Assessing the accuracy of free automated plant identification applications

Hart, Adam G ORCID: 0000-0002-4795-9986, Bosley, Hayley, Hooper, Chloe, Perry, Jessica, Sellors‐Moore, Joel, Moore, Oliver and Goodenough, Anne E ORCID: 0000-0002-7662-6670 (2023) Assessing the accuracy of free automated plant identification applications. People and Nature, 5 (3). pp. 929-937. doi:10.1002/pan3.10460

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Text 12421 Hart, Bosley, Hooper, Perry, Sellors-Moore, Moore, Goodenough (2023) Assessing the accuracy of free automated plant identification applications.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (1MB) | Preview

Abstract

1. Widely available and inexpensive mobile phone applications offer users, whether professional ecologists or interested amateurs, the potential for simple and rapid automated identification of species, without the need to use field guides and identification keys. The increasing accuracy of machine learning is well established, but it is currently unclear if, and under what circumstances, free-to-use mobile phone applications are accurate for identifying plants to species level in real-world field conditions. 2. We test five popular and free identification applications for plants using 857 professionally identified images of 277 species from 204 genera. Across all applications, 85% of images were identified correctly in the top five suggestions, and 69% were correct with the first suggestion. Plant type (woody, forbs, grasses, rushes/sedges, ferns/horsetails) was a significant determinant of identification performance for each application. For some applications, image saliency was also important; exposure and focus were not significant. 3. Applications performed well, with at least one of the three best-performing applications identifying 96% of images correctly as their first suggestion. We conclude that, subject to some caveats, free phone-based plant identification applications are valid and useful tools for those wanting rapid identification and for anyone wanting to engage with the natural world.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Automated identification; Google Lens; iNaturalist Seek; LeafSnap; Plant identification; PlantNet; PlantSnap; Species ID
Related records:	Image library Dataset
Subjects:	Q Science > QK Botany
Divisions:	Schools and Research Institutes > School of Education and Science
Research Priority Areas:	Place, Environment and Community
Depositing User:	Anna Kerr
Date Deposited:	28 Feb 2023 10:53
Last Modified:	05 Jan 2024 14:13
URI:	https://eprints.glos.ac.uk/id/eprint/12421

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