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Maximizing citizen scientists’ contribution to automated species recognition

Technological advances and data availability have enabled artificial intelligence-driven tools that can increasingly successfully assist in identifying species from images. Especially within citizen science, an emerging source of information filling the knowledge gaps needed to solve the biodiversit...

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Autores principales: Koch, Wouter, Hogeweg, Laurens, Nilsen, Erlend B., Finstad, Anders G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090737/
https://www.ncbi.nlm.nih.gov/pubmed/35538130
http://dx.doi.org/10.1038/s41598-022-11257-x
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author Koch, Wouter
Hogeweg, Laurens
Nilsen, Erlend B.
Finstad, Anders G.
author_facet Koch, Wouter
Hogeweg, Laurens
Nilsen, Erlend B.
Finstad, Anders G.
author_sort Koch, Wouter
collection PubMed
description Technological advances and data availability have enabled artificial intelligence-driven tools that can increasingly successfully assist in identifying species from images. Especially within citizen science, an emerging source of information filling the knowledge gaps needed to solve the biodiversity crisis, such tools can allow participants to recognize and report more poorly known species. This can be an important tool in addressing the substantial taxonomic bias in biodiversity data, where broadly recognized, charismatic species are highly over-represented. Meanwhile, the recognition models are trained using the same biased data, so it is important to consider what additional images are needed to improve recognition models. In this study, we investigated how the amount of training data influenced the performance of species recognition models for various taxa. We utilized a large citizen science dataset collected in Norway, where images are added independently from identification. We demonstrate that while adding images of currently under-represented taxa will generally improve recognition models more, there are important deviations from this general pattern. Thus, a more focused prioritization of data collection beyond the basic paradigm that “more is better” is likely to significantly improve species recognition models and advance the representativeness of biodiversity data.
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spelling pubmed-90907372022-05-12 Maximizing citizen scientists’ contribution to automated species recognition Koch, Wouter Hogeweg, Laurens Nilsen, Erlend B. Finstad, Anders G. Sci Rep Article Technological advances and data availability have enabled artificial intelligence-driven tools that can increasingly successfully assist in identifying species from images. Especially within citizen science, an emerging source of information filling the knowledge gaps needed to solve the biodiversity crisis, such tools can allow participants to recognize and report more poorly known species. This can be an important tool in addressing the substantial taxonomic bias in biodiversity data, where broadly recognized, charismatic species are highly over-represented. Meanwhile, the recognition models are trained using the same biased data, so it is important to consider what additional images are needed to improve recognition models. In this study, we investigated how the amount of training data influenced the performance of species recognition models for various taxa. We utilized a large citizen science dataset collected in Norway, where images are added independently from identification. We demonstrate that while adding images of currently under-represented taxa will generally improve recognition models more, there are important deviations from this general pattern. Thus, a more focused prioritization of data collection beyond the basic paradigm that “more is better” is likely to significantly improve species recognition models and advance the representativeness of biodiversity data. Nature Publishing Group UK 2022-05-10 /pmc/articles/PMC9090737/ /pubmed/35538130 http://dx.doi.org/10.1038/s41598-022-11257-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Koch, Wouter
Hogeweg, Laurens
Nilsen, Erlend B.
Finstad, Anders G.
Maximizing citizen scientists’ contribution to automated species recognition
title Maximizing citizen scientists’ contribution to automated species recognition
title_full Maximizing citizen scientists’ contribution to automated species recognition
title_fullStr Maximizing citizen scientists’ contribution to automated species recognition
title_full_unstemmed Maximizing citizen scientists’ contribution to automated species recognition
title_short Maximizing citizen scientists’ contribution to automated species recognition
title_sort maximizing citizen scientists’ contribution to automated species recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090737/
https://www.ncbi.nlm.nih.gov/pubmed/35538130
http://dx.doi.org/10.1038/s41598-022-11257-x
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