Using computer vision to identify limpets from their shells: a case study using four species from the Baja California peninsula

Hollister, Jack D.; Cai, Xiaohao; Horton, Tammy ORCID:; Price, Benjamin W.; Zarzyczny, Karolina M.; Fenberg, Phillip B.. 2023 Using computer vision to identify limpets from their shells: a case study using four species from the Baja California peninsula. Frontiers in Marine Science, 10.

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The shell morphology of limpets can be cryptic and highly variable, within and between species. Therefore, the visual identification of species can be troublesome even for experts. Here, we demonstrate the capability of computer vision models as a new method to assist with identifications. We investigate the ability of computers to distinguish between four species and two genera of limpets from the Baja California peninsula (Mexico) from digital images of shells from both dorsal and ventral orientations. Overall, the models performed marginally better (97.9%) than experts (97.5%) when predicting the same set of images and did so 240x faster. Moreover, we utilised a heatmap system to both verify that models are focussing on the specimens and to view which features on the specimens the models used to distinguish between species and genera. We then enlisted the expertise of limpet ecologists specialised in identification of species from the Baja peninsula to comment on whether the heatmaps are indeed focusing on specific morphological features per species/genus. They confirm that in their opinion, the majority of the heatmaps appear to be highlighting areas and features of morphological importance for distinguishing between groups. Our findings reveal that the cutting-edge technology of computer vision holds tremendous potential in enhancing species identification techniques used by taxonomists and ecologists. Not only does it provide a complementary approach to traditional methods, but it also opens new avenues for exploring the biology and ecology of limpets in greater detail.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 2296-7745
Date made live: 22 Aug 2023 13:11 +0 (UTC)

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