Pink-Eggs Dataset: A Step Toward Invasive Species Management Using Deep Learning Solutions

Invasive pomacea canaliculata, also known as the golden apple snail or the channeled apple snail, poses a significant risk to freshwater ecosystems worldwide due to its rapid reproduction rate and adaptability to various environments. Controlling the population of this invasive species and mitigating its ecological impact is crucial. This control largely depends on detecting and managing pomacea canaliculata eggs. Deep learning techniques have succeeded in numerous object detection tasks, including identifying invasive species, suggesting potential for their application in this context. However, creating accurate detection models for pomacea canaliculata is complicated as no comprehensive image datasets are available for their eggs. To effectively monitor and control these species, we present a unique image dataset, Pink-Eggs Dataset, designed to assist researchers in creating deep learning models for automated detection and quantification of pomacea canaliculata eggs. The dataset includes 1261 high-resolution images of pomacea canaliculata eggs collected from Shenzhen, China, with each egg instance carefully labeled within bounding boxes. This dataset is envisioned as a tool for researchers utilizing deep learning techniques to study the spread of this harmful species. The dataset is accessible at https://www.kaggle.com/datasets/deeshenzhen/pinkeggs.