DCRAG-UNet: An Enhanced Semantic Segmentation Framework for Automated Biomass Parameter Estimation of Litopenaeus vannamei

To address the inherent inefficiencies and high error rates associated with manual biomass estimation in Litopenaeus vannamei aquaculture, this study presents a highly sophisticated automated framework based on an enhanced U-Net framework. We propose the DCRAG-UNetmodel, which integrates Dilated Convolutions to expand the receptive field, and a Residual-hybrid Attention Gate (RAG) to effectively suppress underwater noise while reinforcing edge features. The framework integrates a morphological skeleton extraction algorithm with sub-pixel geometric correction to achieve precise body length measurements, which are subsequently used for weight prediction via a robust regression model. Experimental results demonstrate that DCRAG-UNet achieves a Dice coefficient of 98.55% and an mIoU of 98.72%. Furthermore, the average relative errors for body length and weight estimation were 1.12% and 6.92%, respectively. These findings validate the model’s reliability and effectiveness for non-invasive biomass monitoring in intelligent aquaculture environments.