Logo des Repositoriums
 

CherryGraph: Encoding digital twins of cherry trees into a knowledge graph based on topology

dc.contributor.authorAndreas Gilson, Mareike Weule
dc.date.accessioned2024-04-08T11:56:35Z
dc.date.available2024-04-08T11:56:35Z
dc.date.issued2024
dc.description.abstractCherryGraph is a structural framework for mapping trees into an ontology-based knowledge graph that can be used as database backend for digital twins. Based on the reconstructed 3D topology of scanned trees, information is encoded in a knowledge graph that resembles the real canopy structure of trees. Thus, CherryGraph enables consistent navigation within the branching system of a tree over different time points regardless of natural fluctuations. The resulting knowledge graph can then be queried for arbitrary use cases or aggregated on different hierarchy levels. We demonstrate the potential of CherryGraph by using data of real cherry trees from the 2023 cherry season with exemplary queries that can be extended to include spatial and temporal dimensions for comparing indicators like elongation growth of shoots or tracking the development of other various tree traits over time.en
dc.identifier.isbn978-3-88579-738-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43908
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 344
dc.subjectcherry tree
dc.subjectdigital horticulture
dc.subjectdigital twin
dc.subjectknowledge graph
dc.subjectorchard
dc.subjectphenotyping
dc.subjecttree topology
dc.subjectprecision farming
dc.titleCherryGraph: Encoding digital twins of cherry trees into a knowledge graph based on topologyen
dc.typeText/Conference Paper
gi.citation.endPage82
gi.citation.publisherPlaceBonn
gi.citation.startPage71
gi.conference.date27.-28. Februar 2028
gi.conference.locationStuttgart
gi.conference.reviewfull

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
GIL_2024_Gilson_71-82.pdf
Größe:
660.46 KB
Format:
Adobe Portable Document Format