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A repeatable scoring system for assessing Smartphone applications ability to identify herbaceous plants.

Neil Campbell1, Julie Peacock2, Karen L Bacon1

  • 1Botany & Plant Science, School of Natural Science, University of Galway, Galway, Ireland.

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Smartphone apps can help people engage with plants, but their accuracy varies. Plant Net and Leaf Snap performed best, though even top apps had limitations in identifying plant species.

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Area of Science:

  • Botany
  • Computer Science
  • Citizen Science

Background:

  • Smartphone applications for organism identification are widespread.
  • Plant identification apps offer potential for public engagement with nature.
  • Limited research exists on the accuracy of these plant identification tools.

Purpose of the Study:

  • To evaluate the accuracy of six common smartphone applications in identifying herbaceous plants.
  • To develop a repeatable scoring system for assessing plant identification app performance.
  • To compare the effectiveness of different apps across various plant species.

Main Methods:

  • Photographed 38 herbaceous plant species in natural habitats using a standard smartphone.
  • Assessed identification accuracy of Google Lens, iNaturalist, Leaf Snap, Plant Net, Plant Snap, and Seek without image enhancement.
  • Developed and applied a repeatable scoring system to evaluate app performance.

Main Results:

  • Significant variation in identification accuracy was observed across different plant species and apps.
  • Apps demonstrated higher accuracy in identifying flowers compared to leaves.
  • Plant Net and Leaf Snap showed superior performance over other applications.
  • Top-performing apps achieved accuracies around 88%, while others scored considerably lower.

Conclusions:

  • Smartphone plant identification apps can increase public engagement with flora.
  • App accuracy is variable and should not be assumed as infallible, especially for potentially problematic species.
  • Further development is needed to improve the reliability of plant identification technologies.