Abstract
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.