Abstract
Efficient hay bale detection and counting are essential tasks within modern precision agriculture, significantly impacting yield estimation, logistics, and sustainable resource management. To address current limitations in dataset quality and environmental representation, we introduce BaleUAVision, a comprehensive dataset consisting of 2,599 high-resolution RGB images, each containing numerous human-annotated hay bales. Captured by Unmanned Aerial Vehicles (UAVs) across 16 diverse agricultural fields in Northern Greece, the dataset includes varying flight altitudes (50–100 meters), diverse speeds (3.7–5 m/s), and overlapping strategies to ensure robust data representation. BaleUAVision provides rich annotations through polygon-based semantic segmentation in multiple formats (COCO, CSV, JSON, YOLO, segmentation masks) and high-quality orthomosaics for precise spatial analysis. Technical validation demonstrated the dataset’s effectiveness in training robust hay bale detection models using YOLOv11, achieving high precision and recall under varying geographic and altitude conditions. Specifically, the dataset supported effective generalization across geographically distinct areas (Xanthi and Drama regions) and varying altitudes, highlighting its utility in real-world UAV operations. The dataset and supplementary tools, scripts, and analyses are publicly available on Zenodo and GitHub respectively, following FAIR principles to support wide-reaching applicability within the research community.
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Data availability
All data comprising the BaleUAVision dataset are publicly available in the Zenodo repository29.
Code availability
All related code, including data statistics, insights, usage python scripts, and indicative examples are provided at the living page of the project30.
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Acknowledgements
This research was funded by European Union’s Horizon Europe Innovation Action iDriving, under Grant Agreement No 101147004. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the granting authority can be held responsible for them.
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Conceptualization, G.K.; data acquisition, G.K.; data annotation G.K.; methodology, G.K. and S.G.; technical validation, G.K. and S.G.; formal analysis, G.K. and S.G.; writing—original draft preparation, G.K., S.G. and A.K.; writing—review and editing, G.K., S.G. and A.K.; visualization, G.K., S.G. and A.K. All authors have read and agreed to the published version of the manuscript.
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Karatzinis, G.D., Gkelios, S. & Kapoutsis, A.C. BaleUAVision: Hay Bales UAV Captured Dataset. Sci Data (2026). https://doi.org/10.1038/s41597-026-06622-8
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DOI: https://doi.org/10.1038/s41597-026-06622-8
