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A User-friendly and Powerful R Analysis of Large-scale Datasets

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BaleUAVision: Hay Bales UAV Captured Dataset.

Georgios D Karatzinis¹, Socratis Gkelios², Athanasios Ch Kapoutsis²

¹Information Technologies Institute (ITI), Centre of Research and Technology Hellas (CERTH), Thessaloniki, Greece. gkaratzi@iti.gr.

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|January 29, 2026

Summary

A new dataset, BaleUAVision, aids precision agriculture by enabling efficient hay bale detection and counting using Unmanned Aerial Vehicles (UAVs). This resource improves yield estimation and resource management in farming.

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Area of Science:

Agricultural Engineering
Computer Vision
Remote Sensing

Background:

Efficient hay bale detection is crucial for precision agriculture, impacting yield estimation and resource management.
Existing datasets often lack diversity in environmental conditions and annotation quality.
Unmanned Aerial Vehicles (UAVs) offer a promising platform for agricultural monitoring.

Purpose of the Study:

To introduce BaleUAVision, a comprehensive dataset for hay bale detection and counting.
To provide high-quality annotated data captured under diverse agricultural conditions.
To facilitate the development of robust hay bale detection models for UAV applications.

Main Methods:

Collected 2,599 high-resolution RGB images using UAVs across 16 diverse fields in Northern Greece.
Annotated hay bales with polygon-based semantic segmentation in multiple formats (COCO, CSV, JSON, YOLO, segmentation masks).
Generated high-quality orthomosaics for spatial analysis and validated models using YOLOv11.

Main Results:

BaleUAVision dataset demonstrated effectiveness in training robust hay bale detection models.
Achieved high precision and recall in detection across varying geographic locations and altitudes.
Validated successful generalization of models across different regions (Xanthi, Drama) and flight altitudes.

Conclusions:

BaleUAVision dataset significantly enhances hay bale detection capabilities in precision agriculture.
The dataset's diverse data and comprehensive annotations support real-world UAV operations.
Public availability following FAIR principles promotes wider research community adoption and advancement.