The Application of Artificial Intelligence to Support Behavior Recognition by Zebrafish: A Study Based on Deep Learning Models

Zebrafish (Danio rerio) is an ideal model organism for biological research due to its ease of breeding, maintenance, observation, and complete genome sequencing. As a small aquatic organism with a body length of about 3–5 cm, zebrafish mainly exhibits its behavior through swimming in water. Therefore, trajectory tracking is crucial for a deep understanding of zebrafish behavior and physiological states, as well as for revealing its associations with specific diseases. In addition, zebrafish is widely used for drug screening and toxicology testing to explore the underlying neural and physiological mechanisms. Because of the high efficiency, accuracy, structural simplicity, and versatility of YOLO series models in object detection, they have become one of the preferred deep learning models for many researchers and developers. In this study, a model trained using YOLOv7 was proposed to track the movement trajectories of zebrafish and classify their behaviors into three categories: swimming, sinking, and static, through time-series sorting. According to experimental testing, our method exhibits excellent performance in detecting zebrafish movement trajectories. On a test set consisting of one frame per second, the model achieved a 100% accuracy rate and a 100% recall rate, demonstrating its potential in automated trajectory tracking.