# BaleUAVision: Hay Bales UAV Captured Dataset

## TL;DR

BaleUAVision is a new dataset of aerial images of hay bales to improve detection and counting in agriculture.

## Contribution

The paper introduces BaleUAVision, a diverse and annotated UAV dataset for hay bale detection in precision agriculture.

## Key findings

- BaleUAVision includes 2,599 high-resolution images with polygon-based annotations across multiple formats.
- YOLOv11 models trained on the dataset achieved high precision and recall across varying altitudes and regions.
- The dataset supports generalization in real-world UAV operations due to its diverse environmental representation.

## 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.