Redefining Aquaculture Safety with Artificial Intelligence: Design Innovations, Trends, and Future Perspectives

Abstract

In recent years, safety concerns in aquaculture have become increasingly prominent due to various factors. Concurrently, the emergence of artificial intelligence (AI) has offered novel approaches to addressing these challenges. This paper provides a comprehensive review and synthesis of AI applications in aquaculture safety over the past few decades, while also suggesting future directions. Utilizing bibliometric tools such as Citespace and VOSviewer, we analyzed 513 publications spanning from 1998 to 2025. Our analysis highlighted a growing global research interest in this emerging field. Furthermore, it is forecasted that the integration of remote sensing technology, immune response monitoring, and cross-disciplinary innovations will drive the transformation of aquaculture safety management toward a more intelligent, proactive, and sustainable approach. These advancements are expected to enhance the precision and efficiency of risk assessment and disease prevention in aquaculture systems.

1. Introduction

Aquaculture, as a key sector in modern agriculture, plays a pivotal role in addressing the escalating global demand for protein [1,2,3]. However, the rapid expansion of aquaculture operations, coupled with climate change has introduced significant challenges, including water quality deterioration, disease outbreaks, and resource availability [4,5]. These issues not only hinder the sustainable development of aquaculture but also pose substantial threats to food safety and environmental protection [6,7]. In this context, the rapid development of artificial intelligence (AI) technologies presents innovative solutions to address these critical challenges [8,9]. By employing data-driven approaches, AI has optimized key processes such as water quality monitoring, health management, and environmental monitoring, thereby significantly enhancing farming efficiency and safety [10,11,12]. Despite these advancements, challenges related to data quality, model adaptability, and system complexity persist, necessitating further research and interdisciplinary collaboration. Research focusing on AI-driven solutions for enhancing aquaculture safety has garnered increasing attention from fields such as agricultural science, ecology, and artificial intelligence [13,14,15]. This multidisciplinary domain not only propels the modernization of aquaculture technologies but also provides robust scientific foundations and technical support for ensuring food safety and protecting the environment [16,17]. Ultimately, these efforts infuse new momentum into the sustainable development of the industry [18,19,20].

In recent years, the application of artificial intelligence (AI) in aquaculture safety has made remarkable progress, transitioning from early conceptual exploration to in-depth multidisciplinary practices, thereby driving rapid advancements in the field [21,22,23]. From the late 20th to the early 21st century, Professor Richard Smith’s team at Stanford University pioneered the integration of computer technology with mathematical models in aquaculture [24,25,26]. Their work focused on monitoring aquatic environments and predicting fish behavior, laying a theoretical foundation for subsequent research. In the 2010s, the rise of the Internet of Things (IoT) accelerated the intelligent development of aquaculture [27,28,29]. Professor David K. Jones at MIT introduced a sensor-network-based real-time water quality monitoring system, while Professor Kjell Ødegård of the Norwegian University of Science and Technology developed precise machine learning models for water quality prediction, significantly supporting aquaculture in cold climates worldwide. Since the 2020s, interdisciplinary research has further optimized AI applications in aquaculture [30,31,32]. Dr. Laura Thompson at Cornell University and Dr. Michael Keller at the University of Bonn proposed solutions to address challenges related to data quality and model adaptability [33,34,35]. Meanwhile, Professor James White of the University of Edinburgh designed an intelligent environmental control system for dynamically regulating water parameters [36,37,38]. Dr. Emma Green at the University of Queensland combined deep learning and drone technology to develop a fish health monitoring system [39,40,41]. At the University of California, San Diego, Professor Rahul Mehta introduced a maintenance system for aquaculture cages integrating autonomous underwater robots and AI. Additionally, Professor Hiroshi Tanaka at the University of Tokyo utilized visual deep learning algorithms to accurately identify the health status of various fish species [42,43,44]. These innovative studies cover critical aspects of aquaculture, including water quality monitoring, health management, and resource optimization [45,46,47]. They have not only enhanced farming efficiency and safety but also infused new technological momentum into the sustainable development of aquaculture [48,49,50].

With the rapid advancement of artificial intelligence (AI), its applications in aquaculture safety have been categorized into six major areas (

Table 1. Summary table of artificial intelligence applications in aquaculture safety.

Types	Technical Features	Significance
Intelligent Water Quality Monitoring and Early Warning	Multi-Source Sensor Data Fusion, Water Quality Control Strategies	Reducing Outbreak Diseases, Ensuring Health of Aquatic Organisms
Aquaculture Disease Identification and Diagnosis	High-Resolution Image Acquisition, Early Diagnosis, and Online Monitoring	Reducing Medication Costs and Drug Residues, Reducing Mortality and Losses for Farmers
Smart Feeding and Feed Optimization	Behavior Pattern Analysis, Data-Driven Decision Making	Reducing Water Pollution, Reducing Feed-Associated Costs for Farmers and Improving Animal Welfare
Automated Management of Aquaculture Farms	Cloud Platform and Big Data Analytics, Multi-System Integration	Comprehensive Risk Management
Underwater Robots and Drone Inspections	Defect Detection and Anomaly Identification	Reducing Human Risks, Reducing Labor Costs and Improving Detection Efficiency
Behavior Patterns and Health Assessment Algorithms	Fish Tracking and Individual Identification	Improving Fish Welfare, Data-Driven Optimization

). It is forecasted that, in the near future, research and applications related to AI in this field will experience substantial growth [51,52,53]. This expansion will further enhance the sustainable development of aquaculture, particularly in terms of safety and operational efficiency [54,55].

2. Research Gap

Current research in AI applications for aquaculture primarily focuses on specific technical aspects such as water quality monitoring, early disease warning, and feeding optimization. These studies have proposed various data analysis and management methods. However, there is a notable lack of systematic, interdisciplinary research and comprehensive evaluations that synthesize broader trends and applications in this field. To address these gaps, this paper aims to systematically analyze the current state and emerging hotspots in AI-based aquaculture safety research from a multidisciplinary perspective. This study summarizes the strengths and limitations of existing technologies while exploring potential future research directions and application prospects. By doing so, the paper aspires to provide a theoretical foundation and practical guidance for advancing technological innovation and industrial upgrading in the aquaculture sector.

In the analysis process, we utilized CiteSpace 6.2.6 and VOSviewer 1.6.18 for visualization. CiteSpace specializes in uncovering research frontiers and development trends in scientific fields [56,57,58]. It offers robust capabilities in co-citation analysis, burst detection, and time-series analysis, making it well suited for exploring academic dynamics and identifying critical knowledge nodes [59,60]. Conversely, VOSviewer excels in constructing, visualizing, and analyzing large network datasets, such as co-occurrence, co-authorship, and co-citation relationships [61,62]. Renowned for its intuitive graphical interface, optimized clustering algorithms, and interactive network maps, VOSviewer is ideal for analyzing macro-level research collaborations and thematic structures. The combined use of these tools facilitates a comprehensive analysis of scientific research landscapes, ranging from micro-level details to macro-level patterns [63,64]. This integrated approach enables a holistic understanding of the field’s academic trends, structural relationships, and knowledge dynamics [65].

This study will employ these two primary software tools to address the following research questions (RQs):

Research Question 1: What is the current state of research publications on the role of artificial intelligence in promoting aquaculture safety?

Research Question 2: Which countries are the primary contributors to research on AI-driven aquaculture safety? What are the characteristics and differences in their research contributions?

Research Question 3: Which institutions are key contributors to research on AI-driven aquaculture safety? What notable outcomes have emerged from collaborations among these institutions?

Research Question 4: Who are the leading researchers in the field of AI-driven aquaculture safety? What significant achievements have they made?

Research Question 5: What is the distribution of influential journals in the field of AI-driven aquaculture safety research?

Research Question 6: What emerging trends and hot topics can be identified in research on AI-driven aquaculture safety?

3. Research Methods

This study utilized the Web of Science Core Collection (WOSCC) database as the primary data source due to its high-quality features in the literature classification and comprehensive research coverage. Using a combination of keywords, the search covered publications from 1998 to 2025 related to artificial intelligence and aquaculture safety. A total of 513 relevant documents were identified, including 456 research articles and 57 review papers.

To comprehensively explore research hotspots and development trends in the field of artificial intelligence (AI) applied to aquaculture safety, this study utilized high-frequency keywords from related domains, including AI (e.g., “deep learning”, “machine learning”), aquaculture (e.g., “aquaculture”, “fish farming”), and safety (e.g., “safety”, “risk prevention”). A search formula was constructed (see

Table 2. Search keywords.

TS	Search Terms
Artificial intelligence	(“artificial intelligence” OR “AI” OR “machine intelligence” OR “intelligent systems” OR “computational intelligence” OR “smart systems” OR “intelligent computing” OR “autonomous intelligence” OR “intelligent automation” OR “automated reasoning” OR “intelligent algorithms” OR “AI-driven solutions” OR “machine learning” OR “ML” OR “deep learning” OR “neural networks” OR “cognitive computing” OR “AI technologies” OR “robotic intelligence” OR “intelligent decision-making systems” OR “AI-powered analytics” OR “natural language processing” OR “NLP” OR “computer vision” OR “intelligent agents”)
Aquaculture	(“aquaculture” OR “fish farming” OR “aquatic farming” OR “mariculture” OR “aquatic cultivation” OR “seafood farming” OR “marine aquaculture” OR “inland aquaculture” OR “freshwater aquaculture” OR “brackish water aquaculture”)
Safety	(“safety” OR “security” OR “protection” OR “reliability” OR “stability” OR “safeguard” OR “defense” OR “assurance” OR “well-being” OR “risk prevention” OR “hazard control” OR “accident prevention” OR “safe condition” OR “safety measures” OR “precaution” OR “safe environment”)

) to ensure broad yet relevant coverage of the literature. During the initial screening, irrelevant documents were excluded based on predefined criteria. This was followed by a thorough review of titles, abstracts, and keywords to identify highly relevant articles and ensure data quality. For analysis, this study employed CiteSpace and VOSviewer, leveraging their complementary functionalities in scientometric analysis. CiteSpace was used to uncover research frontiers and developmental trends, while VOSviewer identified collaboration networks and keyword co-occurrence relationships. By integrating these tools, the study constructed a systematic knowledge map that spans from micro-level research hotspots to macro-level collaboration patterns, providing a comprehensive framework for understanding the research landscape and key issues of AI in aquaculture safety. (Figure 1)

4. Results

4.1. Academic Publication Output and Trends

Figure 2 illustrates the annual trend in the number of published papers on artificial intelligence (AI) in aquaculture safety from 1994 to 2024, reflecting the evolution and development of research in this field. The analysis presented in this figure addresses Research Question 1 (RQ-1): What is the current state of research publications on the role of AI in promoting aquaculture safety?

The evolution of research on artificial intelligence (AI) in aquaculture safety can be divided into three distinct phases. From 1994 to 2010, research was in its infancy, with minimal publications annually, reflecting low academic interest and preliminary exploration. Between 2011 and 2018, the field entered a gradual growth phase, characterized by steady increases in publications, particularly after 2015, indicating rising recognition of AI’s potential in aquaculture safety. From 2019 to 2024, the field experienced explosive growth, with publication numbers surging to 63 in 2023 and 129 in 2024, signifying an accelerated focus and rapid advancements in this area.

It is observed that since 2020, there has been an exponential increase in the number of academic papers focusing on the application of artificial intelligence (AI) in aquaculture safety. This surge can be attributed to several driving factors. Firstly, from a policy perspective, numerous countries and regions have introduced supportive policies aimed at fostering smart agriculture and sustainable development. For instance, the European Union’s “Green Deal” has significantly encouraged research and application of AI technologies in aquaculture. Secondly, technological advancements have played a pivotal role. The rapid development of deep learning, the Internet of Things (IoT), and edge computing has facilitated real-time water quality monitoring, fish health assessment, and environmental optimization. These innovations have significantly enhanced the feasibility and practicality of research in this domain.

A regression analysis of data from 1994 to 2024 yielded a quadratic exponential growth model (y = 0.2847x² − 1142.4x + 1000000), with a coefficient of determination (R²) of 0.8512, indicating a strong fit to the observed data. The analysis reveals that after a prolonged period of accumulation, the field has recently entered a phase of rapid development. This trend highlights the significant research potential and broad application prospects of artificial intelligence (AI) technologies in aquaculture safety. Driven by continuous technological advancements and deeper interdisciplinary research, research outcomes in this field are anticipated to continue their upward trajectory. These developments are expected to catalyze the intelligent and sustainable transformation of the aquaculture industry.

4.2. National Analysis

A national collaboration map provides a visual representation of inter-country cooperation and social relationships within a specific field, offering a unique perspective on academic strengths and scientific research. This tool helps identify countries or regions with significant interest in scientific advancements. By analyzing 513 articles contributed by countries worldwide, we address Research Question RQ-2: Which countries are the primary contributors to AI-driven aquaculture safety, and what are the characteristics and differences in their research contributions?

Based on the analysis of the national collaboration network map (Figure 3), the national collaboration chord diagram (Figure 4), and the summary in

Table 3. Number of papers published per country.

Count	Country	Documents	Total Link Strength	Citations	Citation Per Publication
1	China	216	37	2895	13
2	USA	47	30	744	16
3	Spain	30	22	721	24
4	Japan	14	14	489	35
5	Malaysia	21	11	375	18
6	Egypt	15	10	367	24
7	The United Kingdom	18	22	347	19
8	Portugal	14	8	284	20
9	Australia	10	5	275	28
10	South Korea	19	8	223	12

, China occupies a central position in this field, publishing the highest number of academic papers (216). Its collaboration network extends globally, demonstrating particularly strong ties with countries such as the United States, the United Kingdom, Australia, and Saudi Arabia. Additionally, other nations, including the United States (47 papers), Spain (30 papers), and Brazil (22 papers), also play significant roles in regional and international collaborations. The network map highlights China not only as a key node in bilateral partnerships but also as a pivotal player in promoting internationalization through extensive multilateral collaborations. The chord diagram further reveals the intricate interactions between countries. For example, it shows China’s strong connections with European countries, such as the United Kingdom and Spain, as well as the United States’ bridging role in facilitating collaborations between South American and Asian countries.

Further analysis reveals that the United States, China, and Spain have made significant contributions to research on artificial intelligence (AI)-driven aquaculture. The application of artificial intelligence (AI) in aquaculture safety in the United States, China, and Spain each exhibits distinct characteristics, contributing to a multi-layered intelligent system. In the United States, AI applications leverage the Internet of Things (IoT) and machine learning for water quality monitoring, intelligent feeding, health management, and automated operation and maintenance, thereby enhancing production efficiency and sustainability. In China, the integration of edge computing and large-scale models optimizes dynamic water quality control, intelligent feeding, and disease prediction, while cross-domain data fusion facilitates the development of an intelligent decision-making system. In Spain, the emphasis is on the convergence of biosensing, remote sensing, and robotics technology, utilizing autonomous algorithms to monitor water body health and satellite remote sensing to optimize the allocation of aquaculture resources. The advancements in AI technology in these countries are steering aquaculture towards precision, data-driven, and ecologically sustainable practices.

4.3. High-Contribution Institutions

Analyzing the collaboration among academic institutions across different countries and regions helps identify those that have made significant contributions to the field of artificial intelligence-driven aquaculture safety. This approach directly addresses Research Question RQ-3: Which institutions are the major contributors to research on AI-driven aquaculture safety? What notable outcomes have emerged from their collaborations?

From the institutional collaboration network diagram (Figure 5), it is evident that the primary collaboration hubs in this field are centered around the Chinese Academy of Sciences (CAS) and China Agricultural University (CAU). Domestic partner institutions include Huazhong Agricultural University, Dalian Ocean University, and Guangdong Ocean University. In terms of international collaboration, key partners consist of University Putra Malaysia, National Taiwan Ocean University, the French Ifremer Institute, Macquarie University in Australia, and Mississippi State University in the United States. These collaborative networks span multiple prominent research institutions across Asia, Europe, and the Americas, forming a cross-regional, multi-centered collaboration network.

China Agricultural University and University Putra Malaysia have collaborated to develop an intelligent monitoring model specifically tailored for aquaculture systems in Southeast Asia. By leveraging machine learning technologies, this model enables real-time water quality monitoring and enhances farming efficiency, offering a replicable smart solution for tropical aquaculture.

The collaboration between the Chinese Academy of Sciences (CAS) and the French Ifremer Institute in the field of artificial intelligence (AI) for aquaculture safety primarily focuses on intelligent monitoring, automated data analysis, and decision-making optimization. The two institutions have jointly developed a fish behavior recognition and health monitoring system leveraging computer vision and time-series deep learning models (such as CNN-LSTM). This system enables individual identification, swimming pattern tracking, and abnormal behavior detection, thereby enhancing disease early warning capabilities. Furthermore, by integrating multimodal sensor data fusion technology and employing ST-DNN to optimize water quality prediction models, they have combined data from underwater cameras, acoustic sensors, and water quality sensors to improve ecological environment monitoring and dynamic regulation. Additionally, an AI-driven intelligent aquaculture management system utilizes reinforcement learning to optimize oxygenation, feeding, and pollution control strategies, thereby enhancing the safety and sustainability of aquaculture operations.

From

Table 4. Number of papers published per Institution.

Sequence	Organization	Documents	Citations	Citation Per Publication
1	Chinese Acad Sci	22	231	11
2	China Agr Univ	17	404	24
3	Chinese Acad Fishery Sci	11	136	12
4	Huazhong Agr Univ	7	38	5
5	Beijing Engn & Technol Res Ctr Internet Things Ag	5	100	20
6	Dalian Ocean Univ	5	36	7
7	Guangdong Ocean Univ	5	20	4
8	Henan Normal Univ	5	97	19
9	Jiangsu Univ	5	109	22
10	Jimei Univ	5	67	13

, it is evident that the Chinese Academy of Sciences (CAS) and China Agricultural University (CAU) have published a substantial number of studies, which have also garnered significant citations. In recent years, both institutions have made remarkable progress in applying artificial intelligence to enhance aquaculture safety. Professor Li Daoliang’s team from the College of Information and Electrical Engineering at China Agricultural University, in collaboration with China Unicom and other organizations, developed China’s first large-scale fisheries model, “Fan Li Model 1.0”. This model integrates multimodal data from 27 major aquaculture species, enabling diverse prediction, analysis, and decision-making functions in fisheries. It provides precise AI-driven support for aqua culturists and managers. Additionally, a research team from the CAS Institute of Oceanology addressed key challenges in China’s aquaculture industry, such as outdated technical equipment and low levels of automation and intelligent control. They independently developed several innovative intelligent systems, including high-resolution underwater cameras and 4G-enabled multiparameter online water quality monitoring devices. These systems facilitate intelligent monitoring of aquaculture environments and animals, as well as automated control of equipment. These technologies have been successfully demonstrated in industrial shrimp farming and hatchery facilities, achieving the desired outcomes. These advancements highlight the significant role of AI and innovative technologies in enhancing aquaculture safety and efficiency while addressing critical industry challenges.

4.4. Impact Analysis of Journals

This study addresses Research Question RQ-5: What is the distribution of influential journals in the field of artificial intelligence-driven aquaculture safety?

The journal co-citation network (Figure 6) and journal publication density map (Figure 7) reveal key research hotspots and primary dissemination channels for artificial intelligence in aquaculture safety. The findings indicate that core journals such as Aquaculture, Fish & Shellfish Immunology, and Applied and Environmental Microbiology play pivotal roles in this field, serving as critical nodes for knowledge dissemination and academic exchange. High-impact multidisciplinary journals, including Nature and Science, have significantly contributed to the interdisciplinary dissemination of related research, further enhancing the influence of AI technologies in aquaculture. Additionally, cross-disciplinary journals such as Sensors and Computers and Electronics in Agriculture highlight the expansion of AI research and applications into areas like sensor development and agricultural engineering.

Table 5. Table of journal publications.

Rank	Source	Documents	Citations	IF	Quartile in Category
1	Aquaculture	45	1068	3.9	Q1
2	Fish & Shellfish Immunology	34	777	4.0	Q1
3	Aquaculture Research	14	273	1.9	Q2
4	Animals	13	67	2.7	Q1
5	Remote Sensing	11	136	4.2	Q2
6	Computers and Electronics in Agriculture	10	395	7.7	Q1
7	Aquaculture Reports	8	24	3.2	Q1
8	Journal of Fish Diseases	8	62	2.2	Q1
9	Aquaculture International	7	43	2.2	Q1
10	Aquacultural Engineering	5	163	3.6	Q1

highlights that Aquaculture and Fish & Shellfish Immunology are the two leading journals in the field of artificial intelligence-driven aquaculture safety, with 45 publications (1068 citations) and 34 publications (777 citations), respectively.

The journal “Aquaculture” highlights the diverse applications of artificial intelligence (AI) technology in aquaculture, encompassing areas such as computer vision, machine learning, and predictive modeling. These AI-driven tools demonstrate significant potential in disease monitoring, environmental management, and production optimization. However, research also indicates that challenges—such as limited data access, high costs, technical complexity, and social acceptance—have constrained the widespread adoption of AI technology in the aquaculture sector.

4.5. Author and Co-Cited Author Analysis

As a multidisciplinary field, research on artificial intelligence in aquaculture safety has garnered significant attention from scholars. This study employs VOSviewer and CiteSpace to analyze the author collaboration network within this domain. By examining these collaborations, we aim to address Research Question RQ-4: Who are the leading researchers in the field of AI-driven aquaculture safety, and what are their noteworthy contributions?

Based on publication volume, as shown in the scholar publication statistics (

Table 6. Table of authors published literature.

Count	Author	Documents	Citations	Total Link Strength
1	Wei, Yaoguang	9	246	14
2	An, Dong	7	151	13
3	Pan, Luqing	6	112	11
4	Karim, Murni	5	85	1
5	Li, Daoliang	4	166	1
6	Li, Yun	4	40	4
7	Pai, Radhika m.	4	9	6
8	Almeida, Adelaide	3	171	5
9	Calado, Ricardo	3	134	4
10	Gao, Yan	3	56	2

), Wei Yaoguang and An Dong are identified as core researchers in the field of AI-driven aquaculture safety. Their research contributions and collaborative networks have played a pivotal role in advancing this domain.

Wei Yaoguang’s collaboration network focuses primarily on close partnerships between domestic and international scholars, including Chinese experts such as Li Yun and Li Daoliang, as well as several international researchers. Their collaborative efforts are centered on dynamic monitoring of aquaculture ecosystems and disease prevention. By leveraging artificial intelligence (AI) and Internet of Things (IoT) technologies, they have enhanced the controllability of aquaculture environments. Notable achievements include the development of machine learning-based water quality prediction models and early detection and intervention technologies for fish and shrimp diseases, laying a technological foundation for intelligent and efficient aquaculture management. An Dong’s collaboration network includes influential domestic and international scholars in AI and aquaculture, such as Karim Murni and Pan Luqing. Their work emphasizes the integrated development of intelligent aquaculture systems. Specifically, they have jointly researched deep learning-based multiparameter sensor networks that enable real-time monitoring of feed efficiency, fish behavior, and water quality changes, leading to optimized aquaculture efficiency. Additionally, they have made significant progress in regional adaptation of aquaculture technologies, such as developing smart management solutions tailored to the climatic and farming conditions of Southeast Asia.

From the perspective of scholar co-cited literature, Figure 8 highlights the core research hotspots represented by key publications. Foundational contributions are evident in works such as Cui BE (2019) [66] and Li DL (2020) [67], focusing on water quality prediction, disease monitoring, and ecosystem optimization. More recent publications, such as Ahmed MS (2022) [68] and Duan YQ (2020) [69], reflect the latest advancements in multimodal data fusion and the design of intelligent aquaculture systems. These studies underscore the evolving focus of the field and its progression towards integrating innovative technologies to enhance aquaculture safety and efficiency.

Cui Bing’en (Cui BE) and his team made a significant contribution in their 2019 research paper, “Extracting Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure”. They proposed an enhanced U-Net model, referred to as UPS-Net, designed to accurately identify raft aquaculture areas in remote sensing images. The model was optimized to address challenges such as blurred boundaries and the “adhesion phenomenon”, where adjacent aquaculture areas appear fused in imagery. By incorporating the Pyramid Squeeze-Excitation (PSE) structure, the model effectively integrates boundary and contextual information, significantly mitigating adhesion issues. The research team tested the model in the laver raft aquaculture areas of Lianyungang, China. Experimental results demonstrated that UPS-Net outperformed existing methods, particularly in resolving adhesion problems. This study provides a novel approach and a more efficient solution for applying remote sensing technology to aquaculture monitoring, offering valuable insights for the field.

The most-cited “State of World Fisheries and Aquaculture 2022” report underscores the diverse applications of artificial intelligence (AI) technology in aquaculture, particularly in optimizing feed management, fish health monitoring, and water quality management. AI-driven systems analyze data collected from sensors and cameras to optimize feeding schedules and quantities, thereby reducing waste and minimizing water pollution. Additionally, AI technology facilitates real-time monitoring of fish health, enabling early detection of disease symptoms and prediction of disease outbreaks, which guides farmers in implementing preventive measures. In terms of water quality management, AI systems continuously monitor water quality parameters, analyze data patterns to predict potential issues, and automatically adjust conditions to maintain an optimal aquaculture environment. (

Table 7. Author-Document co-citation network table.

Rank	Co-Citation Count	Year	Authors and Articles
1	18	2022	FAO, 2022, THE STATE OF WORLD FISHERIES AND AQUACULTURE 2022. TOWARDS BLUE TRANSFORMATION, V0, PP266, DOI 10.4060/CA9229EN
2	8	2019	Cui BE, 2019, REMOTE SENS-BASEL, V11, P0, DOI 10.3390/rs11172053 [66]
3	8	2020	Liu YM, 2020, INT J APPL EARTH OBS, V91, P0, DOI 10.1016/j.jag.2020.102118 [70]
4	7	2017	Saberioon M, 2017, REV AQUACULT, V9, P369, DOI 10.1111/raq.12143 [71]
5	7	2021	Lu YM, 2021, REMOTE SENS-BASEL, V13, P0, DOI 10.3390/rs13193854 [72]
6	7	2021	Zhao SL, 2021, AQUACULTURE, V540, P0, DOI 10.1016/j.aquaculture.2021.736724 [73]
7	6	2021	Fu YY, 2021, EARTH SYST SCI DATA, V13, P1829, DOI 10.5194/essd-13-1829-2021 [74]
8	6	2020	Cheng B, 2020, INT J REMOTE SENS, V41, P3575, DOI 10.1080/01431161.2019.1706009 [75]
9	6	2019	Adams A, 2019, FISH SHELLFISH IMMUN, V90, P210, DOI 10.1016/j.fsi.2019.04.066 [76]
10	6	2022	Hou TT, 2022, INT J APPL EARTH OBS, V111, P0, DOI 10.1016/j.jag.2022.102846 [77]

)

4.6. Joint Citation Analysis of Reference Literature

Figure 9 presents the co-citation clustering results of the literature on artificial intelligence in aquaculture safety, revealing the main research hotspots and development directions in this field. Cluster #0 “Aquaculture” is the core area, encompassing AI applications in optimizing the aquaculture environment, enhancing production efficiency, and monitoring water quality, thereby providing technical support for precise aquaculture management. Cluster #1 “Marine Infrastructure Inspection” focuses on real-time monitoring of aquaculture facilities using remote sensing and image recognition technologies to enhance system safety. Cluster #2 “Deep Learning” highlights the extensive application of AI core technologies in water quality prediction, disease detection, and fish behavior analysis, significantly improving management efficiency. Cluster #7 “Fish Behavior” emphasizes the use of intelligent monitoring technologies to analyze fish feeding and swimming behaviors, optimizing health management and feed delivery. Cluster #5 “Fluid Utilization” explores the dynamic relationship between water circulation and the aquaculture environment, ensuring water quality stability. Meanwhile, Cluster #4 “AHL Lactonase” examines the role of microbial communities in disease prevention and control, promoting the development of eco-friendly aquaculture technologies. Overall, these research directions indicate that artificial intelligence has become a key driving force in aquaculture safety, advancing the intelligent and sustainable management of the system.

Figure 10 illustrates the temporal distribution and dynamic changes in co-cited literature hotspots in the field of aquaculture safety involving artificial intelligence, reflecting the phased development trends of research focuses. Theme #0 “Aquaculture” has consistently maintained a core position and reached its peak around 2005, indicating that the early integration of basic aquaculture research with AI technology was a central direction. Theme #1 “Marine Infrastructure Inspection” gradually emerged after 2017 and peaked in 2021, suggesting that as aquaculture scaled up, AI-based facility monitoring became an emerging hotspot. Theme #2 “Deep Learning” experienced rapid growth after 2017, reaching its research peak in 2021, highlighting the critical role of AI in water quality monitoring, disease detection, and behavior analysis. Research on specific species aquaculture (such as Theme #3 “Trout Farming”) was particularly prominent around 2013, indicating significant progress in applying AI to optimize single-species management. Theme #7 “Fish Behavior” has rapidly gained prominence since 2021, reflecting the potential of intelligent behavior analysis for health management and feed optimization. Overall, research hotspots have evolved from foundational technologies to intelligent management and system optimization, with AI becoming a crucial driving force for both research and practice in aquaculture safety.

4.7. Keyword Analysis

Keywords form the cornerstone of academic research, encapsulating the core themes and focal areas of a specific field. By analyzing the co-occurrence network of keywords and tracking the evolution of high-frequency terms, researchers can gain a comprehensive understanding of emerging trends and critical research directions within a discipline. This analysis directly addresses Research Question RQ-6, aiming to elucidate the field’s pressing issues and emerging research trajectories.

Figure 11 illustrates the high-frequency keywords and their relative significance in the field of artificial intelligence (AI) and aquaculture safety, highlighting the core research directions in this domain. Among these, “aquaculture” and “deep learning” are among the most prominent keywords, underscoring the pivotal role of AI technologies in advancing intelligent aquaculture management. High-frequency keywords such as “artificial intelligence”, “machine learning”, and “computer vision” indicate that research in this field broadly leverages advanced technologies to optimize farming efficiency and enhance management precision. Keywords like “food safety” and “water quality” highlight the emphasis on ensuring seafood quality and promoting environmental sustainability. Terms such as “immune response” and “Aeromonas hydrophila” further emphasize the importance of disease prevention and biosafety as critical research areas.

Figure 12 uses clustering analysis of high-frequency keywords to reveal research hotspots and trends in aquaculture. With the VOSviewer tool, keywords are grouped into four clusters. Cluster 1 (red) focuses on antimicrobial research, highlighting innovations in combining gene expression with neural networks for drug development. Cluster 2 (green) centers on vaccine development and biotechnology, showcasing efforts to use advanced technologies like nanotechnology to enhance disease prevention. Cluster 3 (blue) explores probiotics and microbial communities, emphasizing their role in promoting ecological health. Cluster 4 (yellow) underscores the importance of food safety and water quality monitoring, with artificial intelligence playing a key role in early disease detection.

Figure 13 presents a timeline-based clustering of keywords related to the application of artificial intelligence in aquaculture safety, clearly delineating the evolution of research hotspots in this field. From 1998 to 2010, research primarily centered on “aquaculture” and “disease resistance”, with an emphasis on traditional farming techniques and disease prevention strategies. Between 2011 and 2017, attention shifted to “antibiotic resistance” and “bacteria”, reflecting increasing concerns about controlling pathogenic microorganisms in aquaculture environments and developing eco-friendly antimicrobial strategies. Since 2018, keywords such as “artificial intelligence”, “deep learning”, and “machine learning” have emerged as central themes, highlighting the widespread application of AI technologies in water quality monitoring, disease detection, and intelligent management. The appearance of terms like “remote sensing” and “immune response” underscores the critical role of intelligent technologies in optimizing the environment and enhancing biological control. Overall, the research focus has evolved from traditional techniques to intelligent management, underscoring the pivotal role of artificial intelligence in enhancing aquaculture efficiency and promoting sustainable development.

4.8. Overall Research Process

Figure 14 presents a knowledge map illustrating research trends and the knowledge framework of artificial intelligence in aquaculture safety. Publication trends indicate that research is primarily concentrated in three core journals: Aquaculture, Fish & Shellfish Immunology, and Aquaculture Research, spanning interdisciplinary fields such as computer science, microbiology, and ecological science. Collaboration networks reveal that China, the United States, and Spain are the major contributors, with key institutions including the Chinese Academy of Sciences, Macquarie University, and Mississippi State University, reflecting a robust model of international cooperation. Co-citation analysis highlights the pivotal role of highly cited studies, such as Cui et al. (2019) [66], Liu et al. (2018) [70], and Saberioon et al. (2020) [71], in areas like remote sensing, disease management, and aquaculture system optimization. Keyword co-occurrence analysis reveals high-frequency themes including “deep learning”, “water quality and food safety”, and “fish behavior”, while emerging hotspots such as “remote sensing”, “immune response”, and “monitoring” underscore the shift towards intelligent management. Overall, this field demonstrates increasing diversity and international collaboration. Artificial intelligence plays a critical role in water quality monitoring, disease control, and intelligent management, providing valuable insights for future research directions.

5. Discussion

5.1. Analysis of Current Challenges

Through the above analysis, it can be concluded that artificial intelligence (AI) has emerged as a powerful driver of sustainable development in the aquaculture sector. AI is progressively shaping a highly interdisciplinary field that spans diverse topics, from optimizing aquaculture systems to disease monitoring and environmental management. This trend has introduced increasing complexity in the scope and applications of AI technologies, while also presenting numerous interdisciplinary research challenges. Based on the analysis of current trending keywords and the corresponding knowledge framework, the field confronts several key challenges that require focused attention from scholars and practitioners. Addressing these challenges is crucial for advancing the sustainable application and development of AI to ensure safety within the aquaculture industry.

(1)

From the analysis of keywords, it is evident that various aspects of artificial intelligence (AI), including deep learning and machine learning, are increasingly being applied to the monitoring and management of aquaculture safety. However, how can machine learning effectively enhance aquaculture safety, and what are the current bottlenecks?

Firstly, the complexity of modeling dynamic environments has emerged as a significant bottleneck. Aquaculture environments exhibit high spatiotemporal variability, characterized by fluctuations in water quality, climate, and pathogen levels. These factors impose stringent requirements on models’ real-time adaptability and predictive accuracy. Secondly, technical barriers in multimodal data integration are particularly pronounced. Aquaculture involves diverse and heterogeneous data sources, including images, sensor readings, ecological metrics, and genomic information. Integrating these data types to generate comprehensive decision-support systems remains an unresolved challenge. Thirdly, the ecological and ethical implications of technology use are becoming pressing concerns. For example, while AI-optimized strategies may enhance aquaculture efficiency in the short term, they could potentially disrupt ecological balance or compromise animal health. The application of intelligent technologies in aquaculture, such as optimized feeding strategies and selective breeding, may have potential adverse effects on ecosystems and aquatic animal health, including water eutrophication, metabolic imbalances, reduced genetic diversity, and ecological disruptions caused by farmed species escaping into natural environments. These dual constraints of ethics and environmental sustainability demand careful consideration during model development and application. Moreover, regional disparities and sustainability requirements limit the widespread adoption of these technologies. Existing methods are often developed based on broad-scale data, making it challenging to adapt global models to specific local resources and climatic conditions, which are essential for meeting the diverse needs of aquaculture systems. This localization gap hinders scalability and the ability to meet the sustainability goals of different regions. These challenges not only highlight the need for new research directions to improve existing technologies but also present valuable opportunities for interdisciplinary innovation in the field.

(2)

Aquaculture safety primarily encompasses concerns related to water quality, farming practices, and subsequent processing and production safety. How can artificial intelligence (AI) drive progress and effectively ensure these safety measures? What are the current challenges and bottlenecks in this process?

Aquaculture safety encompasses three major areas: water quality management, farming process control, and post-harvest processing safety. Artificial intelligence (AI) is driving advancements in these areas through real-time monitoring and prediction, behavior analysis, and quality inspection technologies. In water quality management, AI leverages sensor networks and deep learning to monitor key parameters such as dissolved oxygen, ammonia nitrogen, and pH levels, providing early warnings of anomalies. In farming process control, machine learning and computer vision technologies monitor organism behavior and health, optimize feeding strategies, enhance operational efficiency, and reduce waste resource. In post-harvest processing safety, AI employs image recognition and quality inspection technologies to automatically detect contaminants and microbial risks, ensuring product safety. However, the widespread application of AI in aquaculture safety faces several challenges, including difficulties in data integration, limited model generalizability, high implementation costs, and practical constraints for small and medium-sized enterprises (SMEs). Additionally, ecological and ethical concerns must be addressed. The application of intelligent technologies in aquaculture safety presents ethical concerns, including potential biases in automated disease detection models that may prioritize certain species, the reduced role of human oversight in critical decision-making processes, and the risk of excessive automation leading to job displacement in traditional aquaculture communities. To promote sustainable development, future efforts should focus on strengthening data sharing platforms, enhancing algorithm robustness, reducing technology costs, and promoting localized practices tailored to specific regional needs.

(3)

From the keyword clustering analysis, it is evident that the detection of fish behavior has emerged as a key focus in the application of artificial intelligence for aquaculture safety. Therefore, how can this challenge be effectively addressed?

Monitoring and managing fish behavior has emerged as a critical focus for artificial intelligence (AI) applications in aquaculture safety, necessitating innovative technological approaches to effectively address this challenge. First, intelligent behavior prediction models that integrate few-shot learning and adaptive algorithms can analyze patterns in fish feeding, swimming trajectories, and group interactions using minimal data in real-time. These models dynamically predict deviations in future behaviors, enabling early environmental interventions. Second, personalized behavior analysis leverages deep reinforcement learning to establish specific behavioral baselines for different species or even individual fish. When individuals deviate from these baselines, health monitoring or environmental adjustments are triggered, ensuring that group behaviors do not mask individual anomalies. Additionally, combining virtual reality (VR) simulations with AI allows for the induction of fish behaviors via virtual stimuli. This approach validates fish responses under various conditions, such as changes in water quality, diseases, density, or stress, providing more reliable references for management strategies. Finally, edge computing and distributed intelligent networks facilitate real-time processing of behavioral data at the sensor level and enable multi-node collaboration. This significantly reduces reliance on central servers, offering cost-effective solutions for small- and medium-sized aquaculture operations. This novel multidimensional technological integration not only enhances the accuracy and efficiency of fish behavior analysis but also paves the way for intelligent aquaculture management.

5.2. Analysis of Future Research

It is anticipated that the rapid advancement of artificial intelligence (AI) will significantly enhance aquaculture safety through its increasingly diverse and sophisticated technologies. The following sections outline several key predictions for the future.

(1)

In the future, aquaculture safety management will transcend traditional biological breeding methods. An AI-driven adaptive gene health analysis system, leveraging deep learning and evolutionary computing, will emerge as a core technology for precision breeding. This system integrates high-throughput gene sequencing (NGS) with deep neural networks (DNN) to analyze gene expression patterns in individual fish and populations, predicting their adaptability under various environmental stresses such as water temperature fluctuations, pathogen infections, and dissolved oxygen variations. By optimizing CRISPR-Cas9 or RNA editing strategies through reinforcement learning, AI can autonomously recommend optimal gene modification plans to enhance disease resistance, adaptability, and growth rates while minimizing ecological risks. This technology not only accelerates the development of fish species resilient to environmental stresses but also leverages digital twin technology to construct genetic simulations of aquaculture populations, thereby optimizing long-term ecological stability.

(2)

Aquaculture disease monitoring is transitioning from traditional laboratory analysis to AI-driven, efficient, and real-time detection. The AI-quantum pathogen detection system, which integrates quantum computing and reinforcement learning, will significantly enhance the timeliness and accuracy of pathogen detection in water bodies. This system leverages quantum computing to accelerate gene sequence alignment and employs AI-trained high-dimensional feature analysis models to identify dynamic changes in microbial populations, thereby accurately predicting pathogen outbreak trends. Additionally, the system incorporates nanobiosensors and surface plasmon resonance (SPR) technology to achieve non-invasive, ultra-sensitive pathogen detection in water quality, ensuring the safety of the aquaculture environment. Through this integrated AI-quantum pathogen detection platform, aquaculture managers can receive precise early warnings before disease outbreaks and adjust management strategies in real time to mitigate the risk of disease transmission.

(3)

In the future, large models will play a pivotal role in the safety management of aquaculture, driving the industry towards intelligence, precision, and eco-friendliness. Based on multimodal foundation models (MFM), AI will integrate water quality parameters, meteorological data, fish behavior, and pathogen gene sequences to construct a high-precision environmental perception and dynamic prediction system, enabling real-time optimization and intelligent regulation of water quality. Additionally, the integration of large-scale reinforcement learning (LSRL) with knowledge graphs (KG) will empower autonomous decision-making systems, allowing AI to achieve end-to-end intelligent aquaculture management capabilities. This includes optimizing feed input, stocking density, disease prevention and control, as well as market supply chain forecasting. Furthermore, leveraging federated learning (FL), large models can share optimization strategies across regions, enhancing the overall intelligence level of the industry while ensuring data privacy.

5.3. Case Analysis with Implications for the Future

5.3.1. Modeling of Comprehensive Power Load of Fishery Energy Internet Considering Fishery Meteorology [88]

Based on the research by Xueqian Fu et al. [88] Several key academic insights can be drawn regarding the application of artificial intelligence (AI) in aquaculture. The study constructed a comprehensive power load model that incorporates the impact of fishery meteorology, precisely calculating the power consumption of the fishery energy internet by analyzing factors such as dissolved oxygen dynamics, fish respiration oxygen demand, feeding patterns, and water evaporation replenishment. This indicates that AI can be effectively utilized for modeling and optimizing critical production factors in aquaculture, including oxygen supply prediction, automatic feeding optimization, and dynamic water environment regulation. Additionally, the study employed MATLAB R2022a simulations to quantify energy consumption across various operational stages in fishponds and evaluated power demands under different aquaculture environments. These findings suggest that AI, when integrated with high-precision meteorological data and sensor networks, can achieve intelligent monitoring and management of aquaculture systems, thereby improving energy efficiency, reducing environmental impact, and enhancing the stability and sustainability of aquaculture operations. Going forward, AI can further leverage machine learning and predictive analytics to optimize aquaculture parameters, increase automation levels, and drive the development of the fishery energy internet.

5.3.2. An Environment Control System for Fish Farming Based on Internet of Things [89]

The IoT-based environmental control system developed by D. Dhinakaran et al. (2023) [89] leverages a wireless sensor network to collect real-time data on critical parameters such as water temperature, pH levels, dissolved oxygen, and fish behavior. By employing advanced machine learning algorithms, including random forests, support vector machines, gradient boosting machines, and neural networks, the system analyzes data to provide robust decision support. This system automates the optimization of water quality, disease detection, and feeding strategy adjustments, thereby reducing disease incidence, increasing yield, and minimizing resource waste. It represents a significant advancement in enabling intelligent and secure management of aquaculture farms.

5.3.3. Comparative Analysis

The future development of AI models in aquaculture will focus on multi-variable modeling, adaptive regulation, and universal intelligent optimization. The research by Xueqian Fu et al. emphasizes energy consumption optimization based on environmental variables, while the IoT control system developed by Dhinakaran et al. leverages machine learning to optimize water quality management and feeding strategies. Both studies highlight the need for AI to integrate multiple factors such as environmental conditions, fish behavior, and energy consumption to enhance predictive and optimization capabilities. Combining data-driven machine learning with physical simulations can improve the accuracy of complex system modeling, enabling precise and intelligent management. Additionally, reinforcement learning (RL) and AI agents empower AI systems to perform autonomous regulation, facilitating unmanned intelligent aquaculture. Ultimately, AI will evolve towards large-scale pre-trained models (Foundation Models), integrating multi-modal data and deep learning techniques to build universal intelligent aquaculture systems across diverse environments and species, thereby promoting the industry’s transition towards efficient, safe, and sustainable development.

5.4. Limitations Analysis

Although this article has conducted a systematic analysis of the research hotspots, development trends, and key issues of artificial intelligence in the field of aquaculture safety, there are still certain limitations that need further exploration. Firstly, the limitation of data sources. The analysis data of this article mainly comes from the Web of Science Core Collection. Although this database includes a wide range of high-quality academic publications, it may still omit certain non-English or region-specific studies, as well as relevant initiatives from private companies and startups, thereby limiting a comprehensive understanding of the global research landscape. Although this article combines mainstream visualization tools such as CiteSpace and VOSviewer, which can well reveal research hotspots and knowledge structures, their exploration of dynamic interaction and causal relationships in complex academic networks is still insufficient, and they fail to deeply explore the specific mechanisms of interdisciplinary knowledge integration. Thirdly, the limitation of research content focuses on the subject. This article mainly focuses on the hot technologies of artificial intelligence in aquaculture safety (such as deep learning, remote sensing technology, and immune response) and their application scenarios, while less discussion is given to other potentially related technologies (such as blockchain, edge computing, etc.) and their possible future contributions, thus failing to comprehensively cover the technological ecosystem of this field. Finally, the uncertainty of future predictions. This article makes predictions on research hotspots and future development directions based on existing literature data and analysis results. However, due to the rapid technological iteration and external environmental changes in the field of aquaculture safety, future development may be influenced by multiple factors such as policies, markets, and technological breakthroughs, which may lead to local deviations in the prediction results. In summary, the limitations of this article provide improvement directions for future research. It is suggested that subsequent research further deepen in terms of the diversity of data sources, the application of interdisciplinary tools, the comprehensive exploration of the technological ecosystem, and the construction of dynamic prediction models, in order to more comprehensively and accurately reveal the internal laws and frontier trends of artificial intelligence promoting the development of aquaculture safety.

6. Conclusions

6.1. Article Summary

From the perspective of national and institutional collaboration, the field of artificial intelligence-driven aquaculture safety exhibits a growing trend of global cooperation and integration. Regarding author contributions, authors with high citation counts are not necessarily the most influential; however, journals with high citation counts are generally considered to have greater impact. Moreover, highly cited references encompass a broad spectrum of topics. Therefore, we recommend that scholars consult highly cited references to broaden their research perspectives in the field of AI-driven aquaculture safety and to inspire potential research directions. From the perspective of journals, the interdisciplinary development trend in AI-driven aquaculture safety is becoming increasingly apparent. Keyword analysis reveals that research in this field primarily addresses fundamental issues in contemporary aquaculture safety, including water quality management, disease prevention, and environmental monitoring, as well as challenges related to innovation and sustainable development. In the future, research keywords are anticipated to shift toward more complex and advanced areas, further driving the application of artificial intelligence in aquaculture safety.

In the future, artificial intelligence (AI) will deeply integrate multimodal sensors, deep learning, and adaptive algorithms in the field of aquaculture safety, enabling precise water quality monitoring and dynamic regulation. Non-invasive disease monitoring will leverage hyperspectral imaging and gene sequencing to achieve early detection and prevention. Intelligent feeding systems will optimize behavior analysis and reinforcement learning to enhance feed utilization and reduce environmental pollution. Simultaneously, cloud computing, big data analytics, and automation technologies will drive the development of intelligent decision support systems, facilitating comprehensive risk management across the entire value chain. With the integration of AI with the Internet of Things (IoT) and edge computing, aquaculture safety management will advance towards a more intelligent, efficient, and sustainable development model.

6.2. Analysis of Innovative Features

This study focuses on the application of artificial intelligence (AI) in aquaculture safety, employing tools such as CiteSpace and VOSviewer to conduct a comprehensive bibliometric analysis from multiple perspectives. By examining research hotspots, development trends, and key challenges, the study shows significant innovation. Firstly, it adopts a multi-faceted analytical approach to construct, for the first time, a systematic knowledge map of the aquaculture safety field, addressing the limitations of prior research in capturing the developmental trajectory and trends of the domain. Secondly, by emphasizing the interdisciplinary applications of cutting-edge technologies such as deep learning, remote sensing, and immune response, the study underscores the academic value of AI in transitioning aquaculture safety from traditional management practices to precision and intelligent systems. Moreover, an in-depth analysis of the dynamic evolution of keywords reveals the progression of research hotspots. The study systematically identifies critical challenges in areas such as data quality, model generalization, and multimodal data integration, while proposing strategies to address these challenges and outlining future directions for development. Additionally, it examines the imbalance in international cooperation and its impact on technology dissemination, offering scientific recommendations to enhance transnational collaboration and knowledge sharing. This research makes notable academic contributions in methodology, content, and practical applications, establishing a robust theoretical foundation and providing practical guidance for the sustainable development of AI applications in aquaculture safety.