AI naturalists might hold the key to unlocking biodiversity data in social media imagery

August Tom A., Pescott Oliver L., Joly Alexis, Bonnet Pierre. 2020. AI naturalists might hold the key to unlocking biodiversity data in social media imagery. Patterns, 1 (7):100116, 11 p.

Journal article ; Article de recherche ; Article de revue à comité de lecture Revue en libre accès total
Published version - Anglais
License Licence Creative Commons.

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Abstract : 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.

Mots-clés Agrovoc : Biodiversité, Intelligence artificielle, Imagerie, Réseaux sociaux, apprentissage machine, Analyse de données, Traitement des données, fouille de données

Mots-clés complémentaires : big data, deep learning

Mots-clés libres : Artificial intelligence, Computer vision, Deep learning, Machine learning, Social media, Biodiversity informatics, Botany, Plants, Big data

Classification Agris : F70 - Plant taxonomy and geography
F40 - Plant ecology
P01 - Nature conservation and land resources
U30 - Research methods
C30 - Documentation and information

Champ stratégique Cirad : CTS 1 (2019-) - Biodiversité

Auteurs et affiliations

  • August Tom A., CEH (GBR) - auteur correspondant
  • Pescott Oliver L., CEH (GBR)
  • Joly Alexis, INRIA (FRA)
  • Bonnet Pierre, CIRAD-BIOS-UMR AMAP (FRA) ORCID: 0000-0002-2828-4389

Source : Cirad-Agritrop (

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