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Part 1: Document Description
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Citation |
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Title: |
Images and Annotations for "Evaluating AI-Assisted Pollinator Detection in Agricultural Fields" |
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Identification Number: |
doi:10.60507/FK2/MCQXPP |
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Distributor: |
bonndata |
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Date of Distribution: |
2026-09-28 |
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Version: |
1 |
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Bibliographic Citation: |
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 |
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Citation |
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Title: |
Images and Annotations for "Evaluating AI-Assisted Pollinator Detection in Agricultural Fields" |
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Identification Number: |
doi:10.60507/FK2/MCQXPP |
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Authoring Entity: |
Behley, Jens (University of Bonn) |
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Chong, Yue Linn (University of Bonn) |
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Software used in Production: |
Python |
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Software used in Production: |
Ultralytics YOLOv5 |
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Grant Number: |
EXC-2070 - 390732324 (PhenoRob) |
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Distributor: |
bonndata |
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Access Authority: |
Behley, Jens |
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Depositor: |
Behley, Jens |
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Date of Deposit: |
2026-09-15 |
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Holdings Information: |
https://doi.org/10.60507/FK2/MCQXPP |
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Study Scope |
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Keywords: |
Agricultural Sciences |
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Abstract: |
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. |
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Country: |
Germany |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/licenses/by/4.0">CC BY 4.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
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 |
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Identification Number: |
10.1101/2025.10.27.682286 |
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Bibliographic Citation: |
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 |
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Citation |
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Title: |
"Pollinator monitoring in flower-enriched maize using an iterative AI-assisted annotation pipeline and visual surveys" The article is currently under review. |
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Bibliographic Citation: |
"Pollinator monitoring in flower-enriched maize using an iterative AI-assisted annotation pipeline and visual surveys" The article is currently under review. |
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Label: |
AI-assisted-pollinator-detection_Readme.md |
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Notes: |
text/markdown |
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Label: |
annotations_for_each_iteration.zip |
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Notes: |
application/zip |
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Label: |
images_labels_train-val-test_data.zip |
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Notes: |
application/zip |