Assessing the biomass on longline setups based on acoustic data hold significant potential for improving the efficiency and accuracy of monitoring and management of aquaculture setups. Traditional assessment methods, such as manual sampling and visual inspections, are not only labor-intensive and time-consuming but are also subject to variability, often leading to under- or overestimations. Acoustic data, particularly multibeam water column (MBWC) data, provide a non-invasive alternative that can significantly enhance biomass estimation. Within this paper, we demonstrate that 2D and 3D visualizations based on MBWC data can effectively display aquaculture longline structures. To facilitate processing of MBWC data, we have developed scripts that allow to filter and cluster the data into individual dropper lines, enabling an estimation of the biomass volume on each dropper line individually. Our approach offers a scalable and cost-effective solution for aquaculture monitoring, reducing the reliance on destructive sampling and improving decision-making capabilities. Future improvements, such as enhanced data density, refined filtering techniques and automated acquisition workflows, will further increase the accuracy and usability of this method. Ultimately, this research provides aquaculture managers with an innovative tool for rapid volume assessments, contributing to the optimization of sustainable aquaculture practices.
Vandorpe et al. (Mon,) studied this question.