Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app16094249

Authors: Longlong Min, Ming Chen

Institutions: Ministry of Agriculture and Rural Affairs, Shanghai Ocean University

Language: English

Topic: Water Quality Monitoring Technologies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Life below water

Open Access status: gold

Estimated APC paid: $2,896