Aquacultural Engineering最新文献

{"title":"A review of generative AI in aquaculture: Applications, case studies and challenges for smart and sustainable farming","authors":"Waseem Akram,&nbsp;Muhayy Ud Din,&nbsp;Lyes Saad Saoud,&nbsp;Irfan Hussain","doi":"10.1016/j.aquaeng.2025.102637","DOIUrl":"10.1016/j.aquaeng.2025.102637","url":null,"abstract":"<div><div>Generative Artificial Intelligence (GAI) is revolutionizing aquaculture by providing practical and scalable solutions to longstanding industry challenges, including limited data availability, labor-intensive underwater inspections, disease outbreaks, and inefficiencies in resource management. As the sector evolves toward the Aquaculture 4.0 vision of intelligent, interconnected, and sustainable systems, GAI offers transformative capabilities across perception, planning, optimization, and communication. GAI enhances automation, decision support, and situational awareness across the aquaculture value chain through the intelligent synthesis of multimodal data ranging from sensor logs and underwater imagery to textual records and simulations. This review presents the first comprehensive synthesis of GAI in aquaculture, covering foundational models (e.g., diffusion models, transformers, and GANs), domain-specific applications, and emerging deployment scenarios. We demonstrate how GAI drives industry innovation in areas such as ROV-based infrastructure inspection, digital twins for farm design, synthetic data generation for fish health diagnostics, multimodal sensor fusion, and personalized advisory systems. Importantly, we map GAI models to specific aquaculture tasks, highlighting their suitability and advantages. We also offer a critical assessment of their operational readiness, including trust, performance, and environmental impact issues. In addition, we provide a systematic classification of applications, case studies, and future directions to guide the responsible and scalable integration of GAI in aquaculture. This review highlights GAI as a powerful tool and a foundational enabler of innovative, resilient, and ecologically aligned aquaculture systems, accelerating the industry’s transition toward more efficient, transparent, and adaptive practices.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102637"},"PeriodicalIF":4.3,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposed vessel operations in aquaculture: Experimental validation of numerical models","authors":"Robert Indergård,&nbsp;Simen B. Lysthaug,&nbsp;Eivind Lona,&nbsp;Henning Braaten","doi":"10.1016/j.aquaeng.2025.102617","DOIUrl":"10.1016/j.aquaeng.2025.102617","url":null,"abstract":"<div><div>As more of the aquaculture industry is expected to move production towards exposed waters, accurate numerical models are essential for ensuring safe and efficient operations at sea.</div><div>In this paper, a numerical model of a vessel moored to a fish cage was developed and validated using model test experiments. The tests were conducted in irregular waves, wind, and current, with the vessel positioned in both head sea and beam sea conditions. The same environmental conditions were applied in the numerical analysis. The vessel motions in all degrees of freedom were considered, along with the forces in anchor lines, vessel mooring lines and bridle lines. The numerical model was created using SIMA.</div><div>Various floater ring bending stiffness values were tested in the numerical simulations. The system showed minimal sensitivity to these variations. The numerical model generally over-predicted peak loads and motions due to lack of friction between the vessel and the floating ring. However, it accurately recreated the vessel response and line force response, with mean values and phases matching the model test results. Hence, SIMA can predict motions and forces in typical exposed vessel operation in aquaculture.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102617"},"PeriodicalIF":4.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145217588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dissolved oxygen in aquaculture ponds: Causal factors, predictive modeling, and intelligent monitoring","authors":"Minghao Yu ,&nbsp;Yingqi Feng ,&nbsp;Bing Ouyang ,&nbsp;Paul S. Wills ,&nbsp;Yufei Tang","doi":"10.1016/j.aquaeng.2025.102634","DOIUrl":"10.1016/j.aquaeng.2025.102634","url":null,"abstract":"<div><div>In pond aquaculture, maintaining stable Dissolved Oxygen (DO) concentrations is essential for preventing hypoxia, optimizing growth conditions, and ensuring sustainable operations. Therefore, DO prediction is a crucial aspect of intelligent aquaculture systems, directly influencing water quality, aquatic health, and overall productivity. Advances in sensor technology and data-driven modeling have significantly enhanced the ability to monitor and forecast DO levels, enabling proactive management strategies. This review presents a novel taxonomy for classifying DO prediction approaches in pond aquaculture, structured into three key areas: (1) Driven Factors of DO, examining environmental, biological, and operational influences on DO dynamics; (2) Predictive Models, methods ranging from statistical approaches to advanced deep learning, highlighting promising techniques such as physics-informed neural networks (PINNs) and transfer learning for data-scarce environments; and (3) Monitoring and Sensor Technologies, covering electrochemical and optical sensors, particularly fluorescence-based systems, integrated with Internet of Things (IoT) platforms for real-time assessment. By synthesizing these domains, the review identifies opportunities to enhance DO prediction accuracy and monitoring reliability, supporting intelligent aeration control, improved resource efficiency, and more resilient aquaculture operations.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102634"},"PeriodicalIF":4.3,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biodegradation mechanism and removal routes of 17β-estradiol in aquaculture wastewater: A review","authors":"Changwei Li,&nbsp;Limin Dai","doi":"10.1016/j.aquaeng.2025.102636","DOIUrl":"10.1016/j.aquaeng.2025.102636","url":null,"abstract":"<div><div>Intensive aquaculture production usually induces the accumulation of 17<em>β</em>-estradiol (E2), which causes endocrine disorders and feminization of fish, posing a huge threat to the quality and safety of aquatic products. Biological treatment plays a critical role in eliminating E2. For the purpose of this review, a thorough search of the literature was conducted to collect information on the various E2 removal routes (i.e. physical adsorption, biodegradation, and other routes) during biological treatment of aquaculture wastewater. The review discusses the vital role of degradation with respect to the functioning microorganisms, molecular mechanisms and degradation pathways. The contribution of different routes to E2 removal involved in biological approaches is presented. Finally, future research directions on bridging knowledge gaps for enhancing E2 elimination are proposed.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102636"},"PeriodicalIF":4.3,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving feed intake predictions in aquaculture: Integrating dissolved oxygen, body weight, and temperature for Atlantic salmon and meagre","authors":"Marina Linhares Azevedo ,&nbsp;Tomé Silva ,&nbsp;Filipe Soares ,&nbsp;Sergey Budaev ,&nbsp;Luis E.C. Conceição ,&nbsp;Nina Liland ,&nbsp;Pedro Pousão-Ferreira ,&nbsp;Ivar Rønnestad","doi":"10.1016/j.aquaeng.2025.102635","DOIUrl":"10.1016/j.aquaeng.2025.102635","url":null,"abstract":"<div><div>Mathematical models serve as essential tools to optimize feeding strategies, enhance feed efficiency and promote more sustainable aquaculture production. While previous studies primarily aimed to develop feed intake (FI) models based on body weight and temperature, the effect of dissolved oxygen (DO) — a critical environmental factor influencing FI—has often been overlooked. This work aimed to develop FI models for Atlantic salmon and meagre incorporating three main factors (i) body weight, (ii) temperature and (iii) dissolved oxygen. Oxygen-independent models were developed using data sourced from the literature, while Oxygen-dependent models utilized experimental trial data. The Akaike Information Criterion was used to assess and compare the relative quality of the Oxygen-dependent models. To evaluate the models’ performance, an initial analysis was conducted by plotting the model predictions with DO levels from 0 to 10 mg/L. In addition, an independent dataset for each species was used to evaluate the developed models' performance, and model accuracy for each FI model was assessed via the mean absolute percentage error (MAPE). Results show that DO levels influence FI and growth in both species. For Atlantic salmon, Oxygen-dependent models significantly improved FI predictions, whereas for meagre, the Oxygen-independent model provided the most accurate FI predictions. These findings emphasize the importance of incorporating DO into FI models for species where it plays a significant role, while further research is needed to clarify the putative role of DO in meagre FI.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102635"},"PeriodicalIF":4.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable aquaculture: An Iot-integrated system for real-time water quality monitoring featuring advanced do and ammonia sensors","authors":"Abrar Zuhaer ,&nbsp;Azad Khandoker ,&nbsp;Nafees Enayet ,&nbsp;Pronab Kumar Paul Partha ,&nbsp;Md. Abdul Awal","doi":"10.1016/j.aquaeng.2025.102620","DOIUrl":"10.1016/j.aquaeng.2025.102620","url":null,"abstract":"<div><div>Efficient water quality monitoring is essential for sustainable aquaculture. Key parameters such as dissolved oxygen (DO), ammonia, turbidity, pH, temperature, and total dissolved solids (TDS) are vital for mitigating fish loss and enhancing profitability. However, existing commercial solutions fail to provide a cost-effective, automated system for simultaneously measuring all six parameters. To address this limitation, a novel system integrates Internet of Things (IoT) sensors with real-time data presentation to continuously monitor water quality. Designed using a modified V-model methodology, the system provides real-time updates through an Android mobile application. The sensors demonstrated high accuracy: 90.5 % for DO, 88.9 % for ammonia, 96.4 % for turbidity, 94.8 % for pH, 99.1 % for temperature, and 94.9 % for TDS. Given the high cost of typical DO and ammonia sensors, in-house alternatives were developed, reducing expenses. By combining these with commercially available sensors, a six-sensor product package was created, cutting overall costs by approximately 85 % for end users. This significant cost reduction, coupled with real-time monitoring capabilities, democratizes access to advanced technology, promoting the widespread adoption of efficient and sustainable aquaculture practices.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102620"},"PeriodicalIF":4.3,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UVA-LED365 nm and UVC-LED275 nm combined irradiation effectively inactivates Vibrio parahaemolyticus and controls photoreactivation by regulating illumination","authors":"Wenyu Zhao ,&nbsp;Weixiao Zhang ,&nbsp;Zhujun Qiu ,&nbsp;Yuqing Wang ,&nbsp;Chonghui Fan ,&nbsp;Yao Zhang ,&nbsp;Mingyuan Liu ,&nbsp;Zhongliang Gao ,&nbsp;Zi Liang ,&nbsp;Ziyi Sun ,&nbsp;Qian Zhang","doi":"10.1016/j.aquaeng.2025.102623","DOIUrl":"10.1016/j.aquaeng.2025.102623","url":null,"abstract":"<div><div>Ultraviolet disinfection is a common disinfection method for controlling large-scale disease outbreaks in aquaculture. This study investigated <em>Vibrio parahaemolyticus</em>, a common pathogen in aquaculture, to elucidate the differences in inactivation efficacy between UVA-LED (365 nm) and UVC-LED (275 nm) irradiation, both individually and in sequence. Additionally, it examined the effects and underlying mechanisms of various light conditions on the photoreactivation of <em>V. parahaemolyticus</em> following ultraviolet disinfection. The results demonstrated that UVC-LED achieved superior inactivation efficacy against <em>V. parahaemolyticus</em>, with a 3.9 log reduction at 12 mJ/cm². However, UVA pretreatment did not enhance the subsequent UVC-LED inactivation. Additionally, the bacterial growth rate of photoreactivation reaching 106.96 % under a white light at the intensity of 110 μmol/m²/s, which was significantly higher than that at 50 and 170 μmol/m²/s. Compared to other wavelengths, short wavelengths resulted in a relatively higher bacterial growth rate, with blue light achieving a rate of 143.48 % and significant expression of photolyase genes. In contrast, red and green light significantly inhibited the photoreactivation effect. Correlation analysis demonstrated that the bacterial growth rate was positively correlated with recovery time and light intensity during photoreactivation, while it was negatively correlated with spectral wavelength. This study clarified differences in inactivation efficiency between UVA-LED and UVC-LED, as well as the factors affecting photoreactivation of <em>V. parahaemolyticus</em>. To mitigate photoreactivation, increased light intensity and the avoidance of short-wavelength irradiation during the post-treatment phase provided valuable insights for UV-LED water disinfection systems.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102623"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility study of Field Asymmetric Ion Mobility Spectrometry (FAIMS) for rapid off-flavor detection in recirculating aquaculture system cultured Atlantic salmon","authors":"Rakesh Ranjan ,&nbsp;Gajanan S. Kothawade ,&nbsp;John Davidson ,&nbsp;Lav R. Khot","doi":"10.1016/j.aquaeng.2025.102622","DOIUrl":"10.1016/j.aquaeng.2025.102622","url":null,"abstract":"<div><div>This study evaluates the feasibility of a portable Field Asymmetric Ion Mobility Spectrometry (FAIMS) system for detecting geosmin-related off-flavors in Atlantic salmon (<em>Salmo salar</em>) fillets from fish cultured in a recirculating aquaculture system. Salmon were exposed to a concentrated geosmin solution to spike off-flavor concentration in the fish tissues, followed by purging to create varying geosmin levels. Baseline geosmin and 2-methylisoborneol (MIB) in water and fillet samples were quantified using solid-phase microextraction coupled with gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) fingerprints from geosmin-treated cold and heated fillet samples were analyzed via FAIMS, and the results were compared against the untreated control (UTC). For cold samples, the VOC signatures in FAIMS spectra of the UTC and treatment groups with spiked off-flavor were nearly identical. Heated samples, however, displayed distinct ion current profiles within the compensation voltage range of −2–2 V across the full dispersion field range. Principal component and statistical analysis also demonstrated evident separation between UTC and treatment groups. While FAIMS showed promise in effectively differentiating the VOC fingerprints between UTC and geosmin-treated fish fillets, the system could not consistently distinguish concentration differences within treatment groups, regardless of thermal treatment. Overall, the sampling and pre-processing protocols developed in this study can help fish farmers quickly identify geosmin-contaminated batches during grow-out stages and prior to processing, enabling timely corrective actions. Future studies could focus on training FAIMS for the quantitative analysis of off-flavor compounds, utilizing key biomarkers identified by GC-MS.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102622"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-powered decision support system for mariculture: Real-time fish mortality prediction with random forest","authors":"Ramadhona Saville ,&nbsp;Atsushi Fujiwara ,&nbsp;Katsumori Hatanaka ,&nbsp;Masaaki Wada ,&nbsp;Aris Yaman ,&nbsp;Reny Puspasari ,&nbsp;Hatim Albasri ,&nbsp;Nugroho Dwiyoga","doi":"10.1016/j.aquaeng.2025.102621","DOIUrl":"10.1016/j.aquaeng.2025.102621","url":null,"abstract":"<div><div>Fish mortality is a significant issue in mariculture, affecting productivity and sustainability. Predicting mortality risk in real-time is crucial for improving decision making and operational efficiency in mariculture management. This paper presents the development of a real-time fish mortality risk prediction model, designed as part of a Decision Support System using the Random Forest machine learning algorithm. The innovative aspect of this study lies in the real-time processing of sensor data to deliver daily mortality risk predictions, allowing for immediate adjustments to management practices. This study integrates water quality parameters (seawater temperature, salinity, conductivity, chlorophyll-a, turbidity, and dissolved oxygen) monitored through a sensor network, with daily fish mortality records input by farmers. The Random Forest model predicted fish mortality risk across five levels with an overall accuracy of 78.6 % and precision exceeding 70 % for each level. The model's feature importance analysis highlights seawater temperature, salinity, and turbidity as key predictors of fish mortality risk. This system supports fish farmers and site managers in daily operational decision making, particularly regarding feed and labor management. Future improvements in data collection and continuous model updates are expected to enhance the accuracy and utility of the Decision Support System in mariculture management.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102621"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An experimental study on the internal flow field characteristics in aquaculture vessel tanks under sloshing conditions","authors":"Chunhui Zhao ,&nbsp;Guoqiang Li ,&nbsp;Haixiang Xu ,&nbsp;Yonghe Xie ,&nbsp;Panpan Jia ,&nbsp;Peng Xu","doi":"10.1016/j.aquaeng.2025.102619","DOIUrl":"10.1016/j.aquaeng.2025.102619","url":null,"abstract":"<div><div>An experimental platform was established to investigate the internal flow field of an aquaculture vessel under rolling and heaving motions. The effects of different jet angles (<em>α</em>_<em>jet</em>) and velocities (<em>v</em>_<em>jet</em>), roll angles (<em>θ</em>) and heave amplitude (<em>Z</em>_<em>a</em>), and motion periods (<em>T</em>) were explored. The results show that in the stationary state, the flow field in the aquaculture tank was stable, and the jet conditions affected the flow field characteristics and stability. Under heaving motions, the average velocity (<em>v</em>_<em>avg</em>) exhibited an inverse relationship with <em>Z</em>_<em>a</em>, while the coefficient of variation (COV) demonstrated a non-monotonic trend, with its variation pattern influenced by <em>T</em>. Notably, the heave motion had a negative correlation effect on the <em>v</em>_<em>avg</em> across the entire tank area. With rolling motions, the flow field changes were related to the motion trajectory of the tank. The <em>v</em>_<em>jet</em> had a significant influence on the <em>v</em>_<em>avg</em> which increased as <em>θ</em> and decreased as <em>T</em> increased. Under the combined motion of the two degrees-of-freedom, the <em>v</em>_<em>avg</em> increased with <em>θ</em>. Additionally, an increase in <em>T</em> led to a corresponding enhancement in flow field stability. Under combined heave-roll motion conditions, the <em>v</em>_<em>avg</em> demonstrated a 20–30 % reduction compared to static conditions at identical jet parameters (<em>α</em>_<em>jet</em>, <em>v</em>_<em>jet</em>). These results furnish empirical evidence for refining the hydrodynamic behavior of aquaculture vessels in seaway conditions (e.g., rolling/heaving), thereby enhancing their operational adaptability in fisheries.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102619"},"PeriodicalIF":4.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}