Predictive Maintenance in Tree Care - TreeAngel

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.

Metadaten
URN:	urn:nbn:de:kobv:526-opus4-20801
Publisher DOI:	https://doi.org/10.52825/th-wildau-ensp.v2i.2932
Author:	Richard Fiebelkorn ORCiD, Rafael Kugel ORCiD, Norman Günther ORCiD, Jörg Reiff-Stephan ORCiD GND
Parent Title (German):	Wildauer Konferenz für Künstliche Intelligenz 2025 (WiKKI25)
Series (Serial Number):	TH Wildau Engineering and Natural Sciences Proceedings (2)
Document Type:	Conference Paper
Language:	English
Year of Publication:	2025
Publisher:	TIB Open Publishing
Place of publication:	Hannover
Publishing Institution:	Technische Hochschule Wildau
Tag:	YOLO; machine learning; predictive maintenance; tree care
Version:	Version of Record
Source:	Fiebelkorn, R., Kugel, R., Günther, N., & Reiff-Stephan, J. (2025). Predictive Maintenance in Tree Care - TreeAngel. TH Wildau Engineering and Natural Sciences Proceedings , 2. https://doi.org/10.52825/th-wildau-ensp.v2i.2932
Faculties an central facilities:	Fachbereich Ingenieur- und Naturwissenschaften
Dewey Decimal Classification:	0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
	6 Technik, Medizin, angewandte Wissenschaften / 63 Landwirtschaft / 635 Gartenpflanzen (Gartenbau)
Funding:	Publikationsfonds für Open-Access-Monografien des Landes Brandenburg
Licence (German):	Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:	2025/09/26

Open Access