Search Results (458)

High-resolution Normalized Difference Vegetation Index (NDVI) data are essential for monitoring fine-scale coastal environmental dynamics, yet persistent cloud cover, rapid geomorphic change, and strong spatial heterogeneity limit the availability of temporally continuous observations. Existing spatiotemporal fusion approaches can partially address these limitations, but many rely primarily on data-driven feature learning and do not explicitly incorporate geographic information, leading to boundary blurring, structural inconsistency, and sensitivity to background noise in complex coastal environments. This study presents a geographically constrained Transformer-based framework for 2 m NDVI spatiotemporal reconstruction in coastal landscapes named Coastal-Prior-Embedded Global–Local Fusion Transformer (Coastal-GLFT). The approach integrates high-resolution Gaofen-6 panchromatic and multispectral imagery with high-frequency wide-field-view observations and auxiliary geographic datasets describing elevation, coastline proximity, and land use/land cover. Geographic priors were incorporated as explicit spatial constraints, while a spatiotemporal gating mechanism and global–local fusion architecture were used to improve the representation of temporal variation and multi-scale spatial structure. The method was evaluated using a multi-temporal dataset for the Yellow River Delta comprising 49 high-resolution scenes and 137 coarse-resolution scenes acquired between 2020 and 2025. Compared with representative physics-based, convolutional neural network, generative adversarial network, and Transformer-based fusion methods, the proposed approach reduced reconstruction error by approximately 5–72%, increased signal fidelity by approximately 1–12%, and improved structural similarity by approximately 2–52%. Compared with the strongest Transformer-based baseline, SwinSTFM, Coastal-GLFT reduced RMSE from 0.0896 to 0.0855, increased PSNR from 36.19 dB to 37.09 dB, and improved SSIM from 0.8551 to 0.8742. Qualitative analysis further demonstrated improved preservation of boundary structure, spatial continuity, and heterogeneous coastal features, including aquaculture ponds, tidal creeks, and fragmented wetlands. These results indicate that integrating geographic constraints with multi-scale Transformer-based reconstruction can improve the fidelity and structural consistency of high-resolution NDVI reconstruction in complex coastal environments. The framework provides a basis for fine-scale coastal vegetation monitoring and land-cover analysis, while future work should assess transferability across diverse coastal systems and improve computational scalability. Full article

The expansion of tropical aquaculture in the Peruvian Amazon has increased the need for quantitative information on processing performance and morphometric variation in commercially important native fish species. This study evaluated carcass yield, morphometric characteristics, intestinal traits, and edible tissue recovery of Piaractus brachypomus across three commercial weight categories (251–350 g, 351–450 g, and 451–550 g) in the central jungle of Peru. A total of 75 specimens were collected from a semi-intensive earthen-pond aquaculture unit at the Satipo station of the Tropical Animal Science Program, Universidad Nacional del Centro del Perú, located in Río Negro District, Satipo Province, Junín region, and analyzed under a completely randomized design with 25 fish per category. External morphometric measurements, intestinal variables, and processing yield indicators were recorded and analyzed using analysis of variance, regression models, principal component analysis, and Pearson correlation analysis. Most morphometric variables increased significantly with body weight, whereas tail length remained stable among categories. Principal component analysis explained 80.8% of total morphometric variation and showed clear separation among commercial weight groups. Intestinal length and relative intestinal ratios also increased progressively with fish size. Carcass yield exhibited a moderate decrease with increasing body weight, concomitant with a substantial increase in edible tissue recovery in larger fish. Fresh body weight showed a strong positive relationship with edible tissue recovery and emerged as the main predictor in multiple regression models. These results indicate that commercial weight influences body conformation, digestive development, and processing performance in P. brachypomus, providing information that may support harvest optimization and processing management in tropical aquaculture systems. Full article

Integrated rice–fish farming is increasingly promoted as a sustainable aquaculture model, yet its effects on flesh quality and gut microbial assembly in Pelteobagrus fulvidraco remain insufficiently understood. Here, we compared pond-cultured (PP) and rice-field-cultured (PR) individuals using growth measurements, muscle amino acid and fatty acid profiling, untargeted LC-MS/MS metabolomics and 16S rRNA gene sequencing. Body weight and length did not differ significantly between groups, whereas PP fish showed greater body width and height. PR fish contained higher levels of Ser, Val, Ile and total essential amino acids, together with amino acid and energy-related metabolites. In contrast, PP fish showed higher concentrations of saturated fatty acids, monounsaturated fatty acids, n-3 polyunsaturated fatty acids, total fatty acids and lipid-associated metabolites. The gut microbiota also differed markedly between culture systems. PP fish exhibited higher α-diversity and enrichment of Proteobacteria, Firmicutes, Pseudomonas and Ralstonia, whereas PR fish were characterized by Verrucomicrobiota, Fusobacteriota, Akkermansia and Cetobacterium. Predicted functions suggested enhanced carbohydrate and energy metabolism in PR fish and stronger environmental-response-related functions in PP fish. These findings indicate that pond and rice-field culture promote distinct quality-forming pathways in yellow catfish, providing a basis for optimizing rice–yellow catfish co-culture toward quality-oriented production. Full article

Here we report the draft genome sequences of three Enterococcus faecalis strains, LAB1, LAB10, and LAB11, isolated from the pond water of a tilapia (Oreochromis niloticus) aquaculture farm at the University Nangui Abrogoua, Abidjan, Ivory Coast. These strains were previously characterised for their probiotic, plant growth-promoting (PGP), and nitrifying properties. All three strains were assigned to sequence type ST19 by multilocus sequence typing (MLST). The draft genomes of LAB1, LAB10, and LAB11 consist of 34, 35, and 34 contigs, totalling 2.94 Mb each (GC content 37.40%). Prokka annotation predicted 2872, 2873, and 2875 protein-coding sequences (CDS) for LAB1, LAB10, and LAB11, respectively. Genomic screening revealed no vancomycin resistance genes; however, tet(M) and lsa(A) resistance determinants were identified in all three strains, located on a repUS43-type plasmid replicon. Fourteen virulence factor homologs conserved in the E. faecalis reference strain V583 were detected, including Ebp pili, gelatinase (gelE), Fsr quorum-sensing system, and capsule biosynthesis genes, but no cytolysin operon was identified. Genes associated with stress tolerance (katA, sodA), bile salt hydrolysis (cbh), siderophore transport (fepC, fhuD), and ethanolamine nitrogen metabolism (eutB/eutC) were identified in all three genomes. Pan-genome analysis with the E. faecalis reference strain revealed 551 core gene clusters and 279 gene clusters exclusive to the three aquaculture isolates. Despite their high genomic similarity, we report the three genomes as distinct isolates due to observed differences in their expressed phenotypic properties. These sequences provide a genomic resource supporting the development of multifunctional probiotic consortia for integrated aquaponic systems. Full article

Betanodavirus is a causative agent of viral nervous necrosis (VNN) and a major threat to marine and brackish-water aquaculture globally. This virus causes epizootic outbreaks with particularly high morbidity and mortality in larval and juvenile stages and causes significant economic losses in aquaculture. Here, we developed a rapid and highly sensitive single-tube Reverse Transcription–Loop-Mediated Isothermal Amplification (RT-LAMP) assay for the detection of red-spotted grouper nervous necrosis virus (RGNNV) genotype infection in fish tissue samples. Six primers targeting eight conserved regions of the RNA2 coat protein gene of RGNNV were designed with conservation regions across RGNNV genotypes. RT-LAMP assay was completed within 60 min at 65 °C using a single-tube format that combined reverse transcription and isothermal amplification, and results were directly visualized by the addition of SYBR Green I dye, producing a colour change from orange (negative) to green (positive), observable with the naked eye or under UV illumination. The developed RT-LAMP assay was able to detect five copies of RGNNV from infected samples, which was 20-fold more sensitive than conventional reverse transcription-PCR. The assay demonstrated diagnostic sensitivity and specificity in two fish hosts (Asian seabass and cobia) and showed no cross reactivity with other fish viruses such as tilapia lake virus and cyprinid herpesvirus-2. The developed assay is simple, cost-effective, specific, and enables rapid detection of RGNNV in fish tissues. This single-tube RT-LAMP assay can be applied to screening broodstock facilities, fingerlings, aquaculture farms, quarantine facilities, and juveniles before stocking in ponds or cages, helping prevent disease outbreaks and the transmission of RGNNV in aquaculture systems. Full article

Water temperature is a key driver of microbial succession in aquaculture ponds and may indirectly influence fish intestinal homeostasis, nutrient metabolism, and pathogen risk. This study investigated temperature effects (16 °C vs. 30 °C) on microbial communities in both the intestines of octoploid allogynogenetic gibel carp (Carassius gibelio) (Experiment I, with fish) and the rearing water-column (Experiment II, without fish) over a 20-day experimental period under controlled conditions. The results showed that temperature affected water-column microbiota more strongly than fish intestinal microbiota. In the water-column, diversity indices (Shannon, Simpson, Chao1, and Sobs) decreased over time at both 16 °C and 30 °C, while fish intestinal microbiota showed no significant overall change in alpha diversity. Functional prediction suggested that low-temperature (16 °C) was associated with higher relative abundance of pathways related to amino acid metabolism, carbohydrate metabolism, and genetic information processing in the fish intestine. In contrast, the enrichment of predicted potentially pathogenic phenotypes in the water-column occurred earlier at 30 °C than at 16 °C. These findings indicate that, compared with 30 °C, 16 °C was associated with potentially transient changes in microbial community diversity, slower enrichment of predicted potentially pathogenic phenotypes, and higher predicted nutrient-metabolism potential in intestinal microbiota of allogynogenetic gibel carp during the 20-day experimental period. Full article

Aquatic ecosystems are increasingly impacted by anthropogenic pressures, including nutrient over-enrichment, industrial discharge and physical habitat alteration, resulting from industrialization, urbanization, agricultural intensification and population growth. In recent years, microalgae have been extensively studied in engineered and controlled systems for their potential role in aquatic ecosystem restoration, owing to their capacity to assimilate nutrients, sequester contaminants and interact with microbial consortia, alongside valuable biomass generation. While most of the existing studies are based on ex situ systems, such as high-rate algal ponds, wastewater treatment reactors, algal–bacterial granular sludge, constructed wetlands and aquaculture effluent treatment units, these processes provide mechanistic insights relevant to aquatic ecosystem restoration. Hence, this review critically evaluates the emerging role of microalgae in aquatic ecosystem restoration, mainly through two implementation pathways, namely ex situ engineered systems and in situ applications, based on the current state of the art in microalgae-driven processes, with particular emphasis on nutrient uptake pathways and mechanisms. Furthermore, key challenges and future directions associated with the translational potential of microalgae-based approaches, including field-scale validation, ecological performance assessment, operational stability and regulatory integration, essential for real-world aquatic ecosystem restoration, are discussed. Despite the limitations in direct field-scale restoration, microalgae-based strategies are promising and sustainable platforms for aquatic ecosystem rehabilitation, which align with Sustainable Development Goals 6 and 14. Full article

Fine-grained monitoring of aquatic vegetation in crab ponds is essential for regulating water quality, sustaining ecological balance, and optimizing Chinese mitten crab (Eriocheir sinensis) aquaculture. However, owing to the complex water environment, fragmented vegetation morphology, and the absence of dedicated annotated datasets, traditional remote sensing techniques struggle to achieve highly accurate semantic segmentation and classification. In this study, we construct the first unmanned aerial vehicle (UAV) multispectral dataset for crab pond aquatic vegetation, encompassing four species, Alternanthera philoxeroides, Vallisneria natans, Hydrilla verticillata, and Elodea nuttallii, with pixel-level annotations verified by field surveys across typical aquaculture sites in Jiangsu Province, China. Furthermore, we introduce the Multi-scale Frequency–Spatial Collaborative Network (MFSCNet), built upon a MedNeXt backbone and augmented with distributed modules, including Channel Reduction Attention, Spatial Frequency Selection, a spatial–frequency fusion module, and Mobile Graph Convolution that operate cooperatively across the encoder, skip connections, decoder, and output head. This design suppresses complex water-background interference, enhances vegetation texture representation, and preserves the spatial continuity of vegetation patches. Experimental results demonstrate that, with a lightweight parameter size of merely 19.38 M, MFSCNet achieves a remarkable mean Intersection over Union (mIoU) of 0.9044, outperforming various mainstream convolutional neural network (CNN) and Transformer-based architectures. This study not only provides a high-precision remote sensing technical framework for the accurate multi-class identification and quantitative assessment of aquatic vegetation in crab ponds but also establishes reliable data support for refined aquaculture management and aquatic ecological conservation. Full article

Largemouth bass (Micropterus salmoides) is a major aquaculture species in China. Facility-based aquaculture, such as the “168” model, a high-efficiency recirculating system using funnel-shaped ponds, has promoted water conservation and improved aquaculture efficiency through structural innovation. However, fish die sporadically as the stocking density increases with increasing fish growth. To address this issue, three density groups were established, namely, low (2.5 ± 0.5 kg/m³), medium (4.0 ± 0.5 kg/m³), and high (7.5 ± 0.5 kg/m³). Histological examinations, biochemical assays, and transcriptomic and metabolomic analyses of liver tissues were performed, and fish health was comprehensively evaluated. Histopathological analysis revealed that progressive hepatic vacuolization and severe tissue damage occurred as the fish density increased. Biochemical indicators revealed that the immune system and growth underwent compensatory activation at medium density, shifting to immune suppression, growth impairment, and hepatic exhaustion at high density. Integrated omics analysis revealed that under medium-density stress, the urea cycle was impaired; under high-density stress, Ser metabolism in the liver was rerouted, potentially to overcome methyl donor depletion and prevent disorders of polyamine metabolism, accompanied by a gradual transition from compensatory activation to functional exhaustion. These findings improve our understanding of the physiological response mechanisms of fish to high-density stress. This study provides a theoretical basis for optimizing high-density aquaculture technologies such as the “168” model. Full article

Short-term fluctuations in dissolved oxygen are difficult to capture in warm outdoor Nile tilapia (Oreochromis niloticus) ponds using periodic manual measurements, yet they strongly influence fish performance and farm management. This study presents a preliminary field evaluation of a low-cost IoT workflow for dissolved oxygen monitoring and short-horizon forecasting in pond-based tilapia culture. An ESP32-based sensing node continuously measured dissolved oxygen, temperature, and pH, transmitted readings to a cloud backend, and generated short-horizon forecasts from 5 min aggregated windows. During live validation from 1 to 10 April 2026, the 30 min forecast achieved a mean absolute error of 0.783 mg/L and directional accuracy of 60.23%, with only modest improvement over a persistence baseline. The 6 h forecast achieved 1.109 mg/L and 53.82%, respectively, indicating limited predictive value at the extended horizon. An extended 47-day field deployment (May–June 2026) captured four sensor-recorded low-DO events and two documented power outages, causing sensor downtime and providing additional field-deployment evidence. These results demonstrate the engineering feasibility of the integrated workflow, but they do not establish robust operational forecasting validity because the data were collected from one pond, high-frequency records were temporally correlated, and independent reference-meter validation was not available. The study is, therefore, best interpreted as a proof-of-concept field evaluation that identifies practical requirements for future low-cost aquaculture forecasting systems. Full article

Dissolved oxygen (DO) is a critical water quality parameter in aquaculture systems. Low DO events can stress, limit the growth of, or even cause mortality of aquatic life in aquaculture systems and require rapid management decisions. This study presents a process-based approach for short-term DO forecasting that is intended to support rapid deployment and transferability across various aquaculture systems. Future DO is computed using a mass-balance equation driven by daily stream metabolism and reaeration coefficients estimated from the previous 24 h of weather and water observations. These coefficients are combined with the next day’s observed water temperature, atmospheric pressure, photosynthetically active radiation, and salinity to predict DO 24 h ahead under idealized measured-input conditions with a ten-minute resolution. Model performance was evaluated across multiple aquaculture ponds with varying aeration techniques by assessing prediction accuracy of daily DO minimums using a safety-based metric and full-day DO trajectories using root mean square error. The model successfully predicted 91.77% of DO drops below 6 mg/L within 1 mg/L in a consistently aerated artificial pond and achieved high success in a natural watershed system. Performance was reduced in systems with highly variable aeration. Prediction accuracy was the highest in surface locations away from aerators. These results indicate that a minimal-history process-based framework can identify low DO risk under idealized measured-input conditions, particularly in surface locations away from aerators and in systems with constant or natural aeration. Full article

The classification position of substrates forming on the beds of aquaculture ponds remains a poorly resolved issue at the intersection of pedology, limnology, and aquaculture science. We examine how major international and national soil classification systems—the USDA Soil Taxonomy, the World Reference Base for Soil Resources (WRB), the German Bodenkundliche Kartieranleitung, the Australian Soil Classification (ASC), the Russian Soil Classification, and the classification systems of Brazil and China—approach the systematics of subaqueous soils and their aquaculture analogues. A systematic literature search was conducted across the Web of Science, Scopus, and Google Scholar databases covering the period from 1953 to 2025. Our analysis reveals that Soil Taxonomy provides the most developed taxonomic framework through specialized suborders (Wassents and Wassists), while the WRB offers the greatest flexibility via its qualifier system (subaquatic, limnic, and gleyic). The German classification uniquely assigns subaqueous soils to the highest taxonomic level (division) with a substantive typology that is directly applicable to pond substrates. The Australian classification contributes a three-part sulfidic material typology of practical significance for pond management. The Russian and Brazilian systems currently lack formal taxa for subaqueous soils, although recent proposals (e.g., Aquazems) may address this gap. The Chinese paddy soil model offers a conceptual bridge between subaqueous pedology and aquaculture. No existing system adequately addresses the specific anthropogenic impacts of aquaculture management on pond soil formation. Permanently inundated little-disturbed ponds fall within the subaqueous soil concept, whereas intensively managed, frequently drained or dredged ponds are better treated as anthropogenic soils with a subaqueous phase. We recommend the WRB (4th edition, 2022) as the most suitable framework for current classification of aquaculture pond soils while acknowledging that a multi-system approach may ultimately prove most effective. These findings carry particular relevance for countries of the former Soviet Union (CIS), where extensive pond aquaculture is practiced but pond substrates remain outside formal pedological classification. Full article

Spatially explicit water-quality information is critical for precision management in pond aquaculture but point sampling alone cannot capture pond-to-pond heterogeneity in multi-unit farms. This single-date, single-farm study re-evaluated the potential of UAV hyperspectral imagery for water-quality screening in inland aquaculture ponds in Shanghai, China, using site-matched extraction from a 138-band orthomosaic (450–998 nm, Cubert S185) acquired during a single UAV survey on 24 August 2023 and matched with 23 GPS-registered sampling sites. Eight water-quality parameters were analyzed: chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), ammonium (${\mathrm{NH}}_4^{+}$ ), nitrite (${\mathrm{NO}}_2^{-}$), nephelometric turbidity unit (NTU), chlorophyll-a (Chla), and total suspended solids (TSS). Raw single-band correlations were modest ($\left|\mathrm{r}\right|$= 0.236–0.417), but two-band difference spectral indices (DSI), normalized spectral indices (NSI), and ratio spectral indices (RSI) substantially improved sensitivity, with $\left|\mathrm{r}\right|$ reaching 0.558–0.928. Quadratic inversion models were calibrated on the full dataset and assessed using three validation layers: two-fold cross-validation, nested leave-one-pond-out (LOPO) validation with within-fold predictor reselection, and extraction-window sensitivity tests. Bootstrap 95% confidence intervals for calibration (Cal) R² characterize small-sample uncertainty (n = 23). Three parameters satisfied all three defensibility criteria (Cal R² > 0.5, CV R² > 0.2, and LOPO R² > 0.2): ${\mathrm{NH}}_4^{+}$ (Cal R² = 0.836 [0.61, 0.94]; LOPO R² = 0.420), COD (0.607 [0.34, 0.82]; 0.328), and NTU (0.862 [0.77, 0.96]; 0.204). TP, TN, ${\mathrm{NO}}_2^{-}$, TSS, and Chla showed overfit behavior under nested holdout and were demoted to exploratory products. A TreeSHAP analysis confirmed that band-to-band contrast carried more explanatory power than raw reflectance magnitude. Extraction-sensitivity tests further demonstrated that positional uncertainty (±2-pixel offset: ΔCV $R^2$= 0.23–0.41) exceeded averaging-window sensitivity (3 × 3→10 × 10: ΔCV $R^2$ ≤ 0.11), identifying geolocation control as the dominant robustness constraint. This single-date, single-farm reanalysis suggests that UAV hyperspectral imagery may support exploratory pond-scale screening of ${\mathrm{NH}}_4^{+}$, COD, and NTU. However, robust quantitative inversion and broader transferability remain unverified and will require denser sampling, improved geolocation control, pond-edge masking, multi-site observations, and multi-temporal calibration. Full article

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications. 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