Year: 2025
Type: article
Abstract: Assessment of fish feeding intensity is crucial for achieving precise feeding and for enhancing aquaculture efficiency. However, complex backgrounds in real-world aquaculture scenarios—such as water surface reflections, wave disturbances, and the stochastic movement of fish schools—pose significant challenges to the precise extraction of feeding-related features. To address this issue, this study proposes a fish feeding intensity assessment method based on MobileViT-CoordAtt. The method employs a lightweight MobileViT backbone network, integrated with a Coordinate Attention (CoordAtt) mechanism and a multi-scale feature fusion strategy. Specifically, the CoordAtt module enhances the model’s spatial perception by encoding spatial coordinate information, enabling precise capture of the spatial distribution characteristics of fish schools. The multi-scale feature fusion strategy adopts a three-level feature integration approach (input features, local features, and global features) to further strengthen the model’s representational capacity, ensuring robust extraction of key feeding-related features across diverse scales and hierarchical levels. Experimental results demonstrate that the MobileViT-CoordAtt model, trained with transfer learning, achieves an accuracy of 97.18% on the test set, with a compact parameter size of 4.09 MB. These findings indicate that the proposed method can effectively evaluate fish feeding intensity in practical aquaculture environments, providing critical support for formulating dynamic feeding strategies.
Source: Fishes
Authors: Shikun Liu, Xingguo Liu, Haisheng Zou
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Zero hunger, Life below water
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4410826908