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Weed detection with YOLOv8-seg in UAV-imagery

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dc.contributor.authorMaren Pukrop, Simon Pukrop
dc.date.accessioned2024-04-08T11:56:35Z
dc.date.available2024-04-08T11:56:35Z
dc.date.issued2024
dc.description.abstractAccurate site-specific weed management depends on precise weed localization. In 2023, the YOLOv8 architecture was introduced, providing an accessible instance segmentation tool available in five scaled versions, each with an increasing number of trainable parameters. This study focuses on weed mapping on high-resolution UAV imagery, emphasizing the detection of small weed plants. The research investigates the detection of Cirsium arvense and other weed species in maize. To aid this research, RGB UAV imagery was obtained on three different dates ranging from May to June 2022. The detection of weeds was performed on five different YOLOv8 models. During validation, it was demonstrated that the models' accuracy in detecting weeds with many small plants is comparable, indicating no need for a larger model. Recall is low for small objects measuring only a few cm² across all five models tested but increases as object size increases.en
dc.identifier.doi10.18420/giljt2024_30
dc.identifier.isbn978-3-88579-738-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43906
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.subjectweed mapping
dc.subjectdeep learning
dc.subjectUAV-data
dc.subjectinstance segmentation
dc.titleWeed detection with YOLOv8-seg in UAV-imageryen
dc.typeText/Conference Paper
gi.citation.endPage388
gi.citation.publisherPlaceBonn
gi.citation.startPage383
gi.conference.date27.-28. Februar 2024
gi.conference.locationStuttgart
gi.conference.reviewfull

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P344 - 44. GIL-Jahrestagung 2024 - Fokus: Biodiversität fördern durch digitale Landwirtschaft