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Mapping invasive Lupine on grasslands using UAV images and deep learning

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dc.contributor.authorWijesingha, Jayan
dc.contributor.authorSchulze-Brüninghoff, Damian
dc.contributor.authorWachendorf, Michael
dc.date.accessioned2024-04-08T11:56:36Z
dc.date.available2024-04-08T11:56:36Z
dc.date.issued2024
dc.description.abstractSemi-natural grasslands are threatened by invasive species. This study employs high-resolution images captured by an unmanned aerial vehicle (UAV) and deep learning techniques to map Lupine (Lupinus polyphyllus Lindl.) in grasslands, which is one of the most common invasive species in European grasslands. The methodology involves RGB image acquisition, structure from motion processing, canopy height modelling, and deep learning semantic segmentation model development. The resulting models were trained on RGB data, canopy surface height data, and their combination. The models demonstrate high accuracy and efficacy in identifying Lupine distribution. These models offer a valuable tool for continuously monitoring and managing invasive Lupine, with potential applications in similar environments without retraining. The method is beneficial for early-stage invasion detection, facilitating more targeted management efforts for ecologists.en
dc.identifier.doi10.18420/giljt2024_27
dc.identifier.isbn978-3-88579-738-8
dc.identifier.issn2944-7682
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43921
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.subjectinvasive species
dc.subjectgrassland
dc.subjectdeep learning
dc.subjectsegmentation
dc.subjectUAV images
dc.titleMapping invasive Lupine on grasslands using UAV images and deep learningen
dc.typeText/Conference Paper
gi.citation.endPage466
gi.citation.publisherPlaceBonn
gi.citation.startPage461
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