0
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Accuracy of plant identification applications to identify plants in suspected poisoning cases referred to the Queensland Poisons Information Centre

ORCID Icon, ORCID Icon, , & ORCID Icon
Article: 2377523 | Received 25 Apr 2024, Accepted 03 Jul 2024, Published online: 22 Jul 2024

References

  • Huynh A, Cairns R, Brown JA, et al. Patterns of poisoning exposure at different ages: the 2015 annual report of the Australian Poisons Information Centres. Med J Aust. 2018;209(2):74–79. doi: 10.5694/mja17.01063.
  • Ly J, Brown JA, Buckley NA, et al. Paediatric poisoning exposures in schools: reports to Australia’s largest poisons centre. Arch Dis Child. 2021;106(5):496–500. doi: 10.1136/archdischild-2020-319919.
  • Enfield B, Brooks DE, Welch S, et al. Human plant exposures reported to a regional (Southwestern) poison control center over 8 years. J Med Toxicol. 2018;14(1):74–78. doi: 10.1007/s13181-017-0643-3.
  • Fuchs J, Rauber-Lüthy C, Kupferschmidt H, et al. Acute plant poisoning: analysis of clinical features and circumstances of exposure. Clin Toxicol. 2011;49(7):671–680. doi: 10.3109/15563650.2011.597034.
  • Ng WY, Hung LY, Lam YH, et al. Poisoning by toxic plants in Hong Kong: a 15-year review. Hong Kong Med J. 2019;25(2):102–112. doi: 10.12809/hkmj187745.
  • Islam T, Knoeckel R, Wylie C, et al. Plant poisonings in Australia: a retrospective series of calls to the Queensland Poisons Information Centre. Clin Toxicol. 2023;61(1):72–76. doi: 10.1080/15563650.2022.2133727.
  • Barré P, Stöver BC, Müller KF, et al. LeafNet: a computer vision system for automatic plant species identification. Ecol Inform. 2017;40:50–56. doi: 10.1016/j.ecoinf.2017.05.005.
  • Bodhwani V, Acharjya DP, Bodhwani U. Deep residual networks for plant identification. Procedia Comput Sci. 2019;152:186–194. doi: 10.1016/j.procs.2019.05.042.
  • Bonnet P, Goëau H, Hang ST, et al. Plant identification: experts vs. machines in the era of deep learning. In: Multimedia tools and applications for environmental & biodiversity informatics. Cham: Springer International Publishing; 2018. p. 131–149.
  • Long K, Townesmith A, Overmiller A, et al. Plant identification applications do not reliably identify toxic and edible plants in the American Midwest. Clin Toxicol. 2023;61(7):524–528. doi: 10.1080/15563650.2023.2237282.
  • Otter J, Mayer S, Tomaszewski CA. Swipe right: a comparison of accuracy of plant identification apps for toxic plants. J Med Toxicol. 2021;17(1):42–47. doi: 10.1007/s13181-020-00803-6.
  • Mahonski S, Furlano E, Chiang W. Validation of a plant identification application using digital images of toxic plants. J Med Toxicol. 2022;18(2):159–162. doi: 10.1007/s13181-022-00877-4.
  • Persson HE, Sjöberg GK, Haines JA, et al. Poisoning Severity Score. Grading of acute poisoning. J Toxicol Clin Toxicol. 1998;36(3):205–213. doi: 10.3109/15563659809028940.
  • Mall PK, Singh PK, Srivastav S, et al. A comprehensive review of deep neural networks for medical image processing: recent developments and future opportunities. Healthc Anal. 2023;4:100216. doi: 10.1016/j.health.2023.100216.
  • Campbell N, Peacock J, Bacon KL. A repeatable scoring system for assessing Smartphone applications ability to identify herbaceous plants. PLoS One. 2023;18(4):e0283386. doi: 10.1371/journal.pone.0283386.