ArticlePloS one2023
A repeatable scoring system for assessing Smartphone applications ability to identify herbaceous plants.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- Investigating key drivers influencing AI-based detection and identification of plants.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ubiquity of Smartphone applications that aim to identify organisms, including plants, make them potentially useful for increasing people's engagement with the natural world. However, how well such applications actually identify plants has not been compressively investigated nor has an easily repeatable scoring system to compare across plant groups been developed. This study investigated the ability of six common Smartphone applications (Google Lens, iNaturalist, Leaf Snap, Plant Net, Plant Snap, Seek) to identify herbaceous plants and developed a repeatable scoring system to assess their success. Thirty-eight species of plant were photographed in their natural habitats using a standard Smartphone (Samsung Galaxy A50) and assessed in each app without image enhancement. All apps showed considerable variation across plant species and were better able to identify flowers than leaves. Plant Net and Leaf Snap outperformed the other apps. Even the higher preforming apps did not have an accuracy above ~88% and lower scoring apps were considerably below this. Smartphone apps present a clear opportunity to encourage people to engage more with plants. Their accuracy can be good, but should not be considered excellent or assumed to be correct, particularly if the species in question may be toxic or otherwise problematic.
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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.