Using computer vision to identify limpets from their shells: a case study using four species from the Baja California peninsula
Using computer vision to identify limpets from their shells: a case study using four species from the Baja California peninsula
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.
Baja California, computer vision, convolutional neural network, heatmap, limpets, rocky intertidal, taxonomy
Hollister, Jack Daniel
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Cai, Xiaohao
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Horton, Tammy
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Price, Bemjamin
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Zarzyczny, Karolina Magdalena
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Fenberg, Phillip
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27 July 2023
Hollister, Jack Daniel
6276291d-9921-47d5-935d-008f68d00f2c
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Horton, Tammy
c4b41665-f0bc-4f0f-a7af-b2b9afc02e34
Price, Bemjamin
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Zarzyczny, Karolina Magdalena
f413d318-ce7a-4899-8502-88989b9af01a
Fenberg, Phillip
c73918cd-98cc-41e6-a18c-bf0de4f1ace8
Hollister, Jack Daniel, Cai, Xiaohao, Horton, Tammy, Price, Bemjamin, Zarzyczny, Karolina Magdalena and Fenberg, Phillip
(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, [1167818].
(doi:10.3389/fmars.2023.1167818).
Abstract
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.
Text
Hollister et al. 2023
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Accepted/In Press date: 7 July 2023
Published date: 27 July 2023
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Funding Information:
We would like to thank Moira Maclean and David Paz García for their assistance with fieldwork and specimen collection. We would like to thank the University of Southampton, the National Oceanography centre, and the Natural History Museum for their assistance with additional equipment needs. JDH and KMZ would like to thank NERC and the INSPIRE doctoral training programme for funding portions of this research. PBF acknowledges funding from NERC grant: NE/X011518/1. We would like to thank Sanson Poon from the Natural History Museum for his consultations for appropriate statistical evaluations for this paper.
Funding Information:
We would like to thank Moira Maclean and David Paz García for their assistance with fieldwork and specimen collection. We would like to thank the University of Southampton, the National Oceanography centre, and the Natural History Museum for their assistance with additional equipment needs. JDH and KMZ would like to thank NERC and the INSPIRE doctoral training programme for funding portions of this research. PBF acknowledges funding from NERC grant: NE/X011518/1. We would like to thank Sanson Poon from the Natural History Museum for his consultations for appropriate statistical evaluations for this paper.
Publisher Copyright:
Copyright © 2023 Hollister, Cai, Horton, Price, Zarzyczny and Fenberg.
Keywords:
Baja California, computer vision, convolutional neural network, heatmap, limpets, rocky intertidal, taxonomy
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Local EPrints ID: 482713
URI: http://eprints.soton.ac.uk/id/eprint/482713
ISSN: 2296-7745
PURE UUID: e1e4b2b0-8d0c-4358-b000-652739ef2446
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Date deposited: 11 Oct 2023 16:56
Last modified: 18 Mar 2024 03:56
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Author:
Jack Daniel Hollister
Author:
Xiaohao Cai
Author:
Tammy Horton
Author:
Bemjamin Price
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