Cost-Effective Fish Volume Estimation in Aquaculture Using Infrared Imaging and Multi-Modal Deep Learning

Accurate fish volume estimation is essential for sustainable aquaculture management, yet traditional methods are invasive and costly, while existing non-invasive approaches rely on expensive multi-sensor setups. This study proposes a cost-effective infrared (IR)-only pipeline that reconstructs depth and Red Green Blue (RGB) from low-cost infrared videos (3 and coefficient of determination (R²) of 0.961 for volume estimation, outperforming state-of-the-art methods by 19-41% while reducing hardware costs by 80%. This work advances precision aquaculture by providing robust, deployable tools for feed optimization and health monitoring, promoting environmental sustainability amid rising global seafood demand.