Images and Annotations for "Evaluating AI-Assisted Pollinator Detection in Agricultural Fields"https://doi.org/10.60507/FK2/MCQXPPBehley, JensChong, Yue Linnbonndata2026-09-282026-09-28T10:41:19ZPollinator 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.Agricultural SciencesA 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, doi, 10.1101/2025.10.27.682286, https://doi.org/10.1101/2025.10.27.682286"Pollinator monitoring in flower-enriched maize using an iterative AI-assisted annotation pipeline and visual surveys" The article is currently under review.2026-09-28Behley, Jens2026-09-15GermanyCC BY 4.0