Spatiotemporal Decision-AI for Species-Specific and Non-Lethal Management of Avian Predators in Aquaculture

Avian predation remains a persistent challenge in aquaculture, as conventional deterrents often lose effectiveness due to bird habituation and limited species specificity. Some methods may also unintentionally affect surrounding ecosystems. To address these limitations, this study presents a spatiotemporal Decision-AI approach for species-specific and non-lethal management of avian predators and nuisance birds by integrating visual intelligence with ecological context. The proposed framework integrates a YOLOv8-based detection model with a metadata extraction and spatiotemporal analysis pipeline that records species identity, population count, time, and location. YOLOv8 serves as the visual detection component, while the methodological contribution of the framework lies in transforming these detections into contextual ecological indicators through temporal occurrence analysis, species-specific weighting, and risk-based Decision-AI. This allows the system to transform raw image detections into meaningful ecological indicators that can guide management decisions. The study was evaluated using a dataset of more than 4500 high-resolution images collected from aquaculture sites across multiple U.S. locations. The proposed model achieved an overall classification accuracy of 97.2%, with precision and recall values of 95.8% and 96.4%, respectively. More importantly, the integration of detection results with temporal and spatial metadata enabled the identification of activity patterns, including peak visitation periods, species dominance trends, and location-specific risk levels. These insights can support targeted and non-lethal intervention strategies, such as optimizing deterrent timing and applying species-aware responses, while minimizing disturbance to non-target wildlife. By linking computer vision with ecological decision support, the proposed study moves beyond passive monitoring toward data-informed, adaptive management. This approach provides a scalable and interpretable pathway for improving aquaculture sustainability while maintaining ecological balance.