AI Naturalists Might Hold the Key to Unlocking Biodiversity Data in Social Media Imagery.
artificial intelligence
big data
biodiversity
botany
computer vision
deep learning
informatics
machine learning
plants
social media
Journal
Patterns (New York, N.Y.)
ISSN: 2666-3899
Titre abrégé: Patterns (N Y)
Pays: United States
ID NLM: 101767765
Informations de publication
Date de publication:
09 Oct 2020
09 Oct 2020
Historique:
received:
29
05
2020
revised:
04
08
2020
accepted:
07
09
2020
entrez:
18
11
2020
pubmed:
19
11
2020
medline:
19
11
2020
Statut:
epublish
Résumé
The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword "flower" across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis.
Identifiants
pubmed: 33205140
doi: 10.1016/j.patter.2020.100116
pii: S2666-3899(20)30157-4
pmc: PMC7660428
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100116Informations de copyright
© 2020 The Authors.
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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