Automated 3D Kinematic Monitoring for Circadian Activity and Anomaly Detection in Juvenile Fish

Researchers developed a high-throughput 3D behavioral phenotyping framework integrating deep learning object detection with binocular stereo vision for real-time monitoring of juvenile tilapia. The system automates body length estimation and reconstructs 3D swimming trajectories, enabling precise quantification of velocity and acceleration to establish circadian locomotor baselines and detect anomalies. This approach addresses the phenotyping bottleneck in precision aquaculture by providing an early warning system for physiological stress.

arXiv:2606.14749v1 Announce Type: new Abstract: Precision aquaculture faces a "phenotyping bottleneck" in tracking high-resolution behavioral traits, as conventional methods cannot quantify instantaneous three-dimensional 3D physical exertion. To address this, we present a high-throughput 3D behavioral phenotyping framework integrating deep learning object detection with binocular stereo vision for real-time monitoring of juvenile tilapia in high-density environments. The system automates non-contact body length estimation and reconstructs 3D swimming trajectories from absolute spatial coordinates. By eliminating 2D perspective distortions, this approach precisely quantifies 3D velocity and acceleration, marking the first estimation of true physical swimming speeds in free-roaming juveniles. Results show the framework successfully establishes circadian locomotor baselines, serving as an early warning system for physiological stress and providing an objective metric for fish vitality.