AquaFishNet: A Binocular Vision-Based Method for Fish Body Mass Estimation

Accurate monitoring of fish body length and mass is essential for evaluating growth status, optimizing feeding strategies, and supporting intelligent aquaculture management. However, conventional manual measurements are labor-intensive and may induce stress or injury due to repeated fish handling. To address these limitations, this study developed AquaFishNet, a binocular vision-based framework for non-contact underwater body length and mass estimation of Leiocassis longirostris. Underwater images were collected in a real recirculating aquaculture environment using a calibrated binocular camera system. AquaFishNet integrates lightweight fish body segmentation, stereo vision-based length estimation, and deep regression-based mass prediction. Experimental results showed that body length estimation errors were mostly within approximately ±2 cm, with relative errors generally below 8%. For body mass prediction, most relative errors were within approximately ±7%, and the model achieved an R2 of 0.9851, RMSE of 18.38 g, and MAE of 12.92 g. These findings demonstrate that AquaFishNet provides an effective non-contact solution for fish growth monitoring and biomass estimation in precision aquaculture.