Skip to main content

Swipe Right: a Comparison of Accuracy of Plant Identification Apps for Toxic Plants

Abstract

Introduction

Plant identification applications for use on smartphones have been increasing in availability, accuracy, and utilization. We aimed to perform an introductory study to determine if a plant identification application (ID app) used on a smartphone could identify toxic plants, and to compare apps to determine which is most reliable.

Methods

We compared three popular iPhone plant ID apps, PictureThis (PT), PlantSnap (PS), and Pl@ntNet (PN), used to identify 17 commonly encountered toxic plants. Apps were used to photograph the entire plant, leaves, and flowers of ≥ 10 different plants for each species. Two toxicologists performed plant identification with confirmation of identification performed by a botanist, and inter-researcher agreement was confirmed. For each plant species, scores for accuracy of app identification of leaves, flowers, and whole plant were combined to create an overall composite score used to compare accuracy of each app (95% C.I.).

Results

PictureThis had the best performance with 10/17 (59% [36 to 78]) plant species identified 100% correctly, as opposed to 8/17 (47% [26 to 69]) for Pl@ntNet and 1/17 for PlantSnap (5.8% [1.1 to 27]).

Conclusion

A plant identification app may be a useful tool to assist healthcare providers and the public in identifying toxic plants.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. Gummin DD, Mowry JB, Spyker DA, Brooks DE, Osterthaler KM, Banner W. 2017 annual report of the American Association of Poison Control Centers National Poison Data System (NPDS): 35th annual report. Clin Toxicol. 2018;56(12):1213–415. https://doi.org/10.1080/15563650.2018.1533727.

    Article  Google Scholar 

  2. Gummin DD, Mowry JB, Spyker DA, Brooks DE, Fraser MO, Banner W. 2016 annual report of the American Association of Poison Control Centers National Poison Data System (NPDS): 34th annual report. Clin Toxicol. 2017;55(10):1072–254. https://doi.org/10.1080/15563650.2017.1388087.

    Article  Google Scholar 

  3. Enfield B, Brooks DE, Welch S, Roland M, Klemens J, Greenlief K, et al. Human plant exposures reported to a regional (southwestern) poison control center over 8 years. J of Med Tox. 2018;14:74–8. https://doi.org/10.1007/s13181-017-0643-3.

    Article  Google Scholar 

  4. Fuchs J, Rauber-Lüthy C, Kupferschmidt H, Kupper J, Kullak-Ublick GA, Ceschi A. Acute plant poisoning: analysis of clinical features and circumstances of exposure. Clin Toxicol. 2011;49(7):671–80. https://doi.org/10.3109/15563650.2011.597034.

    Article  Google Scholar 

  5. McKinney PE, Gomez HF, Phillips S, Brent J. The fax machine: a new method of plant identification. J Toxicol Clin Toxicol. 1993;31(4):663–5. https://doi.org/10.3109/15563659309025771.

    CAS  Article  PubMed  Google Scholar 

  6. Joly A, Goëau H, Bonnet P, Bakić V, Barbe J, Selmi S, et al. Interactive plant identification based on social image data. Ecol Inf. 2014;23:22–34. https://doi.org/10.1016/j.ecoinf.2013.07.006.

    Article  Google Scholar 

  7. Goëau H, Bonnet P, Joly A. Plant identification in an open-world (LifeCLEF 2016). Évora: CLEF: Conference and Labs of the Evaluation Forum; 2016. p. 428–39.

    Google Scholar 

  8. Sun Y, Liu Y, Wang G, Zhang H. Deep learning for plant identification in natural environment. Comput Intell Neurosci. 2017;2017:7361042 10.1155.2017/7361042.

    PubMed  PubMed Central  Google Scholar 

  9. Patil AA, Bhagat KS. Plants identification by leaf shape recognition: a review. Int J Eng Trends Tech. 2016;35(8):359–61. https://doi.org/10.14445/22315381/IJETT-V35P273.

    Article  Google Scholar 

  10. Lurie Y, Fainmesser P, Yosef M, Bentur Y. Remote identification of poisonous plants by cell-phone camera and online communication. Isr Med Assoc J. 2008 Nov;10(11):802–3.

    PubMed  Google Scholar 

  11. Hossain J, Amin MA. Leaf shape identification based plant biometrics. In: International conference on computer and information technology, pp. 458–463, Dhaka (2010) https://doi.org/10.1109/ICCITECHN.2010.5723901.

  12. Burrows GE, Krebs GL, Kirchoff BK. ‘Visual learning–agricultural plants of the Riverina’–a new application for helping veterinary students recognize poisonous plants. Biosci Educ. 2014;1:1–3. https://doi.org/10.11120/BEEJ.2014.00028.

    Article  Google Scholar 

  13. Bruni I, de Mattia F, Galimberti A, Galasso G, Banfi E, Casiraghi M, et al. Identification of poisonous plants by DNA barcoding approach. Int J Legal Med. 2010 Nov;124(6):595–603. https://doi.org/10.1007/s00414-010-0447-3.

    Article  PubMed  Google Scholar 

  14. Aquila I, Ausania F, di Nunzio C, Serra A, Boca S, Capelli A, et al. The role of forensic botany in crime scene investigation: case report and review of literature. J Forensic Sci. 2014;59(3):820–4. https://doi.org/10.1111/1556-4029.12401.

    Article  PubMed  Google Scholar 

  15. Sandionigi A, Galimberti A, Labra M, Ferri E, Panunzi E, de Mattia F, et al. Analytical approaches for DNA barcoding data–how to find a way for plants? Plant Biosyst. 2012;146(4):805–13. https://doi.org/10.1080/11263504.2012.740084.

    Article  Google Scholar 

  16. Mezzasalma V, Ganopoulos I, Galimberti A, Cornara L, Ferri E, Labra M. Poisonous or non-poisonous plants? DNA-based tools and applications for accurate identification. Int J Legal Med. 2017;131(1):1–19. https://doi.org/10.1007/s00414-016-1460-y.

    Article  PubMed  Google Scholar 

Download references

Funding

None.

Author information

Authors and Affiliations

Authors

Contributions

Authors JO and CT contributed to the study conception and design. Material preparation, data collection, and analysis were performed by JO and CT. SM performed plant identity verification. The manuscript was written by JO and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jenna Otter.

Ethics declarations

Conflict of Interest

None.

Additional information

Supervising Editor: Mark B. Mycyk, MD

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Previous Presentations: Data from this study were presented at the American College of Medical Toxicology (ACMT) Annual Scientific Meeting in March, 2020 in New York, NY. 

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Otter, J., Mayer, S. & Tomaszewski, C.A. Swipe Right: a Comparison of Accuracy of Plant Identification Apps for Toxic Plants. J. Med. Toxicol. 17, 42–47 (2021). https://doi.org/10.1007/s13181-020-00803-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13181-020-00803-6

Keywords

  • Toxic plants
  • Smartphone applications
  • Plant identification