ArticlePatterns (New York, N.Y.)2020
AI Naturalists Might Hold the Key to Unlocking Biodiversity Data in Social Media Imagery.
Article in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Evaluation of the e-Surveyor Mobile Application for Undertaking Plant Surveys and Predicting Habitat Type.Ecology and evolution · 2026Article
- Investigating key drivers influencing AI-based detection and identification of plants.PloS one · 2026Article
- Citizen Science Quickly Reveals the Argentine Ant Linepithema humile (Mayr, 1868) Distribution in an Invaded Urban Area and Provides Unexpected Findings.Neotropical entomology · 2025Article
- Expanding phenological insights: automated phenostage annotation with community science plant images.International journal of biometeorology · 2025Article
- First national survey of terrestrial biodiversity using airborne eDNA.Scientific reports · 2025Article
- Evaluating the method reproducibility of deep learning models in biodiversity research.PeerJ. Computer science · 2025Article
- Exploring the landscape of automated species identification apps: Development, promise, and user appraisal.Bioscience · 2024Review
- Collecting and managing in situ banana genetic resources information (Musa spp.) using online resources and citizen science.Database : the journal of biological databases and curation · 2024Article
- Deep learning in terrestrial conservation biology.Biologia futura · 2023Review
- Increasing biodiversity knowledge through social media: A case study from tropical Bangladesh.Bioscience · 2023Article
- Citizen Science and Monitoring Forest Pests: a Beneficial Alliance?Current forestry reports · 2023Review
- Citizen science and online data: Opportunities and challenges for snake ecology and action against snakebite.Toxicon: X · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.