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Adapting a Globally-Trained Plant Identification Model for Multi-Species Detection of Invasive Alien Plants in Roadside Imagery

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Published in:Biodiversity Information Science and Standards
Format: Online Article RSS Article
Published: 2026
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container_title Biodiversity Information Science and Standards
description
discipline_display Library and Information Science2
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genre Journal Article
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institution FRELIP
journal_source_facet Biodiversity Information Science and Standards
publishDate 2026
publishDateSort 2026
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spellingShingle Adapting a Globally-Trained Plant Identification Model for Multi-Species Detection of Invasive Alien Plants in Roadside Imagery
Library and Information Science2
General
Library and Information Science2
sub_discipline_display General
sub_discipline_facet General
subject_display Library and Information Science2
General
Library and Information Science2
Library and Information Science2
General
Library and Information Science2
subject_facet Library and Information Science2
General
Library and Information Science2
title Adapting a Globally-Trained Plant Identification Model for Multi-Species Detection of Invasive Alien Plants in Roadside Imagery
title_auth Adapting a Globally-Trained Plant Identification Model for Multi-Species Detection of Invasive Alien Plants in Roadside Imagery
title_full Adapting a Globally-Trained Plant Identification Model for Multi-Species Detection of Invasive Alien Plants in Roadside Imagery
title_fullStr Adapting a Globally-Trained Plant Identification Model for Multi-Species Detection of Invasive Alien Plants in Roadside Imagery
title_full_unstemmed Adapting a Globally-Trained Plant Identification Model for Multi-Species Detection of Invasive Alien Plants in Roadside Imagery
title_short Adapting a Globally-Trained Plant Identification Model for Multi-Species Detection of Invasive Alien Plants in Roadside Imagery
title_sort adapting a globally-trained plant identification model for multi-species detection of invasive alien plants in roadside imagery
topic Library and Information Science2
General
Library and Information Science2
url https://biss.pensoft.net/article/183020/