Predictive Maintenance in Tree Care - TreeAngel
DOI:
https://doi.org/10.52825/th-wildau-ensp.v2i.2932Keywords:
Predictive Maintenance, Machine Learning, YOLO, Tree CareAbstract
Ensuring the traffic safety of trees poses a significant challenge for urban authorities. Conventional manual inspection methods are both time-consuming and resource-intensive, and they are subject to human error. The following paper presents an innovative system for automated tree condition assessment using modern camera technologies and artificial intelligence (AI). As part of a feasibility study, image data generated by various camera systems was analyzed. Based on this data, a YOLOv8 model was trained, which enables precise detection of trees and damage, such as deadwood. The results of the prototype system presented are promising in terms of accuracy and efficiency, suggesting the potential to supplement or replace manual spections with automated procedures.
The results of this study lay the foundation for sustainable and scalable approaches in tree
care and can contribute to increasing public safety and efficiency in urban management.
Downloads
References
M. Jonsson, J. Bengtsson, J. Moen, and T. Snäll, “Tree damage risk across gradients in tree species richness and stand age: Implications for adaptive forest management,” Ecosphere, vol. 15, no. 11, e70071, 2024. DOI: https://doi.org/10.1002/ecs2.70071. eprint: https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.70071. [Online]. Available: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecs2.70071.
D. Haase and R. Hellwig, “Effects of heat and drought stress on the health status of six urban street tree species in leipzig, germany,” Trees, Forests and People, vol. 8, p. 100 252, 2022, ISSN: 2666-7193. DOI: https : / / doi.org/10.1016/j.tfp.2022.100252. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666719322000590.
L. Portoghesi, E. Masini, A. Tomao, and M. Agrimi, “Could climate change and urban growth make europeans regard urban trees as an additional source of danger?” Frontiers in Forests and Global Change, vol. 6, Apr. 2023, ISSN: 2624-893X. DOI: 10.3389/ffgc. 2023.1155016. [Online]. Available: http://dx.doi.org/10.3389/ffgc.2023.1155016.
B. Prell, S. Wilbers, N. Günther, and J. Reiff-Stephan, “Will my job be automated? Fathoming current and persisting impediments for automation,” in Smart Technologies for a Sustainable Future, M. E. Auer, R. Langmann, D. May, and K. Roos, Eds., Cham: Springer Nature Switzerland, 2024, pp. 155–166, ISBN: 978-3-031-61891-8.
D. Gugutishvili, S. Kalyazina, and J. Reiff-Stephan, “Identification and classification of the effects of digital transformation on business,” in Algorithms and Solutions Based on Computer Technology, C. Jahn, L. Ungvári, and I. Ilin, Eds., Cham: Springer International Publishing, 2022, pp. 391–401, ISBN: 978-3-030-93872-7.[6] J. Reiff-Stephan, “Humanity-centered production - Automatisierung für die Gesellschaft,” in Tagungsband der Konferenz der Mechatronik Plattform Österreich 2024: Smart Technologies in Mechatronics, Nov. 2024, p. 36.
G. Jocher, A. Chaurasia, and J. Qiu, Ultralytics yolov8, version 8.0.0, 2023. [Online]. Available: https://github.com/ultralytics/ultralytics.
U. Inc. “Ultralytics yolov8.” (2023), [Online]. Available: https://docs.ultralytics.com/de/models/yolov8/#can-i-benchmark-yolov8-models-for-performance (visited on 11/29/2024).
R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. New York, NY, USA: Cambridge University Press, 2003, ISBN: 0521540518.
A. Jahani and M. Saffariha, “Tree failure prediction model (tfpm): Machine learning techniques comparison in failure hazard assessment of platanus orientalis in urban forestry,” Natural Hazards, vol. 110, no. 2, pp. 881–898, Aug. 2021, ISSN: 1573-0840. DOI: 10.1007/s11069-021-04972-7. [Online]. Available: http://dx.doi.org/10.1007/s11069-021-04972-7.
D. Vidal and R. Pitarma, “Infrared thermography applied to tree health assessment: A review,” Agriculture, vol. 9, no. 7, 2019, ISSN: 2077-0472. DOI: 10 . 3390 /agriculture9070156. [Online]. Available: https://www.mdpi.com/2077-0472/9/7/156.
Downloads
Published
How to Cite
Conference Proceedings Volume
Section
License
Copyright (c) 2025 Richard Fiebelkorn, Rafael Kugel, Norman Günther, Jörg Reiff-Stephan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Funding data
-
Bundesministerium für Wirtschaft und Energie
Grant numbers 01MF23002D