## 2 Data Process  
The study utilized two primary sources of dragonfly data: iNaturalist dragonfly image data and dragonfly images collected by Dr. [PERSON]'s team at the Kunming Institute of Zoology, Chinese Academy of Sciences. The iNaturalist dataset encompasses over 2000 dragonfly species, demonstrating remarkable species diversity. However, this dataset  
Figure 1: Different species of dragonflies.  
exhibits significant imbalances in image distribution, ranging from nearly 6000 images for the most abundant species to merely 5 images for the rarest. Furthermore, the iNaturalist data contain several quality issues, including misclassifications, images without dragonflies, blurred photographs, and images featuring multiple dragonflies. In contrast, Dr. [PERSON]'s dataset comprises 218 common Chinese dragonfly species with exceptionally high-quality images, each meticulously classified by experts. Nevertheless, this dataset is relatively small in scale, with only 1-10 images available per species.  
In summary, these datasets present the following challenges: (1) class imbalance; (2) incorrect labeling; (3) excessive background pixels relative to dragonfly pixels in many images; (4) images completely lacking dragonflies; (5) blurred images; (6) images containing multiple dragonflies of potentially different species.  
To address these issues, the authors employed an object detection algorithm, which can identify dragonflies in images while simultaneously determining their positions. This approach facilitates the removal of images without dragonflies or with poor quality. Additionally, by cropping detected dragonfly regions, the algorithm helps mitigate problems related to low dragonfly pixel ratios and multiple dragonflies in single images. Specifically, the authors implemented the YOLOv10 object detection algorithm [25]. The methodology comprised randomly selecting 5000 dragonfly images for bounding box annotation. The annotation process was performed using CVAT, with all dragonfly specimens uniformly labeled under the category \"dragonflies\" regardless of species variation. Upon completion of annotation, the labeled dataset was randomly partitioned into training, validation, and test sets at a ratio of 8:1:1. Subsequently, the YOLOv10 architecture was implemented using the official code provided in the original paper, with default data augmentation techniques employed during model training. The results on the test set after training are shown in Table 1.  
The dataset processed by YOLOv10 object detection excluded images without dragonflies, blurred images, and those with minimal dragonfly presence. For images containing small dragonflies, the detected regions were cropped to isolate the dragonflies, as illustrated in Figure 2, thereby removing most irrelevant background. In images with multiple dragonflies, each detected bounding box was cropped and saved as an individual image. Notably, when cropping the predicted bounding boxes generated by YOLOv10, the authors slightly expanded the boxes. Additionally, if the predicted bounding box had unequal height and width, the shorter dimension was adjusted to match the longer one, as the subsequent classification algorithm requires square input images (Figure 2). This cropping strategy effectively reduces background pixels, eliminates most interference for classification, and ensures the dragonfly is centered in the image, thereby enhancing the accuracy of the subsequent classification algorithm [26].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **Images** & **Instances** & **FP** & **FN** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline \hline
**Odonate** & 500 & 542 & 18 & 41 & 0.965 & 0.924 & 0.967 & 0.841 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection results of YOLOv10 on the dragonfly test set.