Environment-adaptive Acoustic–Optical sensing for fish abundance estimation in complex aquaculture waters
Abstract
Reliable fish abundance estimation is essential for the operation and management of aquaculture systems, but remains challenging under turbidity, bubble interference, target occlusion, and acoustic reverberation. This study develops an Acoustic–Optical fusion sensing framework for robust fish monitoring in complex aquaculture waters. An environment-aware geometric fusion network, termed EAGF-Net, is proposed to integrate optical appearance information with sonar-derived geometric features. The network employs modality-specific feature extraction with an orthogonality constraint, sonar-guided spatial compensation, and adaptive gating to regulate modality contributions according to environmental conditions. Asymmetric cross-attention is further introduced to reduce semantic inconsistency and suppress unreliable acoustic or optical responses. For densely distributed fish, a keypoint-based topological association method uses fish-eye features and geometric constraints to separate overlapping individuals. The resulting observations are incorporated into Kalman-filter-based trajectory management to reduce missed and repeated counts. Fish abundance is then inferred by combining trajectory flux with the effective sensing volume determined by the Acoustic–Optical field of view and survey path. Experiments covering clear water, dense schooling, turbidity, and bubble disturbance show that the proposed fusion network achieves an F-measure of 0.924 and an average counting accuracy of 96.40%. Field-scale tests in eight aquaculture ponds estimate 1,939 fish against a reference count of 1,975, corresponding to a relative error of 1.80%. These results demonstrate the potential of environment-adaptive Acoustic–Optical sensing for non-invasive fish abundance monitoring in aquaculture engineering applications.
Related
- White Feces Syndrome is closely linked to impaired antioxidant capacity, the aquaculture environment, and microbial dysbiosis in Litopenaeus vannamei reared in small-pond culture system
- When n is not enough: Assessing the interplay of spatial sampling error and sample selection in abundance estimation
- Multi-frequency seismo-acoustic measurements of synthetic ice with fluid inclusions: implications for seismic and through-ice acoustic estimation of sea-ice thickness
- Time-Adaptive Change Analysis through Extension of the M3C2 Algorithm using Multi-Modal Laser Scanning Data in a Salt Marsh Environment
- Antibiotic resistance genes in aquaculture: Molecular detection, environmental dynamics, and biological control strategies
BibTeX
@article{wang2026environmentadaptive,
title = {{Environment-adaptive Acoustic–Optical sensing for fish abundance estimation in complex aquaculture waters}},
author = {Yao Wang and Xiangyu Cheng and Yuchen Lu and Qinglong Zhao and Haitao Zhu and Yanyan Wang and Yuxuan Zhang},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-77035-1},
url = {https://www.nature.com/articles/s41598-026-77035-1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Environment-adaptive Acoustic–Optical sensing for fish abundance estimation in complex aquaculture waters
AU - Yao Wang
AU - Xiangyu Cheng
AU - Yuchen Lu
AU - Qinglong Zhao
AU - Haitao Zhu
AU - Yanyan Wang
AU - Yuxuan Zhang
PY - 2026
DA - 2026/10/26
JO - Scientific Reports
DO - 10.1038/s41598-026-77035-1
UR - https://www.nature.com/articles/s41598-026-77035-1
AB - Reliable fish abundance estimation is essential for the operation and management of aquaculture systems, but remains challenging under turbidity, bubble interference, target occlusion, and acoustic reverberation. This study develops an Acoustic–Optical fusion sensing framework for robust fish monitoring in complex aquaculture waters. An environment-aware geometric fusion network, termed EAGF-Net, is proposed to integrate optical appearance information with sonar-derived geometric features. The network employs modality-specific feature extraction with an orthogonality constraint, sonar-guided spatial compensation, and adaptive gating to regulate modality contributions according to environmental conditions. Asymmetric cross-attention is further introduced to reduce semantic inconsistency and suppress unreliable acoustic or optical responses. For densely distributed fish, a keypoint-based topological association method uses fish-eye features and geometric constraints to separate overlapping individuals. The resulting observations are incorporated into Kalman-filter-based trajectory management to reduce missed and repeated counts. Fish abundance is then inferred by combining trajectory flux with the effective sensing volume determined by the Acoustic–Optical field of view and survey path. Experiments covering clear water, dense schooling, turbidity, and bubble disturbance show that the proposed fusion network achieves an F-measure of 0.924 and an average counting accuracy of 96.40%. Field-scale tests in eight aquaculture ponds estimate 1,939 fish against a reference count of 1,975, corresponding to a relative error of 1.80%. These results demonstrate the potential of environment-adaptive Acoustic–Optical sensing for non-invasive fish abundance monitoring in aquaculture engineering applications.
SN - 2045-2322
ER -