><keyword xml:lang="en">Agricultural Sciences</keyword></subject><abstract date="2026-07-14" xml:lang="en">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.</abstract><sumDscr><collDate cycle="P1" event="start" date="2021-08-18">2021-08-18</collDate><collDate cycle="P1" event="end" date="2021-09-10">2021-09-10</collDate><dataKind>Images; Numerical; Spreadsheet; Structured; Qualitative</dataKind></sumDscr></stdyInfo><method><dataColl><sources/></dataColl><anlyInfo/></method><dataAccs><setAvail/><useStmt/><notes type="DVN:TOU" level="dv">&lt;a href="http://creativecommons.org/licenses/by/4.0">CC BY 4.0&lt;/a></notes></dataAccs><othrStdyMat><relStdy>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</relStdy><relStdy>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</relStdy><relPubl><citation><titlStmt><titl>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</titl><IDNo agency="doi">10.48550/arXiv.2607.14934</IDNo></titlStmt><biblCit>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</biblCit></citation><ExtLink URI="https://doi.org/10.48550/arXiv.2607.14934"/></relPubl><relPubl><citation><titlStmt><titl>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</titl><IDNo agency="doi">10.1177/02783649251379093</IDNo></titlStmt><biblCit>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</biblCit></citation><ExtLink URI="https://doi.org/10.1177/02783649251379093"/></relPubl></othrStdyMat></stdyDscr><otherMat ID="f54906" URI="https://bonndata.uni-bonn.de/catalog/downloads/54906/annotated_seq_frame_list.yaml" level="datafile"><labl>annotated_seq_frame_list.yaml</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">application/yaml</notes></otherMat><otherMat ID="f54912" 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