Robust Inference of Fish Behaviour from Underwater Acoustic and Environmental Data

Understanding appetitive fish behaviour patterns represents a critical challenge for developing intelligent aquaculture monitoring and feeding systems. We present a novel robust analytical framework that combines non-invasive monitoring techniques from 3D multibeam sonar data and environmental monitoring (temperature, dissolved oxygen, and current velocity). Our methodology introduces hourly-averaged acoustic backscatter profiles to quantify behavioural metrics, including the maximum biomass depth and the biomass boundary, enabling comprehensive characterisation of vertical fish distribution dynamics. We used k-means clustering to identify distinct high- and low-activity behavioural modes. The analysis demonstrates appetitive vertical patterns, with fish aggregating deeper in the cage during non-feeding times, moving to shallower depths before scheduled feeding, staying shallow in the water column after feeding and showing high-activity behaviour through the whole cage during feeding hours. These behavioural patterns reveal new insights by correlating vertical fish distribution patterns with scheduled feeding times, suggesting measurable appetitive responses using a multibeam sonar. The robustness of those findings was evaluated through seasonal comparisons, environmental perturbation filtering, and consistency analysis across multiple months. This included storm conditions with lower surface temperatures and stronger currents than typical for the area, where fish consistently retreated to maximum depth. This integrated and explainable inference pipeline provides a foundation for future applications in feeding behaviour detection and optimisation. The work advances the development of non-invasive, data-driven tools for more efficient and sustainable aquaculture management, supporting real-time decision support systems that can optimise feeding strategies and improve fish welfare through behaviour-based management while addressing the industry’s challenge of significant feed loss.