###### Abstract  
Dragonfly classification is crucial for biodiversity conservation. Traditional taxonomic approaches require extensive training and experience, limiting their efficiency. Computer vision offers promising solutions for dragonfly taxonomy. In this study, we adapt the face recognition algorithms for the classification of dragonfly species, achieving efficient recognition of categories with extremely small differences between classes. Meanwhile, this method can also reclassify data that were incorrectly labeled. The model is mainly built based on the classic face recognition algorithm (ResNet50+ArcFace), and ResNet50 is used as the comparison algorithm for model performance. Three datasets with different inter-class data distributions were constructed based on two dragonfly image data sources: Data1, Data2 and Data3. Ultimately, our model achieved Top1 accuracy rates of 94.3%, 85.7%, and 90.2% on the three datasets, surpassing ResNet50 by 0.6, 1.5, and 1.6 percentage points, respectively. Under the confidence thresholds of 0.7, 0.8, 0.9, and 0.95, the Top1 accuracy rates on the three datasets were 96.0%, 97.4%, 98.7%, and 99.2%, respectively. In conclusion, our research provides a novel approach for species classification. Furthermore, it can calculate the similarity between classes while predicting categories, thereby offering the potential to provide technical support for biological research on the similarity between species. dragonfly; classification; face recognition; ArcFace; ResNet50 +
Footnote †: journal: Applied Sciences  
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[PERSON] Li,2,3,4,5,6,7,8,9,1  
perturbations makes dragonflies valuable bioindicators for assessing ecosystem health. Investigations into dragonfly systematics not only elucidate evolutionary relationships but also contribute significantly to biodiversity conservation, environmental monitoring, and invasive species management [1]. Conventional taxonomic approaches relying on morphological diagnostics are not only labor-intensive but also prone to subjective interpretation [2]. Recent advances in computer vision and deep learning have revolutionized taxonomic methodologies, enabling automated image-based classification systems [3]. The integration of artificial intelligence into odonate taxonomy now represents a transformative paradigm in entomological research [4]. Modern computer vision architectures, such as AlexNet, ResNet, DenseNet, EfficientNet, Vision Transformer (ViT), Swin Transformer, and T2T-ViT [5; 6; 7; 8; 9; 10; 11], have achieved remarkable performance in object classification tasks. These algorithms have been successfully adapted for taxonomic classification across various animal and plant taxa [12; 13; 14; 15; 16]. [PERSON] et al. (2022) constructed a comprehensive dataset comprising 256 species and 54,176 images. They employed nine customized CNN architectures, including AlexNet, DenseNet, and ResNet, and trained these models at varying image resolutions (224 x 224 px, 350 x 350 px, 450 x 450 px, and 550 x 550 px). Their comparative analysis revealed that the pretrained DenseNet161 (450 x 450 px) achieved the highest Top1 accuracy of 93.53%, while ResNet152 (224 x 224 px) attained a Top3 accuracy of 98.07%. Additionally, DenseNet201 (550 x 550 px) yielded an F1-score of 86.17%. These results demonstrate the reliability and efficiency of their approach in dragonfly identification [17]. [PERSON] et al. (2021) proposed a hybrid species identification framework that integrates deep convolutional neural networks (VG-GNet16/19 and ResNet18/152 architectures) with geospatial distribution records. For 204 Odonata species, their framework achieved a Top1 accuracy of 66.8% (compared to 54.6% for the image-only model) and a Top3 accuracy of 87.3%. This study underscores the effectiveness of combining computer vision with ecological distribution data for citizen science applications [18]. However, conventional classification models typically generate categorical predictions without accompanying confidence metrics, limiting their interpretability in taxonomic applications.