Search Results (246)

Water quality monitoring is a fundamental element of sustainable aquaculture management, as changes in parameters of physicochemical and biological properties directly affect the health, growth performance, and productivity of the aquaculture systems. Although traditional machine learning (ML) methods have demonstrated effectiveness in water quality classification, their performance often depends on large amounts of labeled data, which can be challenging and expensive to collect in real-world aquaculture environments. This study explores a few-shot learning (FSL) framework for data-efficient water quality classification under limited supervision to address this limitation. Several FSL models, including prototypical networks (ProtoNet), Siamese Networks, and Matching Networks were developed and evaluated in a comparative experimental framework against the traditional machine learning classifiers logistic regression, random forest, support vector machine and extreme gradient boosting. Low-data learning scenarios were simulated using a structured episodic evaluation approach. Experimental results demonstrate FSL techniques outperform traditional machine learning methods across all evaluated scenarios. Among the tested methods, ProtoNet achieved the highest performance, attaining an accuracy of 94.46% and an ROC-AUC score of 98.65%, indicating superior discriminative capability and robustness. Siamese Networks also demonstrated competitive performance under highly constrained data conditions. Furthermore, latent-space visualization, confusion matrix analysis, paired t-test statistical analysis, and ablation studies confirmed that episodic meta-learning enables the learning of highly discriminative latent representations with strong generalization capability under limited labeled data conditions. The findings highlight that FSL provides a robust and scalable framework for intelligent water quality classification in aquaculture systems, particularly in scenarios where labeled data are scarce, offering significant potential for sustainable aquaculture monitoring applications. Full article

The use of functional feeds in aquaculture has increased due to the reduction in fishmeal and the need to improve the gut health of farmed organisms. This study evaluated the effects of supplementing nucleotides (NT) and mannan oligosaccharide (MOS) in the diet of juvenile Penaeus vannamei reared in a synbiotic system for 60 days. Four treatments were tested: a control diet without additives (RC); a diet supplemented with 2 g NT kg⁻¹ (NT); a diet supplemented with 2 g MOS kg⁻¹ (MOS); and a diet supplemented with 1 g NT kg⁻¹ and 1 g MOS kg⁻¹ (NT/MOS). Water quality, zootechnical performance, gut morphology, microbiota indicators, and economic benefits were evaluated. There were no significant differences in water quality or production performance among treatments. However, additive supplementation improved economic efficiency and net revenue, with the NT/MOS treatment showing a 7.06% economic gain relative to the control. MOS supplementation increased the proportion of Bacillus spp. to 13.9 × 10⁷ CFU g⁻¹, suggesting a prebiotic effect and potential control of Vibrio spp. Morphologically, supplemented shrimp exhibited shorter mucosal folds and thicker gut walls. NT and MOS supplementation appeared to be an economically promising strategy for shrimp production in synbiotic systems. Full article

Red tide events pose substantial threats to marine ecosystems, aquaculture, and coastal public health. Timely and accurate delineation of red tide extent from satellite imagery is therefore essential for operational monitoring and early warning. However, existing deep learning-based semantic segmentation methods generally treat multispectral bands as homogeneous inputs and do not fully exploit the domain knowledge embodied in spectral indices commonly used in traditional remote sensing analysis. To address this limitation, this study proposes a spectral-index-guided network (SIG-Net) that explicitly incorporates spectral-index priors into deep feature extraction through a dual-branch architecture. SIG-Net comprises three components: a spectral encoder based on a Mix Vision Transformer (MiT-B2) that learns spatial-spectral representations from the original Sentinel-2 bands; a lightweight CNN-based index encoder that extracts discriminative features from four spectral indices, namely the red-green index (RGI), blue-green index (BGI), normalized difference vegetation index (NDVI), and the normalized difference Noctiluca index (NDNI) proposed in this study; and a spectral-index-guided fusion (SIGF) module that adaptively integrates multi-scale features from the two branches using spatial-reduction cross-attention and a gated fusion mechanism. Experiments on a Sentinel-2 red tide dataset show that SIG-Net outperforms single-branch baselines, including U-Net, DeepLabV3+, and SegFormer, as well as naive multi-source fusion strategies. Ablation studies further confirm the contributions of the SIGF module, the gating mechanism, and the proposed NDNI to performance improvements. The proposed method provides an effective framework for integrating domain knowledge with deep learning for red tide remote sensing monitoring. Full article

Precise, fine-scale mapping of aquaculture ponds (APs) is the technical foundation for refined aquaculture management and environmental regulatory compliance. Despite advancements, current remote sensing workflows often struggle to resolve small-scale APs (<1 ha) or delineate boundaries in dense clusters due to low spectral contrast. To address these challenges, we propose a Water Signal Attention-Guided Network (WSAG-Net), a fine-scale and automated approach for AP mapping using PlanetScope imagery. WSAG-Net incorporates weakly supervised water segmentation into the attention learning process, guiding the model to prioritize water regions. A dedicated joint loss function is employed to jointly optimize the auxiliary water segmentation and the main AP extraction, ensuring that water signal prior knowledge is embedded into shared feature representations. This design enhances discriminative semantic learning and improves the robustness of AP extraction. Tested on PlanetScope imagery, WSAG-Net achieved a Frequency-Weighted Intersection over Union (FWIoU) of 91.09% and an Overall Accuracy (OA) of 95.25%, outperforming all baseline models in both boundary delineation and the identification of small, clustered APs (<1 ha). Furthermore, compared to existing public AP datasets, our method substantially reduces the omission of small APs (<1 ha). This study addresses the persistent difficulty of delineating densely clustered small APs, presenting a practical and transferable framework for fine-scale AP inventory and compliance monitoring. Full article

Continuous dynamic monitoring of coastline changes is essential for revealing the evolutionary laws and spatiotemporal characteristics of coastal systems. In this study, we employed AlphaEarth Foundations (AEF) data and Sentinel-2 imagery to investigate coastline and land use changes in the Pearl River Estuary (PRE) region over the period 2017–2023. The Random Forest (RF) algorithm was adopted to extract coastlines and classify coastal land-use types, after which their spatiotemporal evolution was quantitatively analyzed. The results demonstrate that the classification performance of AEF data is significantly better than that of Sentinel-2 imagery, with the average overall accuracy and Kappa coefficient exceeding 92% and 89%, respectively. The PRE coastline shows an evolutionary pattern of “overall contraction accompanied by regional differentiation”: its total length first increased and then decreased, peaking at 1029.05 km in 2019, representing a cumulative net reduction of 7.54 km over the 2017–2023 period. Meanwhile, land use expansion driven by reclamation resulted in a cumulative net increase of 25.26 km². Aquaculture ponds (AP) constitute the dominant type of newly reclaimed land, accounting for more than 50%, while the expansion of impervious surface (IS) accounts for 24.52%. This study provides novel insights and a scientific basis for the refined management of coastlines, sustainable land use planning, and coastal-marine ecological protection in the Pearl River Estuary and similar regions worldwide. Full article

Oxygen transfer enhancement in aquaculture was investigated using a low-cost fine-bubble spray system operated under controlled hydrodynamic conditions. Experiments were conducted under oxygen-depleted conditions (initial DO = 2.4 mg L⁻¹), and oxygen transfer kinetics were evaluated using the dynamic method. The dissolved oxygen concentration increased to 6.2 mg L⁻¹ within 1 h, corresponding to a net oxygen transfer of 9.55 ± 0.46 g. The volumetric mass transfer coefficient (kLa) was determined to be 1.44 h⁻¹ (R² = 0.97), while the specific oxygen transfer efficiency (SOTE) reached 76.4 ± 7.8 gO₂ kWh⁻¹. Dimensionless analysis (Re ≈ 2 × 10⁴, Sc ≈ 500, Sh ≈ 682) indicates a turbulent, convection-dominated transport regime. Biological observations showed a 43% increase in fish growth under spray-assisted conditions, indicating improved oxygen availability. The observed oxygen transfer enhancement was primarily associated with hydrodynamic interfacial area generation rather than diffusion-limited transport. The low-power configuration and simplified system design suggest potential applicability for decentralized aquaculture operations. The proposed approach also provides an engineering framework for evaluating low-cost aeration technologies under aquaculture operating conditions. Full article

Accurate monitoring of fish body length and mass is essential for evaluating growth status, optimizing feeding strategies, and supporting intelligent aquaculture management. However, conventional manual measurements are labor-intensive and may induce stress or injury due to repeated fish handling. To address these limitations, this study developed AquaFishNet, a binocular vision-based framework for non-contact underwater body length and mass estimation of Leiocassis longirostris. Underwater images were collected in a real recirculating aquaculture environment using a calibrated binocular camera system. AquaFishNet integrates lightweight fish body segmentation, stereo vision-based length estimation, and deep regression-based mass prediction. Experimental results showed that body length estimation errors were mostly within approximately ±2 cm, with relative errors generally below 8%. For body mass prediction, most relative errors were within approximately ±7%, and the model achieved an $R^2$ of 0.9851, RMSE of 18.38 g, and MAE of 12.92 g. These findings demonstrate that AquaFishNet provides an effective non-contact solution for fish growth monitoring and biomass estimation in precision aquaculture. Full article

Accurate mapping of offshore aquaculture remains challenging in complex coastal environments due to heterogeneous backgrounds, variable sea states, blurred pond boundaries, adhesion among densely distributed cages, and the weak texture of floating rafts. To address these limitations, this study proposes a bi-temporal multimodal cross-fusion network (BMCF-Net) for fine-scale offshore aquaculture segmentation from Sentinel-1/2 imagery. The framework jointly exploits bi-temporal observations acquired during non-ice and sea-ice periods and integrates them through a bi-temporal fusion module to enhance target–background separability and suppress environmental noise. In addition, a feature correction module and a multi-head feature fusion module are introduced to strengthen cross-modal alignment between SAR structural information and optical spectral–textural cues, thereby improving the separation of dense aquaculture units and the detection of weak-texture targets. Experiments conducted on a multimodal dataset from the Liaoning coastal zone show that BMCF-Net achieves F1-scores of 93.15%, 93.90%, and 89.04% for aquaculture ponds, cages, and floating rafts, respectively, outperforming state-of-the-art segmentation models such as FTransUNet. The proposed model was further applied to produce a high-resolution aquaculture distribution map for Liaoning Province in 2023 and to analyze area dynamics over the past six years. The results demonstrate the potential of BMCF-Net for large-scale offshore aquaculture monitoring and coastal resource management. Full article

Accurate assessment of fish feeding intensity is significant for the timely understanding of feeding demands, dynamically adjusting feeding strategies, and reducing aquaculture costs. However, existing methods often rely on superficial visual features that fail to capture subtle satiety dynamics, resulting in limited reliability. To address the issue, a method for fish feeding intensity assessment based on spatial features and TabNet model with Dynamic Feature Weighting Layer (TabNet-DFWL) is proposed in this study. Fish body contours are extracted from lateral-view images through a pipeline of segmentation, enhancement, and binarization. Subsequently, spatial features highly correlated with fish feeding mechanisms are proposed to characterize behavioral changes. Based on these, an interpretable model integrating spatial features and TabNet-DFWL is constructed to achieve precise fish feeding intensity assessment. This method explores spatial features related to feeding behavior from the underlying mechanism of fish behavioral changes and establishes a feeding intensity assessment model based on TabNet-DFWL. By doing so, it avoids the black-box risk commonly associated with traditional deep learning models and significantly improves model interpretability and reliability, thereby providing a trustworthy basis for precision feeding in aquaculture. Experiments conducted on a real-world fish feeding dataset demonstrate that the proposed method achieves an accuracy of 95.96%, an average precision of 93.44%, an average recall of 93.33%, an average specificity of 98.15%, and an average F1-score of 93.38%. Compared with comparative algorithms, all evaluation metrics exhibit improvements. These results indicate that the proposed method enables accurate assessment of fish feeding intensity and can effectively support the dynamic adjustment of feeding strategies in aquaculture systems. Full article

In aquaculture, fish physiological information serves as the foundation for behavior recognition, precise feeding, and health monitoring. The acquisition of such information relies on accurate keypoint detection and pose estimation of the fish body. To address the challenges caused by inter-occlusion among fish schools and blurred keypoint boundaries in underwater environments, a novel fish pose estimation method based on the Adaptive-kernel Hybrid-center Structural Constraint Network (AHSC-Net) is proposed. Optimized specifically for the characteristics of fish poses, the proposed method effectively enhances detection accuracy and robustness in complex underwater scenarios. First, a Stochastic Local Centroid Sampling (SLCS) strategy is introduced to improve detection capability. By simulating centroid positions in occluded samples, this approach enhances the model’s ability to detect partially occluded fish. Next, a Spatial-Awareness Enhanced Pose Structural Constraint (SAPSC) is established through coordinate embedding and morphological constraints. It ensures the rationality of the predicted poses. Furthermore, an Adaptive Kernel Modulation Module (AKMM) is designed to dynamically adjust the Gaussian kernel distribution, effectively addressing challenges posed by underwater blurring and variations in fish scales. Experimental results demonstrate that AHSC-Net achieves 92.0% AP and 94.6% AR on a self-constructed largemouth bass dataset, outperforming state-of-the-art methods such as HRNet, HigherHRNet, DEKR, and YOLO-Pose. This study presents a fish pose estimation method that provides effective technical support for automated and precise monitoring in aquaculture. Full article

In Luoyuan Bay, China, Sporobolus alterniflorus invasion has hindered mangrove restoration and disrupted faunal communities within mangrove habitats. This study investigated its impact on mollusk, crab, and fish assemblages across mangrove, mudflat, and invaded habitats from 2019 to 2020. Results showed that species diversity of three assemblages did not differ significantly between invaded and non-invaded mangrove habitats; however, assemblage structure was altered and functional traits declined markedly in invaded areas. Compared with non-invaded mangroves, invaded habitats showed decreases of 81.6% in mollusk density, 50.7% in mollusk biomass, 66.6% in crab density and 84.2% in crab biomass. Dominant fish species (Acanthogobius ommaturus, Liza carinata, Stolephorus chinensis) also exhibited lower body size, total size and biomass in invaded habitats. Given the close dependence of coastal residents on these faunal resources, a socioeconomic analysis of livelihood strategies was conducted, revealing Sinonovacula constricta aquaculture achieved the highest net income-to-investment ratio, 122.7% higher than nearshore fishery and 308.3% higher than shallow-sea oyster cultivation, while professional shellfish farming yielded the highest net income per hectare, 23.6% higher than oyster cultivation. Thus, both forms of shellfish aquaculture provide greater economic returns than other livelihood options. Based on these findings and niche theory, we propose a management framework: after removing S. alterniflorus, plant native mangroves (Kandelia obovata) in mid-to-high intertidal zones and lease lower flats for shellfish farming. This framework has the potential to integrate ecological restoration with local livelihoods and may inform similar efforts in other regions facing biological invasions and restoration challenges. Full article

Nearshore mariculture is a major contributor to the supply of “blue food”; however, its rapid expansion in bay systems has intensified sea-space competition and environmental pressures, underscoring the need for accurate and long-term monitoring. This study used multitemporal Sentinel-2 imagery processed using Google Earth Engine (GEE) to develop an automated identification framework for raft and cage aquaculture along the coast of Fujian, China, from 2017 to 2024. Three widely used classifiers—U-Net, DeepLabV3+, and random forest (RF)—were comparatively evaluated. Of these methods, U-Net had the most stable overall performance under optically complex nearshore conditions and was, therefore, used for province-scale mapping. Based on the U-Net-derived maps, the spatiotemporal evolution of mariculture was quantified. The results showed that mariculture in Fujian exhibited a persistent bay-oriented, dual-core clustering pattern, with major hotspots concentrated in Ningde and Zhangzhou. In the 2024 winter–summer comparison, raft aquaculture displayed a clear seasonal contrast, characterized by expansion in winter and contraction in summer, whereas cage aquaculture showed relatively smaller seasonal variation. Interannually, the mariculture system shifted from a mixed cage–raft configuration toward the dominance of raft aquaculture, accompanied by a spatial redistribution of mapped aquaculture density from inner nearshore waters toward bay mouths and more open waters. Overall, in this study, we demonstrate the potential of deep learning-enabled Sentinel-2 remote sensing for monitoring nearshore mariculture structures and provide mode-specific observational evidence for marine spatial planning, environmental risk management, and sustainable mariculture development in nearshore waters and semi-enclosed bay systems. Full article

This paper details the implementation of an integrated engineering framework for the real-time assessment of pose and size in fusiform fish, utilizing laser-camera technology. The design, comprising a camera and laser emitter, leverages laser triangulation for accurately measuring distances between key points, providing a reliable baseline for data comparison. Enhanced with the yolov7 model backbone, it includes detection and segmentation features, enabling precise image instance segmentation of fish and laser lines. The system’s dual-network structure, which combines fully connected regression and DSNT-MobileFaceNet networks, efficiently identifies six crucial landmarks on fish—an essential step for detailed pose analysis. This method facilitates the accurate determination of two-dimensional fish posture by analyzing the relative positions of these landmarks. A notable capability of this system is its ability to infer depth information from laser lines on the fish’s body, aiding in the accurate measurement of dimensions such as body length and depth. Empirical results demonstrate the system’s effectiveness, with high mean Average Precision (mAP) values for both object detection (0.9560 for fish, 0.8550 for laser lines) and segmentation (0.9740 for fish, 0.8420 for laser lines). The DSNT-MobileFaceNet network, in particular, shows excellent fitting accuracy with an $R^2$ value of 0.9170. The deep learning model achieves an average error rate of 7.75% in detecting fish data, markedly improving upon the baseline error rate of 14.70%. Overall, this study confirms the proposed system’s capability in accurately assessing fish pose and size. As a rigorous proof of concept validated in a controlled laboratory environment, this work establishes a foundational framework for non-invasive morphological monitoring, suggesting its future applicability in marine biology and aquaculture. Full article

With the growing demand for intelligent monitoring in land-based aquaculture, rapid and accurate fish counting from visual data has become important for stocking density regulation, feeding management, and production decisions. To address the challenges in above-water fish images, including scale variation, severe occlusion and adhesion, blurred boundaries, and frequent switching between low- and high-density scenes, this study proposes MultiTask-Fish, a shared backbone multitask counting method. The network uses ResNet34 as the backbone and integrates a feature pyramid network and channel attention to learn unified feature representations. It jointly predicts detection heatmaps, foreground masks, separation boundaries, density maps, density gating, and global count regression, allowing the model to combine local localization cues, structural information, and global statistics. Based on existing polygon annotations, heatmap, mask, boundary, and density supervision are automatically generated for integrated multitask training. Experiments on 495 fish images, including 346 training and 149 validation images, showed that the proposed method achieved an MAE of 5.875, an RMSE of 11.839, and an MAPE of 0.152 on the validation set, while reducing the MAE on the high-density subset from 16.717 to 13.895. These results demonstrate its effectiveness for fish counting in complex above-water aquaculture scenes. Full article

Addressing the challenges of extreme sample scarcity, complex underwater optical environments, and significant variations in lesion scales in real-world aquaculture, this paper proposes a small-sample grass carp disease recognition method, namely Swin Transformer with Supervised Contrastive Learning (ST-SCL), integrating dual-stream data augmentation and supervised contrastive learning. First, a frequency-spatial dual-stream augmentation strategy is constructed. In the frequency domain, the Amplitude-Mix technique is introduced to simulate diverse lighting and turbidity styles by mixing background amplitude spectra, thereby enhancing environmental generalization. In the spatial domain, a pathology-mask-guided instance-level Copy-Paste strategy is employed to directionally expand scarce lesion samples and address data imbalance. Second, the Swin Transformer is adopted as the backbone network, leveraging its hierarchical shifted window attention mechanism to effectively capture multi-scale features, balancing the detection of tiny parasites and extensive superficial ulcerations. Finally, supervised contrastive learning is incorporated to maximize intra-class compactness and minimize inter-class separability within the feature space, effectively reducing overfitting inherent to small-sample learning. Experimental results demonstrate that the proposed method achieves a macro-average F1-score of 95.86% across six disease categories. Compared with mainstream models such as ResNet and ConvNeXt, the ST-SCL exhibits notable performance improvements and enhanced robustness in small-sample scenarios, offering a promising technical path for precise fish disease diagnosis in complex aquatic environments. Full article