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Identification of hybrids between the Japanese giant salamander (Andrias japonicus) and Chinese giant salamander (Andrias davidianus) using deep learning and smartphone images
  • Kosuke Takaya,
  • Takeshi Ise,
  • Yuki Taguchi
Kosuke Takaya
Kyoto University Graduate School of Agriculture Faculty of Agriculture

Corresponding Author:[email protected]

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Takeshi Ise
Kyoto University Field Science Education and Research Center
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Yuki Taguchi
Asa Zoological Park
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Biological invasions are recognized as one of the factors causing biodiversity loss. Incomplete reproductive isolation with a closely related species can result in hybridization when a non-native species is introduced into a new habitat. Management of hybrids is essential for biodiversity conservation; however, the distinction between the two species becomes a challenge in cases of hybrids with similar characteristics to native species. Although image recognition technology can be a powerful tool for identifying hybrids, studies have yet to utilize deep learning approaches. Hence, this study aimed to identify hybrids between native Japanese giant salamanders (Andrias japonicus) and non-native Chinese giant salamanders (Andrias davidianus) using EfficientNet and smartphone images. We used smartphone images of 11 native individuals (with 5 training and 6 test images) and 20 hybrid individuals (with 5 training and 15 test images). In our experimental environment, an AI model constructed with efficientNet-V2 showed 100% accuracy in identifying hybrids. In addition, highlighting the regions that influenced the AI model’s predictions using Grad-CAM revealed that salamander head spots are responsible for correctly classifying native and hybrid species. The results of this study revealed that our approach is one of the methods that enable the identification of hybrids, which was previously considered difficult without identification by the experts. Furthermore, since this study achieved high-performance identification using smartphone images, it is expected to be applied to a wide range of low-cost identification using citizen science.
29 Apr 2023Submitted to Ecology and Evolution
02 May 2023Submission Checks Completed
02 May 2023Assigned to Editor
10 May 2023Reviewer(s) Assigned
01 Jun 2023Review(s) Completed, Editorial Evaluation Pending
07 Jun 2023Editorial Decision: Revise Minor
12 Sep 20231st Revision Received
13 Sep 2023Submission Checks Completed
13 Sep 2023Assigned to Editor
13 Sep 2023Review(s) Completed, Editorial Evaluation Pending
20 Oct 2023Editorial Decision: Accept