<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">Images and Annotations for "Evaluating AI-Assisted Pollinator Detection in Agricultural Fields"</titl><IDNo agency="DOI">doi:10.60507/FK2/MCQXPP</IDNo></titlStmt><distStmt><distrbtr source="archive">bonndata</distrbtr><distDate>2026-09-28</distDate></distStmt><verStmt source="archive"><version date="2026-09-28" type="RELEASED">1</version></verStmt><biblCit>Behley, Jens; Chong, Yue Linn, 2026, "Images and Annotations for "Evaluating AI-Assisted Pollinator Detection in Agricultural Fields"", https://doi.org/10.60507/FK2/MCQXPP, bonndata, V1</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl xml:lang="en">Images and Annotations for "Evaluating AI-Assisted Pollinator Detection in Agricultural Fields"</titl><IDNo agency="DOI">doi:10.60507/FK2/MCQXPP</IDNo></titlStmt><rspStmt><AuthEnty affiliation="University of Bonn">Behley, Jens</AuthEnty><AuthEnty affiliation="University of Bonn">Chong, Yue Linn</AuthEnty></rspStmt><prodStmt><software version="3.7.13">Python</software><software version="Release v7.0">Ultralytics YOLOv5</software><grantNo agency="Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy">EXC-2070 - 390732324 (PhenoRob)</grantNo></prodStmt><distStmt><distrbtr source="archive">bonndata</distrbtr><contact affiliation="University of Bonn" email="behley@uni-bonn.de">Behley, Jens</contact><depositr>Behley, Jens</depositr><depDate>2026-09-15</depDate></distStmt><holdings URI="https://doi.org/10.60507/FK2/MCQXPP"/></citation><stdyInfo><subject><keyword xml:lang="en">Agricultural Sciences</keyword></subject><abstract xml:lang="en">Pollinator monitoring is essential for understanding and addressing the decline of insect species and their populations. While computer vision techniques provide new tools for monitoring pollinators, there is a need to develop well-annotated datasets to support artificial intelligence (AI) methods for pollinator detection and to assess their effectiveness. Creating such datasets is challenging because pollinators are small and difficult to detect among plants, even for human annotators. 

We propose an AI-assisted approach for annotating such image data and provide the four iterations of the annotation process and the final refined annotations together with the field images.  The data is organized into folders following a train-validation-test split, where folders contain images and annotations of the splits.</abstract><sumDscr><nation>Germany</nation></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><relPubl><citation><titlStmt><titl>A Computer Vision Dataset for Pollinator Detection under Real Field Conditions
Yue Linn Chong, Phillip Nachtweide, Julian Bauer, Andrée Hamm, Jana Kierdorf, Lukas Drees, Cyrill Stachniss, Jens Behley, Thomas F. Döring, Ribana Roscher, Sabine J. Seidel, Antonia Veronika Mayr
bioRxiv 2025.10.27.682286</titl><IDNo agency="doi">10.1101/2025.10.27.682286</IDNo></titlStmt><biblCit>A Computer Vision Dataset for Pollinator Detection under Real Field Conditions
Yue Linn Chong, Phillip Nachtweide, Julian Bauer, Andrée Hamm, Jana Kierdorf, Lukas Drees, Cyrill Stachniss, Jens Behley, Thomas F. Döring, Ribana Roscher, Sabine J. Seidel, Antonia Veronika Mayr
bioRxiv 2025.10.27.682286</biblCit></citation><ExtLink URI="https://doi.org/10.1101/2025.10.27.682286"/></relPubl><relPubl><citation><titlStmt><titl>"Pollinator monitoring in flower-enriched maize using an iterative AI-assisted annotation pipeline and visual surveys"
The article is currently under review.</titl></titlStmt><biblCit>"Pollinator monitoring in flower-enriched maize using an iterative AI-assisted annotation pipeline and visual surveys"
The article is currently under review.</biblCit></citation></relPubl></othrStdyMat></stdyDscr><otherMat ID="f88861" URI="https://bonndata.uni-bonn.de/api/access/datafile/88861" level="datafile"><labl>AI-assisted-pollinator-detection_Readme.md</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">text/markdown</notes></otherMat><otherMat ID="f70583" URI="https://bonndata.uni-bonn.de/api/access/datafile/70583" level="datafile"><labl>annotations_for_each_iteration.zip</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">application/zip</notes></otherMat><otherMat ID="f70950" URI="https://bonndata.uni-bonn.de/api/access/datafile/70950" level="datafile"><labl>images_labels_train-val-test_data.zip</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">application/zip</notes></otherMat></codeBook>