Accurate assessment of fish feeding behavior enables aquaculture practitioners to optimize feed allocation and enhance production efficiency. While existing studies predominantly rely on surface-view recognition, this perspective suffers from water surface reflections, insufficient fish exposure, and limited camera angles. Underwater recognition effectively mitigates these constraints but faces two primary challenges: complex spatial distribution patterns during feeding (aggregation, dispersal, occlusion) that single-scale feature extraction cannot comprehensively capture, and subtle visual disparities between feeding intensities in underwater environments causing boundary ambiguity and misclassification susceptibility. To address these limitations, this study proposes DF-MobileNetV4, an improved MobileNetV4 incorporating dual-scale adaptive attention (DSA) and Fused ConvBlock. DSA jointly channel and spatial attention across multiple scales, achieving a 2.2% accuracy improvement over the baseline and demonstrating clear superiority over single-scale attention mechanisms. Fused ConvBlock enhances feature representation through dynamic channel expansion, reducing strong-to-medium boundary misclassification by 60% and contributing an additional 2.3% accuracy gain, effectively alleviating underwater turbidity-induced boundary ambiguity. In addition, we proposed RASUFI, a dataset comprising 4,944 underwater feeding intensity images from recirculating aquaculture systems. Comparative experiments against five mainstream models (e.g., ResNet34-CBAM and MobileViT-SENet) demonstrate that DF-MobileNetV4 achieves 95.59% recognition accuracy on RASUFI, representing a 2.7% improvement over baseline MobileNetV4, with only 4.04 M parameters, 1G FLOPs, and 15.4 MB model size. Cross-domain validation further confirms strong generalization capability, achieving 99.09% and 93.75% accuracy on underwater laboratory and surface-view datasets respectively, outperforming all comparison models on both benchmarks. Additionally, structured pruning improves accuracy to 96.49% while reducing model size to 12.5 MB, providing an efficient solution for intelligent feeding optimization in aquaculture applications.
Zhang et al. (Wed,) studied this question.