Artificial intelligence in seafood: enhancing logistics management for a smarter supply chain

1 Introduction

The seafood sector is a significant contributor to the world's food supply chain, providing a major share of daily protein requirements to individuals all over the world (Bene et al., 2015; FAO, 2022). The sector is composed of different components such as culture, capture, processing, and distribution of aquatic flora and fauna, making it one of the major components of world's agriculture and food production (NOAA, 2021). Seafood is consumed in different forms such as fresh, frozen, canned, smoked, and dried, highlighting its significance and share to an abundance of products for the global market (Troell et al., 2014). The seafood sector also plays a crucial role in food security and is a significant economic activity in most coastal communities (FAO, 2022). Being high in protein, omega-3 fatty acids, vitamins, and essential minerals, seafood offers a sustainable and low-cost source of nutrition, supporting global food security (Bene et al., 2015; FAO, 2022; Troell et al., 2014). The seafood sector also generates employment opportunities around the world, ranging from fishermen to plant workers and from distributors to retailers (NOAA, 2021).

Different national and international organizations, including the U.S. Food and Drug Administration (FDA), the European Food Safety Authority (EFSA), and the World Health Organization (WHO), have set standards and guidelines for seafood safety (FAO, 2022; WHO, 2021). These organizations manage acceptable levels of contaminants, including toxic trace elements and pesticides, in seafood to promote consumer health and food quality (Codex Alimentarius Commission, 2020). The Codex Alimentarius, which is an international collection of food safety standards, guidelines, and codes of practice, offers a system for ensuring seafood product safety across borders [Food and Agriculture Organization of the United Nations (FAO) and World Health Organization (WHO), 2020]. These organizations regulate acceptable levels of contaminants including toxic trace elements (e.g., mercury, cadmium, and lead) and pesticide residues such as organochlorines and organophosphates in seafood products. Proper handling of seafood is important as otherwise it could promote proliferation of bacteria resulting in spoilage creating health and economic hazards (EFSA, 2021; WHO, 2021). Preservation of freshness and prevention of contamination is achieved through correct monitoring of parameters like temperature, biochemical spoilage indicators and microbial load across the supply chain (FAO, 2022). Seafood needs to be stored at certain temperatures from the time they are harvested up to the time they are consumed, as any transportation delay or temperature changes may result in safety issues (EFSA, 2021). Logistics management is further complicated by factors like long haul distances for transport, port congestion, and poor refrigeration in certain areas (NOAA, 2021). These are challenges that have to be dealt with by adopting improved technology, stringent regulation, and better supply chain management in order to assure seafood consumer safety [Food and Agriculture Organization of the United Nations (FAO) and World Health Organization (WHO), 2020]. This review organizes Artificial Intelligence (AI) applications across the seafood supply chain under three interrelated thematic concepts such as Traceability, Sustainability, and Food Safety for a more broad spectrum understanding of how emerging AI technologies collectively reinforce these functional outcomes along with the following objectives.

• How AI streamlines cold chain logistics, forecasts optimal transportation routes, and reduces wastage in seafood distribution.
• Exploring the challenges of implementation of AI such as high cost of implementation and data accuracy issues, while also venturing into future developments in AI-based seafood management.
• Role of AI in enhancing transportation efficiency, minimizing spoilage, and optimizing inventory management through AI-enabled temperature fluctuation monitoring.

2 Methodology

Duplicate records were removed, and studies were screened in two stages: (i) title–abstract screening to exclude papers unrelated to seafood or logistics; and (ii) full-text screening to include only peer-reviewed articles that directly addressed AI-driven approaches to seafood supply-chain management or quality assurance. Inclusion criteria followed: peer-reviewed articles, substantial focus on seafood logistics, AI/IoT/blockchain/computer-vision applications, and available performance metrics. Exclusion criteria followed: non-seafood studies, conceptual papers without data, conference abstracts, or patents. A total of 520 records were retrieved across Scopus, Web of Science, and ScienceDirect. After removal of 120 duplicates, 400 records were screened by title and abstract, yielding 160 for full-text review. Of these, 53 were excluded for methodological or data-quality reasons, and 107 peer-reviewed articles were included in the final synthesis. Study quality was assessed by evaluating methodological transparency, dataset availability, model validation procedures, and the presence of quantitative performance metrics such as accuracy, precision, recall, or prediction error. Only studies providing reproducible methodological details and validated results were included in the final synthesis. Figure 1 illustrates the PRISMA-style flow of study identification, screening, eligibility, and inclusion. Data were extracted on: technology type, dataset used, validation metric, and principal outcome.

3 Challenges in seafood logistics management

There are several challenges facing the seafood business, especially in the areas of logistics. Maintaining freshness from catch to customer is essential since seafood is extremely perishable. Figure 2 illustrates the multifaceted challenges involved in managing seafood logistics. The seafood supply chain is particularly vulnerable due to the perishable nature of the product, requiring strict temperature control throughout storage and transportation to prevent spoilage (Waldhans et al., 2024). Any disruption in the cold chain can accelerate bacterial growth, compromising taste, texture, and safety. Additionally, the industry faces issues of fraud and mislabelling, where lower-value species are often substituted for premium ones, misleading consumers and damaging trust (Lawrence et al., 2023). Ensuring sustainability adds another layer of complexity, as illegal fishing, unreported catches, and overfishing remain prevalent. Traditional quality assessments relying on subjective evaluations of texture, color, and odor further complicate the process, leading to inconsistent results (Miguéis et al., 2022). Moreover, emerging threats like microplastics, toxic trace elements, and viral contaminants require advanced detection methods beyond traditional protocols.

An effective cold chain is crucial to the seafood industry's ability to preserve product freshness and to guarantee food safety. AI integration has given an edge to the industry by enhancing real-time management, transparency, predictive maintenance, and fraud prevention in cold chain monitoring. Using real-time temperature and humidity monitoring to ensure cold chain integrity is one of AI's most effective uses in seafood logistics. Throughout storage and transit, smart sensors with AI algorithms could continually monitor important environmental factors like temperature, humidity, and the atmospheric gas composition (Waldhans et al., 2024). These sensors send data to cloud-based systems, where real-time AI model analysis is performed, sending out immediate notifications if any conditions deviate from ideal ones, enabling operators to take action before the quality of the seafood declines. By keeping seafood within permissible temperature ranges, this proactive monitoring minimizes wastage and guarantees adherence to food safety laws (Ismail et al., 2023). The following challenges are crucial in understanding the technological and operational gaps existing in current seafood cold chain.

3.1 Cold chain integrity

Seafood is highly perishable and requires stringent temperature control throughout storage and transportation to maintain its freshness, taste, texture, and safety. Any deviation from the optimal temperature range typically close to 0 °C for fresh seafood and −18 °C for frozen seafood products can trigger bacterial growth (Hoel et al., 2017). Even a brief temperature fluctuation during transport can accelerate spoilage, resulting in off-flavors, mushy textures, and increased food safety risks due to harmful pathogens like Listeria monocytogenes or Vibrio parahaemolyticus (Giarratana et al., 2022). Maintaining a consistent cold chain is essential not only for preserving product quality but also for extending shelf life and reducing waste.

3.2 Fraud and mislabeling

Seafood is among the most frequently mislabeled food products worldwide. Fraudulent practices, such as substituting premium fish species with cheaper alternatives, misrepresenting farmed fish as wild-caught, or falsifying geographical origins to dodge import restrictions, undermine consumer trust and pose significant health risks (Cundy et al., 2023). Mislabeled fish may expose allergic consumers to unintended species or introduce toxins from misrepresented lower-quality products (Tinacci et al., 2023). This deception also distorts market prices and harms authentic fisheries.

3.3 Sustainability and traceability gaps

Illegal, unreported, and unregulated (IUU) fishing is an important and persistent hazard to global marine biodiversity, food security, and sustainable fisheries management. IUU activities contribute toward overfishing, depletion of fish stocks, and destruction of marine ecosystems, undermining conservation efforts and fair market competition. According to the Food and Agriculture Organization (FAO), nearly 34% of global fish stocks are fished at biologically unsustainable levels, with IUU fishing being a major contributor (FAO, 2022). Traditional seafood supply chain systems are un-equipped to address the challenges of traceability and confirmation. In many cases, seafood products pass through multifaceted and non-transparent networks involving multiple intermediaries, where traditional hard copy documentation can be fabricated or lost. This lack of traceability makes it nearly impossible to determine whether seafood is legally sourced, allowing IUU products to enter genuine markets unnoticed (Gephart et al., 2025). As an outcome of this situation, both consumers and regulators are progressively demanding transparent, verifiable systems to guarantee that seafood is sourced legally and sustainably.

3.4 Quality assessment limitations

Traditional seafood quality assessments, relying on texture, color, and odor evaluations, are inherently subjective and time-consuming. These sensory methods often lead to inconsistent results due to variations in human perception, environmental factors, and inspector fatigue (Marques et al., 2022). Moreover, such methods primarily detect visible or perceptible signs of spoilage, often missing early-stage deterioration that affects taste, texture, and safety. Adding to the complexity is the growing concern over contaminants like viruses, microplastics, and heavy metals, which are invisible to the naked eye and undetectable through sensory assessments. For example, heavy metals like mercury can accumulate in seafood, causing long-term health issues, while microplastics infiltrate marine food chains, raising safety concerns (Papanikolaou and Fulgoni, 2021; Hossen et al., 2022). On the other hand, chemical analyses determine seafood freshness and spoilage through the examination of chemical compounds like pH, Total Volatile Basic Nitrogen (TVB-N), and lipid oxidation markers (Zomorodian et al., 2023). Chemical analysis is time-consuming and expensive as it needs elaborate laboratory infrastructure and trained personal (Özogul and Özogul, 2019). Instrumental methods on the other hand involves sophisticated equipment like gas chromatography (GC), high-performance liquid chromatography (HPLC), and electronic gas detectors that identify volatile compounds and spoilage indicators (Lv et al., 2018). Other non-destructive techniques, like near-infrared spectroscopy, enables seafood composition determination without damaging the product (Yu et al., 2024). Though more accurate than chemical or sensory analysis, instrumental techniques also involve expensive instruments and skilled expertise, often making them less convenient for wholesale or high-speed quality assessments in the seafood sector (FAO, 2022). Chemical assays, including determination of pH, TVB-N, and Thiobarbituric Acid Reactive Substances, provide greater accuracy but are time, labor, and equipment intensive (Kim et al., 2024). Microbiological assays for the determination of spoilage bacteria and pathogen presence takes several hours to days to procure the results and are not feasible for application in real-time monitoring (Zambotti et al., 2022). The major limitation is the absence of continuous monitoring throughout the supply chain. As seafood is extremely perishable, even small temperature fluctuations during distribution will promote spoilage, and conventional testing methods does not generate real-time information regarding storage conditions [Food and Agriculture Organization of the United Nations (FAO) and World Health Organization (WHO), 2020]. Addressing these challenges requires a shift toward more advanced, technology-driven solutions that ensure consistent, accurate quality evaluations and safeguard against hidden hazards.

3.5 Logistics disruptions

The seafood supply chain functions on a global scale, making it vulnerable to disruptions caused by weather events, pandemics, or geopolitical tensions. These disruptions can delay transportation, affecting the accessibility and freshness of seafood products. Delays can lead to spoilage, financial losses, and supply shortages, ultimately impacting consumer trust. Due to their dependence on uninterrupted cold chain infrastructure, seafood supply chain is particular vulnerable to logistics disruptions. A more recent example being the Russia–Ukraine conflict that has disrupted trade through Black Sea port closures and container service suspensions resulting in rerouting and creating significant delays (Sun and Ma, 2024). This has resulted in increasing the average European food transit period by more than 40% significantly increasing the cold chain hazards for seafood traders (Gulyi, 2022). The breakdowns in the cold chain system is also equally significant. It has been reported that cod loins in Iceland during their transit often exceeded recommended storage temperatures by up to 5 °C, impacting their shelf life and economic outcome (Martinsdóttir et al., 2010). In many developing and underdeveloped regions, industries continue to rely on non-insulated vehicles to transport iced seafood exposing the commodity to increased spoilage risks (Ndraha et al., 2018).

3.6 Emerging contaminants

The marine ecosystem is notorious for accumulating a wide range of anthropogenic chemicals that ultimately endanger human health by passing through the food web. Traditional contaminants such as dioxins, PCBs, and toxic minerals are generally well-monitored and studied. The growing risk regarding the occurrence of contaminants of emerging concern (CECs) is currently challenging the seafood industry worldwide (Wang et al., 2024). Many of them lack established legal maximum limits even though they pose serious human health risk due to their perseverance, bioaccumulation, and toxicologic effects. Toxic elements like methylmercury have been reported to bioaccumulate in predatory fishes, posing neurotoxic risks to vulnerable groups like children and pregnant women (Ray et al., 2025). Dietary exposure concerns are often raised regarding the levels of cadmium in crustaceans and bivalves sourced from polluted waters. Another group of CECs are the endocrine disruptors such as bisphenol A (BPA), alkylphenols, and perfluoroalkyl substances (PFASs). BPA can leach into seafood through food contact plastic packaging materials while the others are persistent bioaccumulates often linked to immunotoxic and carcinogenic outcomes (Sadrabad et al., 2023). The point of concern is that these compounds are regularly detected in seafood at levels often exceeding environmental quality standards. Brominated flame retardants (BFRs) are another category, especially Polybrominated diphenyl ethers and hexabromocyclododecanes due to their lipophilicity that leads to accumulation in seafood, with some category exhibiting neurotoxic and endocrine-disrupting properties (Enyoh et al., 2024). Likewise, pharmaceuticals and personal care products that are connected with municipal and aquaculture effluents are also posing threat to seafood along with antibiotics, antidepressants, and UV filters raising significant concerns regarding microbial resistance and long-term health effects. Microplastics are another group of CECs gaining recent attention in this regard. Due to their presence in aquatic environment microplastics enters potential seafood varieties through feeding and other functional activities. Filter feeders like bivalves are affected particularly that results in direct human exposure as they are usually consumed whole (Abisha et al., 2024). Other than physical consumption, microplastics pose additional risk as they act as carriers of persistent organic pollutants, pesticides, and toxic minerals on their surfaces.

4 Role of AI in transforming the seafood sector

AI is revolutionizing the seafood industry by enhancing efficiency and sustainability across various sectors. AI-powered computer vision systems have significantly advanced seafood quality assessment, attaining high accuracy in detecting spoilage, texture variations, and contamination as observed by Genç et al. (2024) who demonstrated that a Convolutional Neural Network (CNN) + Long Short-Term Memory (LSTM) model achieved a 99.07% accuracy rate in classifying the freshness levels of frozen-thawed shrimp, surpassing traditional manual inspections. Regarding supply chain optimization, a study by Taghiyeh et al. (2020) introduced a multi-phase hierarchical forecasting approach, demonstrating improvements in forecast accuracy ranging from 82 to 90%. Traceability and food safety benefits from up to 80% accuracy as blockchain and AI-driven tracking systems ensure transparency and rapid identification of contaminated batches, thereby reducing health risks (Elgharbawy et al., 2023). Waste reduction also improves by approximately 65%, as AI algorithms minimize over-processing and spoilage through precise, real-time monitoring and predictive analytics (Fang et al., 2023). Additionally, operational efficiency and cost reduction experienced a 60%−70% enhancement driven by automation, smarter resource management, and AI-enhanced decision-making across logistics and supply networks (PatentPC, 2024). This transformation not only ensures higher product quality and safety but also supports a more sustainable and profitable industry. A conceptual framework of AI-Integrated seafood supply chain is presented in Figure 3.

5 Traceability: digital transparency in seafood value chains

The traceability pillar addresses the growing demand for verifiable, transparent seafood value chains. Blockchain-based smart ledgers integrated with IoT sensors ensure immutable records of origin, handling, and temperature history. AI-enabled seafood logistics technologies are transforming the industry by improving productivity, traceability as well as reducing side stream generation, the challenges faced for a long time due to its unique temperature dependant commodity profile. This unique nature renders seafood varieties with inimitable quality bottlenecks such as histamine accumulation in tuna (Trevisani et al., 2019), melanosis in shrimp (Tian et al., 2025), microbial spoilage in finfish and furthermore resulting in potential economic losses and export rejections as it passes through different laps of the logistics chain. It has been demonstrated that the machine learning (ML) models could be trained on historical shipment data allowing the industry stakeholders to foresee demand more precisely, recommend optimized shipping routes and systematize cold storage handling with minimum human intervention. Erikstad and Haugland (2025) reported that AI-based demand forecasting has enabled Norwegian companies to reduce inventory losses by up to 15% for Atlantic salmon exports to Japan by positioning harvest schedules with real-time market needs. Similarly, Singh et al. (2023) also pointed out that the shrimp export sector in India utilized predictive analytics using Artificial Neural Network (ANN) models for forecasting export prices.

5.1 Role of AI in maintaining cold chain integrity

One of the critical areas where AI provides transformative impact is cold chain monitoring. The optimum maintenance of temperature and humidity within the critical zone is necessary to prevent rapid deterioration and associated economic loss. AI-powered IoT sensors are now being used to continuously monitor environmental parameters in reefer containers and cold store facilities. Xiao (2021) observed that the integration of real-time AI-driven cold chain monitoring during storage trials of frozen tilapia minimized spoilage rate from 18 to 6%, simultaneously extending shelf life by up to 4 days when compared with conventional monitoring. Most importantly, the monitoring system also sends immediate alerts when situations fall below safety thresholds, empowering operators to take timely corrective action. This at the same time ensures food safety as well as safety compliances with stringent import regulations where even an isolated breach could result in shipment rejection. Spoilage and loss during transit can also be caused by fishing vessel and refrigerated container associated equipment failures. AI could play a crucial role in this scenario by applying predictive maintenance capabilities such as tracking indicators for compressor vibration, energy consumption, and coolant flow preventing breakdowns during shipment. Goikoetxea et al. (2024) demonstrated the potential of machine-learning techniques in contributing toward a more sustainable fisheries production in the context of Indian Ocean tuna purse seine fishery. This was achieved by modeling historical catch data of vessel fleets and environmental conditions for tropical tuna as target species, and silky shark as bycatch species using machine learning models. This AI intervention contributed toward improving the fishing industry's climate change adaptation capacity. Akhtar et al. (2024), used a MATLAB-based (Matrix Laboratory) linear regression model with an accuracy level of 95% to detect irregularities, evaluate trends, and forecast forthcoming maintenance requirements of cold storage in shrimp processing industries. AI is transforming cold chain management in the seafood industry through ensuring that perishable products remain fresh and safe throughout the supply chain. Two critical areas where AI plays a transformative role are real-time environmental monitoring and predictive maintenance of refrigeration systems along with seafood traceability and fraud prevention. A comparative analysis of key performance indicators (KPIs) are depicted in Table 1 which highlights how AI applications improve measurable outcomes across seafood cold-chain systems. By integrating IoT-enabled anomaly detection, reinforcement learning, and predictive analytics, studies report substantial gains in operational efficiency, including reductions in temperature excursions (Ismail et al., 2023), spoilage rates (Xiao, 2021), and energy consumption. These KPI-oriented frameworks demonstrate the transformative potential of AI to establish resilient, data-optimized, and sustainability-aligned seafood supply chains.

Table 1. Key cold-chain KPIs and AI-based performance improvements.

5.2 Role of AI in managing fraud and mislabeling

Seafood fraud, which includes marketing illegally sourced seafood, mislabeling species, as well as fabricating origins, is still observed as a significant issue worldwide. Blockchain technology combined with AI provides a creative solution for addressing this concern recently. Blockchain records every step of the seafood's journey, from the point of harvest to the point of delivery functioning as an unalterable digital log (Elgharbawy et al., 2023). By automating data entry, AI reduces the need for human interface while guaranteeing accuracy. AI-driven image recognition, combined with DNA barcoding and blockchain technology, helps tackle these issues by accurately identifying species and verifying the seafood's journey from ocean to plate. This ensures transparency and empowers consumers to make informed decisions. Blockchain technology has shown strong potential in enhancing seafood fraud detection across the entire supply chain. Konstantopoulou and Mikalef (2024) explored its potential application in Norwegian fish supply chains, highlighting how detailed digital recordkeeping can serve as proof that products comply with ethical and legal standards. By providing verifiable data on production practices, sourcing of raw materials, and precise geographical origin, blockchain can help consumers confirm whether seafood products meet expected standards. This contributes to combating various forms of fraud, including chain-of-custody abuse, IUU fishing, catch method fraud, illegal processing, and species substitution. The practical application of blockchain technology in combating seafood fraud has been demonstrated through pilot projects across different regions. One notable example is a project led by the World-Wide Fund for Nature (WWF), reported by Cook (2018), which utilized blockchain to monitor tuna caught in a Fijian longline fishery. This initiative aimed to create a tamper-proof digital record of each product's journey through the supply chain. Although the primary focus was on enhancing transparency and traceability, the initiative also illustrated blockchain's potential to mitigate seafood fraud by reducing risks such as species mislabeling, false origin claims, and illegal sourcing. In addition, Project Provenance Ltd. has leveraged the Ethereum blockchain to support verification of the origin and authenticity of yellowfin and skipjack tuna within Indonesian fish supply chains (Provenance, 2016). The initiative assists in tracing fish caught through responsible practices and contributes to reducing the presence of illegal, unreported, and unregulated (IUU) seafood. Likewise, integrating blockchain with Internet of Things (IoT) technologies such as Global Positioning System (GPS), Radio Frequency Identification (RFID) tags, and Quick Response (QR) codes, has been applied to monitor vessel locations, fishing activities, catch records, and maritime routes. These tools play a critical role in preventing IUU fishing, chain of custody violations, fishery substitution, and fraudulent catch method claims (Grecuccio et al., 2020; Marttila et al., 2019). Furthermore, AI-powered DNA analysis tools assist in verifying species and identifying fraudulent changes, such as replacing expensive seafood varieties with cheaper alternatives (Tinacci et al., 2023).

5.3 Role of AI in managing logistics disruptions

AI-driven predictive analytics have proven its advantage in enabling early forecasting and rerouting that could help in avoiding fishing delays, delays in aquaculture harvests, transport and further more resulting in minimal economic loss. Especially during recent pandemics, AI-based demand forecasting and inventory optimization ensured improved resource allocation and waste reduction when lockdowns restricted workforce availability and logistics capacity. Machine learning models such as Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM) can be trained on historical shipment data to predict demand and optimize logistics. During geopolitical tensions, such as the Russia–Ukraine conflict also AI-powered dynamic routing and supplier risk assessment provided adaptability through identifying alternative routes and diversifying sourcing (Sun and Ma, 2024). AI-enabled IoT sensors has the potential to help during cold storage equipment breakdowns through continuous monitoring of storage conditions, detecting potential anomalies, and activating advanced preventive maintenance before actual crisis (Adimulam et al., 2024). Spoilage and financial losses can result from malfunctioning of refrigeration and transportation equipment. The predictive maintenance capabilities of AI could be utilized to address this issue. AI detects possible problems early by constantly tracking engine performance, temperature changes, and vibration patterns, as well as mechanical wear on delivery trucks, fishing boats, in addition to refrigeration equipment (Mahale et al., 2025). For instance, the system notifies operators and suggests preventive maintenance if a refrigeration unit begins using more electricity than usual, which is an indication of failing components (Adelekan et al., 2022). This proactive strategy lowers the need for expensive emergency repairs, guarantees on-time delivery, and minimizes downtime. In the end, it promotes a stronger, more dependable seafood supply chain particularly for shipments abroad, requiring a more precise temperature control. Furthermore, AI can also assist in predictive maintenance integrating with IoT-enabled cold chain monitoring systems that allows live monitoring and tracking of cargo temperature, humidity, and equipment functioning (Elkateb et al., 2024). More than a mere alert mechanism of machine failure, these systems could generate predictive forecasting based on historical data by applying machine learning models to prepare and avoid future critical threshold breaches. In addition to this, AI based predictive models could be combined with digital twin simulations of seafood logistics networks, empowering administrators to assess situations such as failure of critical equipment during intercontinental transport and strategize corrective actions before the occurrence of the actual disruption (Kreuzer et al., 2024). By connecting predictive analytics with operational decision-making, AI reinforces resilience, waste reduction, and promotes sustainability in seafood logistics, ensuring a safe and efficient transport of high-value products globally.

6 Sustainability: AI for resource optimization and circular economy

The sustainability concept focuses on enhancing operational efficiency, reducing waste, and promoting circular resource use. Digital Twin models simulate processing and logistics systems for predictive control and energy optimization, while AI-driven robotics and maintenance scheduling reduce material loss. These advances align with broader sustainability goals, ensuring that seafood production and distribution remain both economically and environmentally resilient. The integration of artificial intelligence (AI) and blockchain technology offers a promising and sturdy solution to combat IUU fishing and improve traceability in seafood supply chains (Wang, 2024). Blockchain offers a decentralized, tamper-resistant digital ledger where every operation or movement of seafood is recorded from the point of harvest to the end consumer. When joined with AI-driven data analytics and automated validation, this system creates a transparent, secure, and efficient mechanism to confirm the legitimacy of seafood products and enhance liability across the supply chain (Tripathi et al., 2023).

AI technologies improve traceability through satellite imaging, remote sensing, and Vessel Monitoring Systems (VMS). Machine learning algorithms can analyze real-time vessel location data, fishing patterns, and port activities to identify suspicious behaviors, such as unregistered vessels operating in restricted zones or vessels that disable tracking transponders which is an indicator of potential IUU activity (Ferreira et al., 2022). These predictive models facilitate enforcement agencies and monitoring organizations to spot irregularities, forecast risk areas, and respond more rapidly to illegal operations. For example, an international NGO named Global Fishing Watch, uses machine learning algorithms to analyze data from the Automatic Identification System (AIS) and VMS to map global fishing activity and detect anomalies. Their platform provides open-access, near real-time data on fishing efforts, allowing governments, researchers, and NGOs to monitor and manage fisheries more effectively (Welch et al., 2024) which has been contributory in detection of illegal fishing operations in marine protected areas and has assisted to prosecute culprits and impose marine conservation laws.

Blockchain technology adds another layer of accountability by providing an incontrovertible digital trail for seafood products. Firms such as Provenance and IBM Food Trust have implemented blockchain solutions that document each stage of the supply chain ranging from catch certificates and processing logs to distribution records and retail metadata (Carlos et al., 2025). Consumers can scan QR codes on seafood packaging to access detailed information about the origin, species, fishing method, and sustainability certifications, allowing them to make informed purchasing decisions and increasing faith in the product (Li et al., 2024). AI and blockchain technology are being applied in the aquaculture sector to track feed sources, antibiotic usage, water quality parameters, and harvest data. This is particularly significant as aquaculture is increasingly becoming a leading source of seafood. Implementing AI-enabled responsible aqua farming practices helps in preventing fraud, supports certification efforts (ASC, BAP), and ensures better management of disease outbreaks and resource utilization (Roy et al., 2025). Furthermore, merging blockchain with biometric and genetic identification techniques enhances the reliability of traceability systems. DNA barcoding and stable isotope analysis can verify the species and geographic origin of seafood, helping to detect species substitution and mislabeling which are common forms of seafood fraud (Yang et al., 2024). AI-enabled platforms can automate the analysis and matching of these biological datasets against blockchain records to guarantee consistency and accuracy. The enforcement of legal and sustainable fishing practices is further strengthened when blockchain data is linked with AI-powered smart contracts (Landge et al., 2025). These contracts automatically implement regulatory requirements or financial transactions when certain conditions are met. For instance, a smart contract could avert payment release to a supplier unless catch documentation matches blockchain-verified data and sustainability certifications (Patro et al., 2022). This automation potentially decreases the chances for human error and enhances compliance with environmental and legal standards.

Building on this potential, the Norwegian Seafood Association and the Scandinavian IT infrastructure company Atea launched the Norwegian Seafood Trust in 2020, a national tracking network using IBM's blockchain technology to enhance transparency in the seafood industry (Hawk, 2021). Through a mobile app, participants can scan each salmon lot at different points along the supply chain. Although the initiative is promising, it still faces challenges related to the complexity and global scale of seafood logistics. To address these issues, future developments consider integrating blockchain with Internet of Things (IoT) technologies. This combination allows for automatic capture of product movements using RFID tags or embedded sensors, enabling real-time tracking and reducing the risk of deliberate or accidental mislabeling.

Extending the implications of the WWF initiative, Visser and Hanich (2018) underscored that blockchain technology can also help uncover unethical labor practices in seafood supply chains such as exploitation and unsafe conditions, particularly in low-income regions where such abuses constitute a serious form of fraud akin to modern day slavery. Despite its promise, blockchain adoption still faces several obstacles, such as the reliability of recorded data, the need for full digitalization across supply chains, high implementation costs, and the complexity of integrating supporting technologies like RFID and sensors (Cook, 2018).

In spite of its potential, several challenges must be addressed for the effective employment of AI and blockchain in contending IUU fishing. The expense of installing advanced monitoring and data systems can be challenging, particularly for small-scale fisheries in developing nations (Jiao et al., 2025). Capacity building, infrastructure development, and financial incentives are required to bridge the digital divide and guarantee equal accessibility. Moreover, data interoperability and standardization are vital for integrating different systems across global supply chains. Privacy concerns and the need for regulatory frameworks to govern the use of data, particularly location and proprietary operational information, require consideration. Governments and intergovernmental bodies must cooperate to establish policies that balances transparency along with the safeguarding of sensitive data. Regulatory alignment and international cooperation are crucial to enforcing blockchain traceability and AI monitoring on a global scale (Yang et al., 2024). In conclusion, the conjunction of AI and blockchain technologies presents a transformative opportunity to fight IUU fishing and establish a transparent, sustainable seafood supply chain. By leveraging on real-time surveillance, incontrovertible data records, and predictive analytics, these technologies address the core challenges of traceability, confirmation, and compliance. Successful execution will depend on inclusive policies, stakeholder collaboration, and investment in digital infrastructure. As consumer demand for ethically sourced seafood increases, embracing these technologies is essential for marine conservation, regulatory compliance, and the long-term sustainability of the seafood industry.

AI improves traceability systems by incorporating blockchain technology to produce an unchangeable record of every shipment's path from the ocean to the table, guaranteeing transparency and regulatory compliance. In international seafood markets, seafood fraud that includes mislabeling, weight inflation, and species substitution remains a serious threat. In addition to misleading customers, this endangers public health as well as sustainable fisheries. AI-driven computer vision systems as well as DNA analysis tools identify product adulteration and species replacement. To identify abnormalities, machine learning models compare textural information, chemical profiles, or fish photos to validated databases (Tinacci et al., 2023). Furthermore, seafood is properly labeled with complete visibility into its origin, handling, and processing phases (Elgharbawy et al., 2023). Figure 4 gives a bird's eye view of Sustainability and traceability application of AI. The seafood industry faces significant challenges in striking a delicate balance between meeting demand and minimizing waste. AI-powered demand forecasting and inventory optimization systems offer a transformative approach toward managing this challenge, enabling businesses to predict market trends accurately, optimize stock levels, and reduce losses. Table 2 depicts some important AI applications in seafood forecasting, inventory, and logistics.

Table 2. AI applications in seafood forecasting, inventory, and logistics.

7 Food safety: AI for quality assurance and contaminant monitoring

The food safety concept encompasses AI-driven systems designed to ensure the chemical, microbiological, and sensory integrity of seafood. Machine learning models interpret multi-sensor data from electronic noses, hyperspectral imaging, and biosensors to detect early spoilage and contamination. AI-assisted analytical platforms for PFAS, BFRs, and microplastics improve sensitivity and accuracy, supporting proactive safety management throughout the cold chain.

7.1 Role of AI in addressing quality assessment limitations

The conventional seafood quality assessment framework is increasingly becoming limited in the aspect of addressing ever changing quality risks and health hazards that need rapid detection and swift corrective measures. AI based technological interventions are now addressing these limitations and further transforming the concept of seafood quality assurance to a more rapid version of hazard monitoring and control with an advantage of advanced forecasting provisions. AI components such as computer vision and machine learning algorithms have added an additional level of precision recently to these sensory assessments. Key characteristics of seafood, including color, texture, size, as well as surface irregularities, may be examined using high-resolution photography and pattern recognition tools (Prabhakar et al., 2020). AI, can distinguish between fresh and spoiled seafood based on minute color variations that a human eye could miss (Kumaravel et al., 2025). Furthermore, quality identification is improved by combining AI with sophisticated sensors that detects pH, temperature, as well as gas emissions such as ammonia (Ziani et al., 2025). By interpreting this data in real-time, AI may spot early degradation indicators and stop items from moving farther down the supply chain. This not only guarantees better quality but also lowers food waste and safeguards the health of consumers. AI related technological inventions like Internet of Things (IoT) and smart sensors could perform regular real-time monitoring of environmental health by collecting water quality and other ecosystem health indicators enabling early warning systems for emerging hazards like harmful algal blooms (Lakshmi Narayana et al., 2024). Other than that, machine learning (ML) and blockchain applications provides complete cold chain traceability and transparency by providing a reliable tamperproof digital data transmission from production to consumption, thereby empowering stakeholders with reliable information. Also, during the crucial stages of seafood transit through the cold chain, AI-driven systems such as computer vision and smart biosensors could facilitate automatic detection of pathogens, spoilage organisms, and chemical contaminants, while using predictive modeling to generate reliable advance hazard alerts (Deng et al., 2025). According to De Cock et al. (2025), AI can also support the consumers in accurately assessing the risks associated with seafood consumption by avoiding critical allergens through sophisticated risk-benefit assessments. They also points out that simultaneously, it could also help them to leverage Findable, Accessible, Interoperable, and Reusable (FAIR) data principles to integrate various datasets on hazards and nutritional components to make intelligent nutritional decisions. This could help certain vulnerable consumer groups toward a more safer seafood choices through a more informed decision-making. Therefore, the incorporation of AI-related tools throughout the seafood supply chain assures a more resilient system with enhanced safety, quality, and sustainability of seafood products while efficiently reducing public health hazards and capitalize on the nutritional benefits for consumers. Comparative evaluation of different AI approaches across seafood logistics and quality monitoring studies is summarized in Table 3. These studies demonstrate that computer vision systems are particularly effective for automated quality inspection, while machine learning models perform strongly in forecasting, predictive maintenance, and demand prediction tasks. The integration of machine learning and computer vision has enabled predictive modeling of freshness, optimization of cold-chain operations, and reduction of logistics delays. These studies collectively demonstrate that AI-driven analytics not only enhance product quality and traceability but also contribute to improved supply-chain efficiency and sustainability across various seafood commodities.

Table 3. AI applications in seafood logistics and quality control.

7.2 Role of AI in addressing emerging contaminants

The challenge with many of the CECs are they are often present in lower concentrations making it difficult to monitor using conventional methods. AI could assist in addressing these new risks through developing rapid, cost-effective, and highly sensitive methods that could lead toward better detection, prediction, and risk assessment (Canchola et al., 2025). Sophisticated methods such as chromatography, mass spectrometry, and microscopy are some of the reliable methods currently used to evaluate different chemical hazards in seafood even though they are inadequate considering scalability and real-time application. AI can complement such methodologies by applying machine learning models and assist in analyzing large datasets from these methods along with imaging to recognize unique chemical or physical signatures associated to contaminants (Cai et al., 2025). For example, deep learning AI algorithms could recognize spectral patterns of methylmercury or BPA, while advanced algorithms can determine complex mixtures of PFASs and BFRs (Zou et al., 2023). Identically, AI-based image recognition could accelerate identification and quantification of microplastics in seafood (Jin et al., 2024). Other than detection of these contaminants, AI based predictive modeling integrates environmental, hydrological, and biological data for forecasting contamination hotspots like that of in the case of aquaculture sites which are prone to antibiotic or UV filter residues (Slater et al., 2023; Okeke et al., 2022). AI can also play a major role in toxicological risk assessment and traceability utilizing machine learning models that could connect contaminant levels with exposure scenarios and health consequences, supporting the establishment of validated safety thresholds (Hartung, 2023). AI-enabled blockchain systems could contribute toward improving traceability by correlating contamination data with specific harvesting, processing, or distribution stages, enabling rapid recalls and strengthening consumer trust (Femimol and Joseph, 2025). Using AI platforms such as machine learning, predictive analytics and IoT-enabled monitoring, a comprehensive defensive mechanism against emerging contaminants could be established for preservation of public health while ensuring regulatory compliance as well. Recent advances in AI-assisted analytical platforms have significantly improved the detection and quantification of seafood contaminants, including brominated flame retardants (BFRs), per- and polyfluoroalkyl substances (PFAS), and microplastics. As summarized in Table 4, AI-enabled chemometric and imaging approaches increase sensitivity and classification precision in contaminant detection, facilitating faster and more reliable risk assessment. The integration of deep learning and biosensor data fusion further strengthens the monitoring of multiple chemical hazards within seafood, supporting data-driven food safety assurance. Table 5 depicts the AI empowered blockchain applications in seafood supply chain.

Table 4. Detection and monitoring of contaminants (BFRs, PFAS, Microplastics).

Table 5. AI empowered blockchain applications in seafood supply chain.

8 Emerging AI technologies in seafood processing

8.1 Edge AI

Edge AI refers to the implementation of artificial intelligence directly on local devices, such as smart sensors or processors, without relying on continuous cloud connectivity. In seafood processing and logistics, this technology is crucial for real-time data analysis and decision-making. For example, during transit monitoring, Edge AI-enabled sensors can immediately detect deviations in temperature, humidity, or vibration that may compromise seafood quality. The system can then autonomously trigger corrective actions, like adjusting refrigeration settings or alerting the logistics team, ensuring the seafood remains fresh (Waldhans et al., 2024). Similarly, during seafood inspections, AI algorithms embedded within handheld scanners or conveyor systems can instantly analyze fish quality, detect contaminants, or assess size and weight, streamlining the sorting and grading processes. This localized data processing minimizes latency, enhances system reliability, reduces data transmission costs, and ensures operations continue even in environments with poor internet connectivity—an essential feature for remote fishing locations and onboard processing vessels (Ismail et al., 2023).

8.2 Digital twins

Digital twins are advanced AI-driven virtual models that mirror the physical operations of seafood processing facilities. These dynamic simulations integrate data from various sources including production equipment, environmental sensors, and human input to replicate real-world conditions in a virtual environment. By doing so, they enable seafood processors to visualize, test, and optimize workflows without disrupting actual operations. For instance, digital twins can simulate the effects of changing production speeds, reorganizing assembly lines, or introducing new machinery to predict the impact on throughput and product quality (Chen et al., 2025). Additionally, they help anticipate potential bottlenecks, such as machinery failures or resource shortages, and suggest preventative measures to maintain efficiency. Beyond performance optimization, these digital replicas also support sustainability efforts by modeling energy consumption and waste generation, helping facilities fine-tune processes for greener, cost-effective production. In the long term, the continuous learning capability of digital twins ensures they evolve alongside the physical plant, providing ever-more accurate predictions and recommendations (Zhao et al., 2023).

8.3 AI-driven robots

AI-driven robotic systems are rapidly advancing in seafood processing and logistics, enabling high-precision automation of tasks such as grading, fileting, trimming, conveying, and packaging. Recent work in the fish-processing industry highlights that robots can now handle variable raw materials such as whole fish of varying species and sizes by leveraging machine vision, adaptive tooling, and real-time control to optimize yield and quality (Einarsdóttir et al., 2022). Azarmdel et al. (2021) proposed a vision-based system to process trout through belly-cutting, beheading, gutting and cleaning stages by integrating imaging, mechanical design and control algorithms tailored for fish of different dimensions. More lately, robotic solutions for fish filet-shaping, combining AI-powered computer vision with adaptive robotic arms and force-control, have been deployed in production lines to improve output and consistency while reducing manual labor (Mainali and Li, 2025). Similarly, robotic cleaning systems designed for fish-processing equipment demonstrated how robotics address hygiene and operational sustainability by automating physically demanding tasks with precision (Giske et al., 2019). This conjunction of AI, robotics and sensor-driven automation is central to evolving smart seafood factories that not only enhance productivity but also reduce waste, improve worker safety, and contribute to a sustainable digital blue economy.

9 Ethical concerns in AI-driven seafood management

9.1 Job displacement

The introduction of AI in seafood processing has the potential to revolutionize efficiency, but it also raises concerns about job displacement, particularly for low-skilled workers who perform repetitive, manual tasks. Automated fileting machines, AI-driven sorting systems, and autonomous quality inspection robots can outperform human labor in terms of speed, precision, and consistency, leading to a reduced need for traditional roles (Fang et al., 2023). This shift can disproportionately affect coastal communities and regions where seafood processing is a key source of employment. However, businesses face the challenge of striking a balance between automation and human involvement. To mitigate job losses, companies can invest in retraining initiatives, helping workers transition to more technical or supervisory roles, such as AI system maintenance, data analysis, or quality oversight. By fostering a workforce skilled in managing and collaborating with AI technologies, businesses can create a hybrid operational model that enhances productivity while preserving jobs (Rane et al., 2024). Governments and industry associations can also play a crucial role in supporting reskilling programs to ensure the human workforce evolves alongside technological advancements.

9.2 Privacy of data

As AI systems integrate with real-time monitoring and blockchain traceability in seafood supply chains, data privacy becomes a critical concern. These systems collect vast amounts of sensitive data including catch locations, transportation routes, processing metrics, and even proprietary production methods. For large seafood companies, this data is invaluable for ensuring product quality, traceability, and regulatory compliance. However, unauthorized access to such information could lead to competitive disadvantages, price manipulation, or even supply chain sabotage (Elgharbawy et al., 2023). Additionally, blockchain traceability, while enhancing transparency and consumer trust, requires secure handling to prevent tampering or misuse. Companies must invest in robust cybersecurity measures, including encryption, access controls, and secure data storage, to protect both internal operations and third-party supply chain data. Compliance with international data protection regulations, such as the General Data Protection Regulation for exports to the European Union, is also crucial (Thilakarathne et al., 2025). By prioritizing data privacy and cybersecurity, seafood businesses can safeguard their competitive edge while maintaining trust across global markets.

9.3 Fair access

AI technology holds the promise of transforming seafood production, but its high cost can create a significant divide between large corporations and small-scale fisheries, particularly in developing nations. Advanced AI-driven processing equipment, blockchain integration for traceability, and smart logistics monitoring systems require considerable upfront investment, infrastructure, and ongoing maintenance. While large seafood processors may absorb these costs and benefit from increased efficiency and higher yields, small-scale fishers and local producers may find such technology prohibitively expensive (Ahmed et al., 2023). This disparity can widen the economic gap, with smaller operations struggling to compete in global markets due to slower, less consistent production and reduced access to traceability tools which are increasingly demanded by international buyers. To promote fair access, governments, non-profits, and industry leaders can collaborate on initiatives such as subsidized technology programs, shared AI processing facilities, or open-source AI models designed specifically for smaller operations. Ensuring equitable access to AI advancements can empower regional producers, support sustainable fishing practices, and preserve the livelihoods of smaller seafood communities (Fang et al., 2023).

10 Barriers to AI adoption in the seafood industry

Despite its potential advantages, blockchain implementation in seafood supply chains faces practical limitations. The deployment of blockchain infrastructure requires significant investment in digital systems, data storage, and network integration. Additionally, interoperability challenges arise when integrating blockchain platforms with existing logistics databases, IoT monitoring systems, and international regulatory frameworks. These limitations may slow adoption, particularly among small-scale fisheries and developing economies where digital infrastructure remains limited.

10.1 Expensive implementation

Implementing AI systems in seafood processing involves significant upfront costs that can be daunting for smaller businesses. The expenses extend beyond just purchasing hardware, like smart sensors, robotic systems, and computing infrastructure—they also include specialized software tailored to the seafood industry's unique requirements. Additionally, staff must be trained to operate, maintain, and optimize these AI-driven systems, which can further escalate costs. Smaller seafood processors, which often operate on tight margins, may struggle to justify such an investment, especially when the return on investment (ROI) isn't immediately visible (Ahmed et al., 2023). Unlike larger enterprises that can absorb these expenses, smaller companies may face a competitive disadvantage, potentially slowing the industry's overall digital transformation. Without affordable, scaled-down AI solutions tailored for small and medium-sized processors, this cost barrier remains a significant obstacle to widespread adoption (Fang et al., 2023).

10.2 Insufficient technological knowledge

Many seafood companies, especially those that have operated traditionally for decades, may lack the technical expertise required to integrate and manage AI systems effectively. Understanding machine learning algorithms, data analytics, and robotic automation isn't typically part of the skill set in these businesses. As a result, they often need to rely on external consultants or technology providers for implementation and ongoing support. This dependency can be costly and may lead to operational inefficiencies if communication gaps arise between the seafood experts and the tech specialists (Rane et al., 2024). Furthermore, businesses that attempt to upskill their workforce internally may face steep learning curves, causing delays in adoption and potential misconfigurations that reduce the system's effectiveness. To bridge this gap, accessible training programs, industry-specific AI solutions, and user-friendly interfaces will be essential to empower seafood companies to manage their technology independently over time (Fang et al., 2023).

10.3 Variable features of seafood

Seafood presents a unique challenge for AI systems due to its natural variability. Unlike uniform products like packaged snacks or beverages, seafood differs significantly in size, shape, texture, and quality even among the same species. For example, wild-caught fish may vary more than farmed ones due to differences in diet and environment, and shellfish can present irregular shapes and hardness levels. AI models, especially those using computer vision for grading or robotic systems for fileting, rely on training data to make accurate predictions and adjustments. However, the wide range of seafood characteristics makes it difficult to create a one-size-fits-all model. If the training data isn't diverse enough, the system may struggle with underrepresented variations, leading to misjudgements, inconsistent cuts, or incorrect quality assessments (Ahmed et al., 2023). To address this, companies need robust datasets that capture a comprehensive range of seafood variations, coupled with adaptable AI algorithms that continuously learn and refine their accuracy through real-world operation (Genç et al., 2024).

10.4 Uncertainty in regulations

The rapid advancement of AI technology often outpaces the development of regulatory frameworks, creating ambiguity around compliance, particularly in food safety and labeling. Seafood processing involves strict guidelines to ensure freshness, hygiene, traceability, and accurate nutritional information. AI systems that influence these processes—such as automated quality assessments, sorting, and packaging—may introduce new uncertainties. For instance, if an AI system misclassifies a batch of seafood, who holds accountability: the manufacturer, the AI provider, or the operator? Moreover, international seafood markets may have varying regulations, creating additional compliance hurdles for exporters using AI (Elgharbawy et al., 2023). Food safety authorities may also require detailed transparency on how AI algorithms assess and ensure quality—a demand that's difficult to meet when companies use proprietary machine learning models that are often considered “black boxes” (Rane et al., 2024). To navigate this, seafood companies must stay agile, closely monitor evolving regulatory landscapes, and collaborate with policymakers to ensure AI advancements align with food industry standards without stifling innovation.

Hybrid AI models that improve the accuracy of seafood quality assessments by fusing computer vision with biochemical data (volatile chemicals) (Genç et al., 2024). AI-driven climate resilience tools that forecast fish migratory patterns, assisting fisheries in adjusting to changing populations and warmer oceans (Fang et al., 2023). Decentralized AI for small-scale fisheries, which might be implemented through cloud-based systems or simple mobile apps, allows smaller producers to use AI solutions without having to spend heavily on infrastructure. Sustainable AI development to make sure that AI systems' energy usage, especially in deep learning models, complies with the seafood industry's more general environmental objectives.

11 The way forward with AI in seafood sector

Unquestionably, AI has the potential to revolutionize the seafood sector, but its success depends on cooperation between governments, business executives, technological companies, as well as environmental groups. Clear laws must be established by governments to guarantee data privacy, ethical AI usage, and fair access to technology, particularly for small-scale farmers and fishermen (Elgharbawy et al., 2023). Real-time data collecting and AI applications will also be made possible by infrastructure upgrades, such as increasing dependable internet connectivity in isolated fishing areas. Additionally, cross-sector collaborations can aid in the development of more affordable AI solutions that are available to all industry participants, not just big businesses (Fang et al., 2023). It is necessary to build safer, more flexible, and environmentally friendly global food systems by incorporating AI into seafood operations in conjunction with strong regulations and infrastructural improvements, guaranteeing the seafood industry's continued success for future generations. With cutting-edge solutions like computer vision for quality inspection, machine learning for demand forecasting along with spoilage prediction, blockchain for improving traceability and transparency, and Internet of Things (IoT) sensors for real-time monitoring of cold chains and aquaculture environments, AI is becoming a disruptive force in addressing these issues (Ismail et al., 2023). The seafood business can increase consumer trust, enhance operations, save waste, and improve product safety by incorporating AI technology. Predictive analytics powered by AI can also assist manage resources and fisheries sustainably, balancing environmental preservation with productivity (Rane et al., 2024). Continued investment in AI research, cross-sector collaboration, and governmental support will be essential to speeding the use of these technologies as the industry navigates infrastructure constraints, cost restrictions, and data privacy issues. Adopting AI advancements can eventually result in a seafood industry that is more robust, and effective, along with sustainable, guaranteeing future generations' food security as well as financial stability. The future of AI integration in seafood logistics and quality management must evolve toward an ethically grounded and socially inclusive model that benefits all players within the value chain. Figure 5 outlines the proposed research roadmap structured around three interlinked pillars such as small-scale fisheries access, ethical and explainable AI, and equitable technology access, which collectively guide the transition toward a sustainable digital blue economy. Future investigations should prioritize the development of lightweight, open-source AI tools adaptable to small-scale fisheries, enabling local data generation and decision support in low-resource locations. At the same time, research on ethical and explainable AI frameworks is essential to ensure algorithmic transparency, bias mitigation, and regulatory compliance. Finally, achieving equitable technology access requires interdisciplinary collaborations to design affordable IoT infrastructure, inclusive data policies, and shared digital governance models. Also, adoption of FAIR data principles (Findable, Accessible, Interoperable, and Reusable) is critical for enabling seamless integration between AI analytics, IoT monitoring systems, and blockchain traceability platforms across global seafood supply chains.

Integrating these three dimensions will enable a transformative shift toward human-centered, transparent, and globally accessible AI systems that strengthen sustainability, traceability, and social responsibility in seafood value chains. To ensure equitable adoption, future initiatives should focus on developing low-cost AI tools accessible to small-scale fisheries. Mobile-based monitoring systems, open-source AI platforms, and shared digital infrastructure models can significantly reduce technological barriers. Public–private partnerships and international development programs can also support training, infrastructure development, and financing mechanisms for digital transformation in small-scale fisheries.

12 Conclusion

This article deals with transformative role of Artificial Intelligence (AI) in seafood quality assessment and logistics management. The objectives outlined in the introduction were addressed by systematically reviewing AI applications across seafood supply chains, evaluating their role in improving cold chain monitoring, traceability, fraud prevention, and predictive logistics management. Ensuring seafood safety is a significant challenge due to its high perishability, susceptibility to contamination, and the complexity of the global supply chain. Maintaining seafood quality throughout the supply chain is challenging due to temperature fluctuations, microbial spoilage, oxidation, and texture degradation. The industry faces issues like seafood fraud, where lower-quality seafood is mislabeled as premium, eroding consumer trust. Effective monitoring, storage, and transportation are essential to prevent spoilage and economic losses. AI is reshaping the seafood sector by improving quality control, enhancing supply chain transparency, reducing waste, and optimizing logistics. By integrating machine learning, IoT sensors, computer vision, and blockchain technology, the industry can achieve higher efficiency, lower costs, and greater sustainability. Future advancements in AI will further streamline seafood processing, transportation, and safety compliance, ensuring high-quality seafood reaches consumers worldwide. AI is revolutionizing seafood quality assessment through machine learning (ML) and deep learning (DL) models that can predict spoilage and detect contaminants. Computer vision systems analyze seafood images to identify defects, color changes, and microbial contamination. IoT-powered smart sensors continuously track real-time data such as temperature, pH levels, and gas composition, ensuring seafood remains fresh throughout the supply chain. The integration of artificial intelligence (AI) is revamping sustainability efforts and contributing to the development of the seafood industry. AI is changing the seafood sector by providing previously unheard-of levels of precision, efficiency, and transparency in logistics management and seafood quality evaluation. AI technologies are improving product quality while lowering operating expenses and human error, from computer vision systems that detect spoiling to machine learning algorithms that anticipate the freshness of seafood to smart IoT sensors that guarantee cold chain stability. The industry's capacity to preserve freshness, stop fraud, and streamline supply chains is being further reinforced by cutting-edge techniques like hyperspectral imaging, blockchain traceability, and autonomous logistics. AI's contribution goes beyond operational enhancements, thereby it tackles significant issues specific to the seafood sector, such as the perishable nature of goods, the necessity for a cold chain, and the increasing need for sustainable practices. Demand forecasting, AI-driven route optimization, as well as predictive maintenance of refrigeration systems all help to reduce food waste and carbon footprints, which supports international sustainability initiatives. AI has enormous potential to propel industry expansion. AI enables businesses to satisfy customer demands for premium, ethically sourced seafood by promoting more transparency, improving food safety, and increasing supply chain efficiency. AI advancements also aid in the creation of substitute seafood items, such as plant-based or lab-grown substitutes, supporting efforts to conserve the ocean and ensure food security. However, realizing this promise necessitates overcoming obstacles including high implementation costs and moral dilemmas like data privacy and employment displacement. Future developments in scalable, easily available AI technologies, along with encouraging legislation and employee retraining, will be essential to guarantee that big businesses and small-scale fishers alike use technology fairly. In summary, AI has the potential to revolutionize the seafood sector by guaranteeing sustainability, safety, and outstanding quality from the ocean to the table. An innovative, inclusive, and responsible approach to embracing new technologies will create a seafood supply chain that is more robust, effective, and sustainable in the future. Despite all of these advantages, sustainability practices come with challenges, i.e., higher costs and administrative problems, which could hinder short-term business growth. Balancing sustainability and growth are essential in a bid to sustain long-term economic wellbeing without damaging the environment.

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Footnotes

Edited by: Johannes Iitembu, University of Namibia, Namibia

Reviewed by: T. Anukiruthika, University of Manitoba, Canada

Aline Issa, Notre Dame University – Louaize, Lebanon