Automation in the Shellfish Aquaculture Sector to Ensure Sustainability and Food Security

Abstract

Shellfish aquaculture is considered a major pillar of the seafood industry for its high market value, which increases the value for global food security and sustainability, often constrained in terms of conventional, labor-intensive practices. This review outlines the importance of automation and its advances in the shellfish value chain, starting from the hatchery operations to harvesting, processing, traceability, and logistics. Emerging technologies such as imaging, computer vision, artificial intelligence, robotics, IoT, blockchain, and RFID provide a major impact in transforming the shellfish sector by improving the efficiency, reducing the labor costs and environmental impacts, enhancing the food safety, and providing transparency throughout the supply chain. The studies involving the bivalves and crustaceans on their automated feeding, harvesting, grading, depuration, non-destructive quality assessments, and smart monitoring in transportation are highlighted in this review to address concerns involved with conventional practices. The review puts forth the need for integrating automated technologies into farm management and post-harvest operations to scale shellfish aquaculture sustainably, meeting the rising global demand while aligning with the Sustainability Development Goals (SDGs).

1. Introduction

Global fisheries and aquaculture production reached a record 223 million tonnes in 2022, including close to 186 million tonnes of aquatic animals. Among them, aquaculture production reached 131 million tonnes, and for the first time, farmed aquatic animals (94 million tonnes) exceeded wild-caught volumes, showing aquaculture’s central role in future food security and sustainability agendas [1]. Among them, shellfish aquaculture involving the culture of bivalves like clams, oysters, mussels, and scallops, and crustaceans like shrimps, crabs, lobster, and krill is economically beneficial for their importance and popularity among consumers worldwide [2]. It makes up around 70% of the aquaculture species and recently contributed to around 35 million tonnes of production in 2024 [3]. The global revenue from shellfish has seen an increase from USD 4 billion in 1985 to over USD 120 billion by 2024, necessitating the need for increased production. Shellfish aquaculture is not only economically substantial but also irreplaceable for nutritional and environmental sustainability. For example, bivalve shellfish need no feed as inputs and can even improve water quality, supporting the United Nations Sustainable Development Goals (SDGs) related to health, hunger, and environmental stewardship [4]. In fact, seafood provides essential micronutrients and omega-3 fatty acids often lacking in land-based proteins, making it a crucial factor in nutritious diets worldwide. Developing the shellfish sector is thus seen as a calculated path to enhance food supply sustainably, support coastal economies, and promote multiple SDGs, primarily Zero Hunger (SDG2), Decent Work and Economic Growth (SDG8), and Life Below Water (SDG14) [2]. However, to fully understand these benefits, the shellfish farming industry must overcome significant challenges. Many shellfish farms still rely on labor-intensive, traditional practices, limiting their productivity and resilience [5]. As demand for seafood rises and climate pressures increase, there is a growing recognition of the need for technological innovation and automation throughout the shellfish supply chain from production in farms to harvest and distribution.

Automation and smart aquaculture tools have the potential to improve efficiency, lessen environmental impact, and ensure the sustainability aspect in the industry. Automation technologies, including IoT, robotics, AI, and blockchain, have emerged as transformative tools to enhance operational efficiency, reduce mortality, and promote sustainability in global shellfish farming systems, briefly discussed in Table 1 [1,5,6]. This review aims to consolidate these developments and analyze their contribution to sustainable intensification and food security by focusing on farmed shellfish species such as oysters, mussels, clams, scallops, and farmed crustaceans like shrimps, lobsters, and crabs. It discusses the importance of the shellfish sector and outlines recent advances in automation aimed at improving its sustainability. This review will also discuss global practices across major shellfish species, identify the needs and objectives driving automation, and examine current developments in automating production (hatchery, grow-out, monitoring) and harvest stages of farmed shellfish. Key challenges to implementing these technologies and future trends are also analyzed. The literature review followed a structured process using Scopus, Web of Science, and ScienceDirect databases with the keywords ‘automation’, ‘IoT’, ‘robotics’, ‘blockchain’, ‘artificial intelligence’, and ‘shellfish aquaculture’, covering the period 2010–2025 and limited to peer-reviewed and institutional sources.

1.1. Shellfish Aquaculture Species, Production, and Availability

Shellfish aquaculture includes a diversity of species and farming practices worldwide. Molluscan shellfish, mainly clams, mussels, oysters, and scallops, are basic species, widely farmed in coastal regions on most continents. Crustacean shellfish, such as crabs, lobsters, and shrimps, also represent a large share of aquaculture production [7]. Many of these species have faster life cycles and can be bred and grown to market size in shorter time (often 1 to 2 years or less), which gives shellfish farming a capacity for rapid production compared to slower-growing fish [2]. For example, shrimp reached harvest size in just 4 to 6 months, while mussels and oysters typically reach harvest size in 12 to 24 months, whereas some farmed fish can take several years [8]. This rapid throughput makes shellfish well-suited to meet rising demand on time.

Global shellfish production is dominated by a few key species. Mussels are commonly known in China, Chile, and Europe. Pacific oyster and other related species are extensively produced in Europe, East Asia, Oceania, and North America, while scallops and clams are found to be significant in China, parts of Europe, and Japan. Crabs are widely produced and exported by Russia, China, Canada, and Indonesia, while countries like Canada and the United States are considered a hub for the highly valuable seafood industry by producing and exporting lobsters. Shrimps, on the other hand, are the single largest shellfish produced globally [2]. Collectively, China is the world’s largest producer of shellfish, accounting for about 40% of the global output [9].

The key attentions for shellfish producers are seasonality and availability. Unlike livestock, many aquatic species have seasonal reproduction or growth patterns that limit their tendency to be harvested. For example, wild oysters in temperate regions spawn in summer months, which traditionally leads to their inadequate availability [10]. Similarly, spawning periods have been designed to prevent overharvest of wild mussels. Aquaculture has greatly leveled out these seasonal variations by modifying the geography and controlling the breeding cycles in hatcheries, resulting in a year-round supply. For example, hatchery-produced oyster spat can be set out in multiple batches such that crops mature in rotation, ensuring a continuous harvest of market-size oysters. Mussel producers maintain their production by putting rope seeding and taking advantage of both hatchery seed and wild spatfall seasons. However, seasonal effects like water temperature and algal blooms sometimes affect the quality and growth rates at different times of the year. Automation offers tools to better manage these variables, such as real-time environmental monitoring to decide optimal harvest windows or to move stocks pre-emptively if conditions deteriorate [11]. Overall, today’s shellfish industry can provide a moderately stable, year-round supply by leveraging precise breeding, but upholding this stability with ongoing environmental change has been a big challenge.

1.2. The Need for Automation in the Shellfish Sector

As shellfish aquaculture moves up to meet global demand, the boundaries of traditional farming approaches have become progressively apparent. This sector has been labeled as “held back by its dependence on obsolete methods and tools”, with many responsibilities conducted physically or with minimal modernization [5]. This lack of automation can lead to high labor costs, operational inadequacies, and increased exposure to risks that threaten sustainability. Several key drivers underline the need for automation in shellfish farming. They include labor intensity and costs, scaling production and economic viability, precision and resource efficiency, quality, traceability, market access, and resilience to environmental change.

Shellfish farming is a labor-intensive process at nearly every stage, starting from seeding young shellfish to regularly cleaning or moving culture gear, to harvesting and sorting the final product. For example, oyster farms using bag culture must flip heavy bags by hand every week or two to ensure uniform growth and prevent biofouling [12]. This repetitive manual work is physically challenging and time-consuming, contributing to high labor costs. Small farms, which dominate much of the industry, often struggle to find enough labor for peak work periods and cannot easily expand production [13]. In regions like the European Union, analyses have linked stagnant or declining shellfish production in part to the prevalence of small, poorly mechanized farms with limited capacity to increase efficiency. Automation can reduce the human workload for tedious or strenuous tasks, lowering labor costs and making the occupation safer and more attractive. For instance, a robotic system that cleans mussel ropes or flips oyster bags automatically can save hundreds of person-hours and reduce the risk of injury from heavy lifting or working in rough weather.

The manual nature of shellfish farming imposes practical limits on production volume and farm size. Automation would allow farmers to manage larger quantities of shellfish and infrastructure without proportional increases in labor. For example, in longline oyster farming, inserting oyster spat onto culture ropes has been a tedious holdup performed by hand, limiting how quickly farmers can deploy new stock [9]. This automated seeding device can significantly reduce costs and time for farm setup. On the other hand, manually managing a shellfish farm often means relying on experience and periodic checks, which can lead to suboptimal decisions, such as overfeeding or missing early signs of disease or water quality stress. Automation, combined with digital monitoring, enables a shift toward precision aquaculture, where inputs and operations are optimized in real-time. Sensors and AI systems can monitor parameters like temperature, salinity, phytoplankton abundance, and oxygen continuously, providing farmers with data-driven insights [14]. For instance, an IoT-based water quality network can alert a farmer to an algal bloom or low-oxygen event developing, allowing timely actions (e.g., lifting oyster baskets out of water or closing farm intake valves) to prevent stock losses [11]. In mussel and oyster farming, precise knowledge of growth rates and condition index can inform exactly when to thin out crowded stock or harvest for best yield, reducing waste. Automation of feeding (in cases like shrimp or abalone farming) can dispense feed in optimal amounts and timing, improving feed conversion and minimizing excess feed waste. Real-time monitoring and automated interventions in aquaculture have been shown to reduce mortalities by up to 40% and increase yields significantly, as sensor-driven systems respond faster and more accurately than human observation alone [6]. By improving precision, automation can help use resources (feed, seed, energy, and space) more efficiently and sustainably.

Modern regulators are placing greater importance on food safety, quality, and traceability. Automated grading and sorting machines can ensure only shellfish of the right size and quality make it to market, with nominal handling stress. Sensor tags and blockchain-based tracking can automatically record the conditions each batch experienced (temperature, location, harvest date), improving traceability from farm to fork. This can expand market access, especially for export, where proof of sustainability and safety is increasingly required.

Finally, the role of climate change poses growing risks to shellfish aquaculture, from marine heatwaves and ocean acidification to more frequent harmful algal blooms and storms. Automation and advanced technology offer adaptive capability to handle these stresses. For example, machine learning models have been developed to predict water quality events that could force shellfish farm closures by analyzing data from sensors, satellite remote sensing, and weather forecasts [11]. Such predictive analytics, integrated into farm management, give farmers early warning to safeguard their crop (harvesting early, moving gear, etc.) before any disaster. In addition, physical automation can allow more adaptive farming practices. For example, automated upwelling systems can flush culture lines with deeper, cooler water during summer thermal stress. Also, robotic submersible cages can be lowered or raised in response to the conditions. While many of these innovations are in early stages, the complexity of environmental challenges is driving the shellfish sector to seek smarter, automated solutions for resilience and sustainability. Within typical small and medium farm constraints, interoperable automation is best staged as modular building blocks where low-power IoT sensing, vision modules, and traceability services are added in steps using open interfaces to limit capital load and preserve future compatibility [15].

2. Automation in Production Stage

The shellfish aquaculture production stage includes activities from breeding and seed to the grow-out of young shellfish into market-ready adults on the farm. Automation in this stage focuses on making hatchery operations efficient, improving the shifting of young shellfish to the farm, and streamlining the monitoring of the growing process, as shown in Table 2.

2.1. Hatchery and Spat Collection Automation

Automation of this sub-stage is still emerging and has significant potential as it ensures uniform feed distribution, reducing waste and labor. Hatchery tasks such as feeding microscopic algae to larvae, grading larvae by size, maintaining precise water quality, and or setting oyster larvae onto substrates could all benefit from using automation. For example, a GPS-guided feeding catamaran can achieve positioning accuracy for feeding. This system minimizes territorial fights among stocks and avoids water pollution from excess feeds. In shrimp culture, traditional cues like ‘kentongan’ have been automated. This is based on an ANN-based sound recognition system that converts the gong’s sound pattern into a feeder command, resulting in automatic feeding [15]. Beyond feed pellets, a sensor-driven top-dressing dispenser was developed in biofloc shrimp farming to introduce probiotics and nutrients based on microbials and real-time water signals. This resulted in improving shrimp immunity and growth, thus enabling sustainability [16]. Some advanced hatcheries have also explored imaging systems to count larvae or monitor the development, improving the accuracy of stocking [17,18].

In the oyster hatchery, once the spat is developed, automating their transfer to the farming system has been a crucial research focus. In oyster longline farming, oyster spats are attached to a spat carrier, which is then fixed on long ropes hung above water for grow-out [19]. To address the tedious process of threading by hand, Yang et al. developed an automated oyster spat insertion device for longline farming systems [9]. This equipment uses conveyors, negative pressure-based suction for pick and place, and robotic bundling to take the strings of spat-covered shells and mechanically attach them to culture ropes at fixed intervals. Preliminary tests were found to be promising as high rates of proper attachments with minimal damage to the spat were observed.

Whether it is oyster or any other shellfish, such as mussels, the aim for spat collection automation is to ensure the transfer of young to the farm is efficient and does not impact production scale. By reducing the labor of stocking farms, producers can easily expand their operations by restocking promptly. In addition, well-spaced feeders can lead to more uniform growth results, as depicted in Figure 1.

2.2. Farm Monitoring and Husbandry Automation

Once the shellfish are in their grow-out phase, farmers face daily challenges in monitoring health and growth, managing the farm environment, and maintaining culture equipment. Traditionally, these tasks require frequent physical presence, which can make the process reactive and inefficient. Precision aquaculture for shellfish has been proactively explored to address these issues [11]. This framework is a combination of sensing technologies and Internet of Things (IoT) networks that allow continuous monitoring of these conditions.

Shellfish aquaculture environment monitoring has been one of the most important applications of automation. An array of sensors with wireless networks and cloud platforms has been used to continuously measure water temperature, pH, dissolved oxygen, salinity, turbidity, and chlorophyll a in shellfish farms [14]. Wireless sensor networks have been deployed in clam and oyster farms that send real-time data to cloud platforms, which is accessible via mobile devices [20]. This results in a dataset that not only informs day-to-day farm management but also feeds into developing predictive models. This can be conducted by analyzing sensor data alongside satellite observations and historical closure records to develop machine learning models. For instance, it can alert if oxygen drops or if a harmful algal bloom might be developing in the aquatic culture, which leads authorities to close the shellfish culture. O’Donncha et al. achieved an 83% accuracy in predicting the toxin-driven shellfish farm closure, assisting farmers to take preventive measures [21]. Tassetti et al. developed an augmented reality system for mussel farms that overlays object detections and sensor data onto a real-time view of the farm. This results in an immersive and synoptic view of the underwater farm, enhancing situational awareness for monitoring tasks [22].

Beyond fixed sensors, highly maneuverable, drone-like, remotely operated underwater vehicles (ROVs) have gained attention among farmers. Cameras and computer vision have continuously contributed to development of the aquaculture monitoring options. Sadrfaridpour et al. developed an affordable BlueROV2 underwater drone with a camera to count and measure oyster on farm leases. Their computer vision algorithm achieved over 80% precision in estimating the oyster sizes [23]. Klahn developed an ‘Oystamaran’, which is a catamaran-style robotic platform that can glide over the oyster lines, and by using computer vision, it can locate the floats, then grab the bag, and then flip it in the water. This robotic system has shown a promising result in avoiding the development of fouling organisms and silt on the submerged side of the bags. In addition, it also aims to cut down on labor and weather exposure involved in the routine flipping of the bags [24]. Marine fouling/corrosion dictates cleaning and seal-replacement intervals; net-cleaners and marine-rated housings extend uptime but must be scheduled [25,26]. Correia et al. developed an image-based monitoring system for tank-raised shrimp that can count the individuals (within 7% error) and measure their length and weight (within 1.5% error). They used a Raspberry Pi-based system that captured the shrimp images during feeding, then a trained segmentation model estimated their numbers and sizes, providing insights into their growth and feed attractiveness [27]. Similarly, innovations like ‘Grain Ocean Roll Bag’, ‘Solar Oyster Production’, and ‘FlipFarm’ have been commercialized, which aim to maximize oyster output while minimizing manual labor [28]. For crab ponds, Cao et al. [29] designed an automated bait-casting machine to uniformly distribute bait for crabs. Soft-shell crab traditionally requires farmers to check each crab every few hours, so that they can be removed promptly when they start shedding their shells. To further address this issue, Pitakphongmetha et al. [30] designed an IoT-based prototype system that uses motion detectors, water quality sensors, and automated feeders to monitor soft-shell crabs. Other mechanized systems that are under development include ‘predator deterrents’ such as acoustic devices to keep sea ducks away from shellfish farms and ‘automated cleaners’ like robotic brushes or ultrasound devices to remove biofouling [31].

With all these developments, it is worth noting that integrating AI with all the ground data (generated by the automated systems) and generating a user-friendly software platform for farmers can assist in actionable insights. Using cloud computing, AI might analyze growth curves and possibly direct whether a farm needs splitting or detachment. Pilot data indicate measurable advantages over manual routines: sensor-driven control reduced mortality and feed or energy inputs in controlled trials, vision-guided grading increased unit output and lowered labor hours per thousand items, and automated hatchery or feeding systems decreased power per biomass through tighter set-point control [32]. Although human judgment remains vital, these systems can augment the decision-making time to manage large and complex farms efficiently, as shown in Figure 2 and Table 2.

3. Automation in the Harvest Stage

Harvesting shellfish is a labor-intensive process where timely execution is crucial for quality and operational efficiency. This stage includes netting, lifting cages, initial handling such as cleaning and temperature control, sorting, and slaughtering. Automation has long been used in harvesting to some extent. For example, large mussel farms use deck machinery to strip mussels from ropes, but new technologies are streamlining these processes with minimal stress while maintaining product quality and traceability. Cross-system integration is stabilized by message-oriented middleware and materials choices suitable for marine exposure, plus anti-fouling maintenance that extends sensor service life under wave, corrosion, and salinity stress [32]. Planned brush or ultrasound cleaning and gasket inspections are required to preserve line-speed under wave and turbidity stress [25,32].

For bivalve shellfish in suspended culture (longlines, rafts, etc.), one fundamental harvest task is lifting heavy cages, baskets, or ropes out of the water. Traditional methods involve manual labor or basic winch systems to hoist product-laden gear onto boats or docks [31]. Today, many farms use mechanized lifting equipment such as hydraulic cranes, automated winches, and conveyor belts to make this safer and faster. On mussel barges, for instance, a typical setup includes a hydraulic winch that continuously pulls a mussel longline rope through a mechanized stripper, which knocks the mussels off into a collection hopper, while jets spray off debris [32]. Modern versions of these harvest machines can be semi-automated in such a way that once a line end is fed in, the machine does the rest, requiring only oversight. Some farms have further automated by using programmable logic controllers (PLCs) to control the speed of hauling and the operation of multiple cleaning steps (stripping, washing, de-clumping) in sequence. This reduces the number of crew needed on a harvest vessel and ensures a more consistent process, minimizing shell damage and meat loss.

In oyster farming, harvesting methods vary by culture technique [33]. For oysters grown in cages or baskets, one emerging approach is the use of amphibious or floating harvest robots/vehicles. Concepts have been proposed (and prototypes tested) where a robotic barge or platform can navigate along an oyster farm, using machine vision to identify which cages or bags are ready for harvest (based on location data or even visual assessment of oyster size) and then mechanically lifting those out of the water [34]. A prototype in development in the U.S. mid-Atlantic region involves a system of two coordinated robots: one underwater to scan and map oyster growth, and another at the surface capable of lifting and transferring cages to a boat [35]. Although still experimental, these ideas showcase how automation might take over complex tasks like selective harvesting in the future. In the meantime, many oyster farms have adopted simpler mechanization. For example, some use tumble graders on site after oysters are lifted in baskets as they can be fed into a rotating cylindrical grader (with holes of certain sizes) that automatically sorts the oysters by size, so that only market-size oysters are separated for sale while smaller ones go back into a grow-out container. Newer grading machines use vision technology to sort oysters by size and shape even more precisely. An automated vision-based oyster sorting system was recently reported to classify oysters by size at a rate of several oysters per second, offering a potential replacement for slow manual sorting [36]. Integrating such sorters at harvest time can greatly speed up the post-harvest handling process, as shown in Figure 3.

Another crucial aspect at harvest is maintaining the cold chain and food safety. As soon as shellfish come out of the water, especially in warm climates, they need to be cooled to prevent microbial growth (for oysters, regulations often mandate transporting them to refrigeration within a certain number of hours). Automation helps here by monitoring temperatures and controlling cooling systems. Some farms employ refrigerated seawater tanks into which oysters are placed immediately after harvest. These tanks can have automated chillers and circulation, maintaining a constant sub-10 °C environment. Sensors in these systems log the temperature and can alert if it deviates, ensuring compliance with safety rules. Additionally, automated tagging systems can label batches with time and temperature data, providing traceability. While not “harvesting” per se, these are automated extensions of the harvest process that ensure sustainability in terms of food safety (Refer to Table 2 and Table 3).

4. Automation in the Processing Stage

4.1. Automated Grading Systems

After harvesting, followed by sorting, the bivalves and crustaceans are graded depending on the shape, size, and color right before they enter the depuration or purification line. The deployment of automation in grading systems reduces labor costs, manual errors, optimizes time and wastage, and enhances operational efficiency. Several studies have been performed to date by utilizing artificial intelligence and machine learning to detect, segment, and classify the shellfish by taking notable features into consideration. One of them includes the application of machine learning methodologies to classify the oysters and grade them based on their external morphological characteristics, achieved using object detection and classification frameworks like YOLOv-9 segmentation and Support Vector Machine (SVM), paving the way for the automatic oyster grading systems [37]. For commercial readiness, evaluations should report accuracy, precision, recall, and inference latency with cross-species testing and k-fold validation under varied illumination and turbidity, and deploy compact models or hardware optimization to meet line speed with constrained compute [37].

In crustaceans, just like lobsters, the Chinese mitten crab (Eriocheir sinensis) is a highly valued product because of its uniqueness and special characteristics like high protein content, tenderness, a rich source of vitamins, and desirable taste. To address the rapid increase in demand, an efficient grading system is needed to deliver a product with good quality, which drove interest to employ computer vision techniques along with deep learning algorithms for sex identification, carapace dimensions, and fatness ratio to classify the crabs for their quality by YOLOv5 and YOLOv8 segmentation, resulting in 100% accuracy in sex identification and 0.995 mAP for carapace identification [38]. Tran et al. (2023) employed the spectrometric analysis techniques to grade the mud crabs (Scylla paramamosain) based on their internal quality, coupled with machine learning for the development of a non-destructive automatic grading system, where the crabs were analyzed at various near-infrared wavelengths with respect to shell, ovary, and meat (940, 760, and 680 nm) [39]. While automation has shown clear technical gains, its adoption by small and medium enterprises remains constrained by capital costs and scalability limits. Prototype robotic or sensor modules reported in the literature generally range between USD 5000 and 15,000, depending on functionality, with estimated payback periods of two to four years when labor savings and yield improvements are considered [40]. Economic analyses further indicate that modular system designs and shared-service or leasing models can substantially reduce initial investment barriers for SMEs [21,22]. Including such considerations is essential for evaluating automation not only as a technological innovation but also as a financially sustainable pathway for the aquaculture sector.

4.2. Automated Depuration and Purification

Automation in depuration systems of bivalves and purification systems of both bivalves and crustaceans makes the process less cumbersome by controlling parameters in the cleansing of accumulated impurities, contaminants, and pathogens after harvesting. Traditional depuration practices provide variable efficacy in removing bacteria, viruses, and anthropogenic particles from the bivalves [41,42]. To address and regulate the persistent heavy metal contamination in green mussels (Perna viridis), native to the Asia-Pacific region, an automated depuration system was introduced to optimize the efficiency, which involved the integration of an ESP-32 microcontroller for the effective monitoring of water quality parameters like salinity, pH, TDS, and temperature by coupling with EMA (Exponential Moving Average) and LPF (Low Pass Filter) for accelerated reading stability, resulting in higher accuracy in the operational parameters with low error percentage, aiding in higher survival rates [42].

4.3. Image and Sensor-Based Technologies for Quality

Traditional chemical and sensory evaluation of shellfish quality is time-consuming, and real-time monitoring of the internal defects is necessary, which cannot be achieved through the conventional methods, as it requires a destructive sampling procedure, becoming a major concern to the shellfish that are consumed or sold alive, often leading to product wastage. To address these concerns, non-destructive methods like imaging and spectroscopy can be employed as they offer the results in real-time with high precision and accuracy, with imaging being more advantageous than spectroscopy. The presence of a parasite called polychete worms, responsible for causing mud blisters that destroy oysters, was studied using X-ray Computed Tomography (X-ray CT) and Magnetic Resonance Imaging (MRI). The application of CT and MRI resulted in the identification of lesions and their severity, which can prevent contamination with other oysters either during transportation or storage [43]. Apart from parasites, the anthropogenic particles like microplastics also degrade the quality of bivalves like mussels, and also have the potential to cause illness to human beings if consumed. The cruciality of understanding and identifying the localization of ingested microplastics in mussels paved the way for the development of volumetric Raman imaging-based internal analysis, achieved with a 532 nm excitation laser and a CCD detector in which the obtained images were classified and analyzed with the help of standard normal variate (SNV), principal component analysis (PCA), and Asymmetric Least Squares (ALS), offering the detailed information on the presence of microplastics inside the organs of mussels [44].

Moving to the crustaceans, the imaging techniques were widely employed in the identification of freshness, internal, and external quality of fresh and frozen shrimp, crabs, and lobster at various processing stages. For instance, to ensure the shrimp quality during the drying process, the moisture content, color, and texture were monitored using hyperspectral imaging, where images of 104 samples of shrimps at different drying levels were categorized and analyzed to develop an optimal model for moisture content, L*, a*, b*, elasticity, and hardness by using Pearson correlation analysis, partial least squares regression, and least square support vector machine (LSSVM), performed with residual predictive deviation (RPD) of 2.814, 3.292, 2.753, 3.211, 2.842, and 2.807, respectively [45]; similarly the freshness discrimination of the frozen shrimp was carried out using multidimensional fluorescence imaging by equipping CCD camera and Xenon light of 380–610 nm wavelength as an excitation light source, achieving predication accuracy of 0.80 and 0.53 for K-value and pH [46]. The given applications as evidence, internal imaging techniques like photoacoustic imaging, soft X-ray imaging, ultrasound imaging, NIR hyperspectral imaging, terahertz imaging, Magnetic resonance imaging (MRI), and thermal imaging have a wide potential in assessing the internal quality while the external quality can be observed by using digital imaging, biospeckle imaging, fluorescence imaging, etc., in all variants of shellfish, including bivalves and crustaceans.

In addition to the imaging processing, sensor-based technologies also have the potential to serve as a robust tool for detecting both internal and external quality in shellfish across various processing stages from the reception to the storage. The traditional characterization of volatile organic compounds (VOC), an indicator of freshness in oysters, is time-consuming, requires analytical instruments with high cost, and is often destructive. To achieve the non-destructive analysis of the presence of VOCs, an integrated system was introduced, containing a colorimetric sensor array (CSA) through the utilization of color sensitive dyes which are sensitive to the VOCs present in the oyster, a CCD camera to obtain the images and a visible near-infrared spectroscopy (VNIRS) through the reflected spectral data, aimed to determine the freshness of oysters in a non-destructive way, facilitating in the automatic classification of healthy and stale oysters [47]. The real monitoring of perishability and the shelf-life index of the shrimps for meeting the export standards is a major concern that resulted in the development of an electronic nose (e-nose), primarily designed for shrimps (shrimp-nose), which contains 6 metal oxide-based gas sensors sensitive to the freshness indicators, investigating the quality changes to address the perishability by achieving 96.29% and 95.73% accuracy in un-iced and iced shrimps [48]. Similarly, the crabs were also tested for their freshness and quality using an e-nose containing 4 MQ sensors sensitive to the volatile compounds like ammonia, ethanol, methane, and carbon dioxide, which crabs often release, and the resultant gas sensors achieved 98% accuracy in evaluating the freshness in crabs through K-NN (K-Nearest Neighbor) methods, accelerating the development of an automated system by saving time and increasing efficiency [49]. In addition, these sensor-based approaches can be further strengthened through multimodal data fusion, which integrates outputs from multiple sensing modalities such as colorimetric sensor arrays (CSA), visible–near-infrared spectroscopy (VNIRS), and e-nose systems with machine learning algorithms. Fusion can occur at the data, feature, or decision level, enabling comprehensive utilization of visual, spectral, and chemical information for enhanced freshness evaluation and spoilage detection. When combined with AI methods, fusion methods such as K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and deep learning-based classification models will improve prediction accuracy, reduce noise sensitivity, and support real-time automated decision-making in shellfish quality monitoring [49].

4.4. Robotic and Intelligent Handling Systems

Robotic and intelligent handling systems in shellfish processing are making the industry more advanced, enabling automation in various labor-intensive and unsafe processing stages that are often performed using conventional methods, including shucking, deshelling, sorting, grading, and packaging. One of the most recent studies includes the development of an automation system integrated with a vision-based algorithm and a delta robot with a soft gripper for the size classification of washed oysters by utilizing image processing techniques followed by the placement of classified oysters in their designated line with the help of a parallel robot, performing with higher precision and consistency than manual operations [36]. For the bivalves, like scallops, a Coboshell robot was introduced to perform automatic shell removal operations by integrating an AI-coupled vision system and a collaborative robot (cobot), aiding in the determination of the opening’s shape and width, and the entry point of the nut muscle, leveraging automation and shell detection imaging, resulting in the separation of meat from the shell without destructing the property of the muscle [50].

Moving on to the crustaceans, the investigation into the weighing discrepancies in the shrimp exports paved the way for the introduction of an automated in-line weighing system that reduces manual errors, either by over-filling or under-filling the bags, enhancing the operational efficiency and packaging accuracy by ensuring the consistency of the export packaging standards [51]. For crabs (blue crabs), the dependency on manual labor for the meat-picking process drove an interest in the exploration of automated meat-picking that involves the detection of crab back-fin knuckle for the identification of cutline in the XY plane by utilizing the CNN model for image processing, achieving 97.67% accuracy to support autonomous crab meat picking. The knuckle detection algorithm, achieved using a dual imaging system, which includes RGB color imaging and 3D laser imaging, was further utilized to develop an intelligent cutting and meat-picking system by understanding the morphology of the Atlantic blue crabs that involved the removal of fin chamber meat cartilage and meat picking of lump meat and chamber meat with the help of spoon end and comb like structures to brush-off the meat and mimic the hand picking process with the pixel classification accuracy of 0.9843. Across all the shellfish, intelligent systems often integrate vision, force, or tactile sensors, machine learning algorithms, robotic arms, and grippers to help reduce the labor force by offering repeatability and ensuring compliance with the hygiene practices [36,52].

5. Automation in Traceability, Logistics, and Transportation

5.1. Shellfish Supply Chain and Traceability

Food traceability is the ability to track the movement of a food product, either in a fresh or processed form, and its associated attributes from the stages of production, processing, to distribution by addressing the product’s forward and backward movement throughout the supply chain [53]. In the case of shellfish, the traceability is consequential due to their unique biological characteristics and vulnerability to parasites and the diseases caused by them, as shellfish like bivalves, including oysters, mussels, clams, and scallops, feed through filtration. These filter feeders can pose a significant threat to public health if the contaminated batch reaches a consumer, as they can bioaccumulate microbial pathogens, biotoxins, chemical contaminants, and heavy metals [54]. In general, the shellfish supply chain is very complex due to the high globalization of shellfish in the global market, ranging from harvest to consumption. Starting with harvesting, it typically occurs in aquaculture farms, inland waters, or seawater (wild caught), followed by a cleaning process called depuration or relaying process, where the shellfish are freed from the impurities, reducing the microbial loads. Later, the cleaned shellfish are transported to the processor, where they are processed through grading, shucking, cleaning, and packaging under controlled conditions by following the hygiene practices [55]. After processing, the finished products begin their movement through wholesalers and distributors, reaching retail outlets and food service operators, before ultimately reaching consumers. It is mandated that each stage should be documented and tracked by providing information about origin, handling conditions, storage, and transportation parameters, ensuring the traceability and meeting domestic and international regulatory standards [56].

Traditionally, the shellfish are traced based on paper-based documentation, lot coding, barcoding, and centralized databases, providing transparency over the traceability system. Despite their benefits, the persistent reliance on the traditional traceability system poses significant challenges as it can be easily tampered with, resulting in false results and intentional fraud, prone to errors and delays, as manual entry often results in data inaccuracies [57]. Paper records are often incomplete or inconsistent, making it difficult to achieve real-time traceability or execute timely recalls in the event of contamination. Moreover, traceback investigations during foodborne outbreaks are delayed, leading to economic losses and health risks. The global nature of the shellfish trade further complicates traceability, as inconsistent documentation standards and fragmented supply chain practices undermine transparency. The challenges associated with these traditional methods are further amplified in the case of shellfish, where perishability, susceptibility to contamination, and cross-border trade demand a higher standard of monitoring and verification. Mislabeling of species or origin, data manipulation, and delays in communication across different factors compromise both food safety and authenticity [58]. Additionally, fragmented information systems across harvesting, processing, and retail nodes limit interoperability, highlighting the need for advanced, technology-driven solutions to address the traceability gap in shellfish supply chains. End-to-end assurance emerges when QR gives consumer access, RFID secures machine-level events, and IoT streams contextual conditions into structured ledgers that regulators and buyers can verify at each node [57]. The integration of RFID, blockchain, QR codes, and IoT in shellfish traceability enables automated, transparent, and real-time monitoring across the supply chain, overcoming traditional challenges, ensuring regulatory compliance, enhancing food safety, and fostering consumer trust in seafood authenticity and sustainability, as shown in Figure 4, Figure 5 and Figure 6 and Table 1 and Table 2 [57].

Table 1. Overview of applications, advantages, and disadvantages of the technology used in the shell supply chain, traceability, transportation, and logistics.

S.No.	Technology	Role	Main Applications in Shellfish	Advantages	Disadvantages	Authors
1.	Blockchain	Distributed ledger ensuring traceability and data integrity	1. Verification of oyster and mussels 2. Fraud prevention in lobster and crab exports 3. Evaluation of frozen shellfish quality 4. Traceability of shrimps	1. Aids in traceability 2. Builds trust in consumers	1. High energy demand 2. Adoptable in small-scale	[52,57,58,59,60,61]
2.	RFID tags	Radio-frequency chips for real-time tracking	Monitoring oyster crates, mussel sacks, and lobster consignments in the cold chain	1. Accuracy 2. Automated tracking 3. Reduces manual errors	1. Fail in saline and wet conditions 2. Higher cost	[54]
3.	QR codes	Scannable labels for product information	1. Consumer accessible data on shrimp and prawn handling 2. Oyster freshness	1. Low cost 2. Easy to adopt 3. Linkable to blockchain	1. Static in nature 2. Prone to tampering 3. Doesn’t monitor real-time 4. Prone to wet conditions	[61]
4.	IoT	Networked sensors transmitting environmental data	1. pH, DO (mg/L), and CO2-based sensors in prawn tanks 2. Salinity (PSU) and temperature (°C) loggers for lobsters 3. Real-time monitoring in crab farms 4. Water quality monitoring for crab larvae	Continuous real-time monitoring	1. Internet connectivity issues in remote areas 2. Battery life	[61]
5.	Cloud computing	Centralized storage and analytics for logistics	1. Shellfish distribution dashboards 2. Real-time alerts for mussel desiccation 3. Crab overheating monitoring	1. Scalability 2. Integrative 3. Remotely accessible	1. Data security concerns 2. Internet dependency	[61]
6.	Artificial Intelligence	Algorithms for pattern recognition and prediction	1. Survival prediction in shrimp overcrowding 2. Loster stress recognition 3. Automated breeding system for crabs	1. High prediction accuracy 2. Decision automation	1. Requires large datasets 2. Training complexity	[62]
7.	Big data	Large-scale collection and processing of logistics data	1. Market demand forecasting for oysters 2. Mortality analysis in crayfish transportation	Supports optimization of supply chains	Data heterogeneity and integration challenges	[59]
8.	GPS	Satellite-based geolocation	1. Route optimization for long-distance lobsters and crab exports 2. Live location tracking of the mussel	1. Enhances logistics transparency 2. Reduces delays	Signal dropouts in containers or ports	[62]
9.	NFC	Short-range wireless for simulation	Consumer-level shellfish freshness validation at retail points	1. Easy customer interaction 2. Secure data transfer	1. Very short range 2. Requires NFC-enabled devices	[62]
10.	Digital Twin	Virtual model of logistics system for simulation	Simulation of the oyster and mussel cold chain to predict survival under different routes	Aids in proactive risk management	1. High implementation costs 2. Expertise required	[59]
11.	Biosensors	Analytical sensors for biological signals	1. Mortality and stress detection in shrimps 2. Ammonia buildup in dense prawn consignments	1. Non-destructive 2. Real-time monitoring 3. High sensitivity	Calibration and Fouling issues in seawater	[63]
12.	Smart Packaging	Packaging embedded with sensors or indicators	1. Colorimetric freshness labels for oysters 2. Humidity control liners for mussels	1. Enhances consumer trust 2. Reduces waste	1. Additional cost 2. Disposable challenges	[64,65]

Table 1. Overview of applications, advantages, and disadvantages of the technology used in the shell supply chain, traceability, transportation, and logistics.

S.No.	Technology	Role	Main Applications in Shellfish	Advantages	Disadvantages	Authors
1.	Blockchain	Distributed ledger ensuring traceability and data integrity	1. Verification of oyster and mussels 2. Fraud prevention in lobster and crab exports 3. Evaluation of frozen shellfish quality 4. Traceability of shrimps	1. Aids in traceability 2. Builds trust in consumers	1. High energy demand 2. Adoptable in small-scale	[52,57,58,59,60,61]
2.	RFID tags	Radio-frequency chips for real-time tracking	Monitoring oyster crates, mussel sacks, and lobster consignments in the cold chain	1. Accuracy 2. Automated tracking 3. Reduces manual errors	1. Fail in saline and wet conditions 2. Higher cost	[54]
3.	QR codes	Scannable labels for product information	1. Consumer accessible data on shrimp and prawn handling 2. Oyster freshness	1. Low cost 2. Easy to adopt 3. Linkable to blockchain	1. Static in nature 2. Prone to tampering 3. Doesn’t monitor real-time 4. Prone to wet conditions	[61]
4.	IoT	Networked sensors transmitting environmental data	1. pH, DO (mg/L), and CO2-based sensors in prawn tanks 2. Salinity (PSU) and temperature (°C) loggers for lobsters 3. Real-time monitoring in crab farms 4. Water quality monitoring for crab larvae	Continuous real-time monitoring	1. Internet connectivity issues in remote areas 2. Battery life	[61]
5.	Cloud computing	Centralized storage and analytics for logistics	1. Shellfish distribution dashboards 2. Real-time alerts for mussel desiccation 3. Crab overheating monitoring	1. Scalability 2. Integrative 3. Remotely accessible	1. Data security concerns 2. Internet dependency	[61]
6.	Artificial Intelligence	Algorithms for pattern recognition and prediction	1. Survival prediction in shrimp overcrowding 2. Loster stress recognition 3. Automated breeding system for crabs	1. High prediction accuracy 2. Decision automation	1. Requires large datasets 2. Training complexity	[62]
7.	Big data	Large-scale collection and processing of logistics data	1. Market demand forecasting for oysters 2. Mortality analysis in crayfish transportation	Supports optimization of supply chains	Data heterogeneity and integration challenges	[59]
8.	GPS	Satellite-based geolocation	1. Route optimization for long-distance lobsters and crab exports 2. Live location tracking of the mussel	1. Enhances logistics transparency 2. Reduces delays	Signal dropouts in containers or ports	[62]
9.	NFC	Short-range wireless for simulation	Consumer-level shellfish freshness validation at retail points	1. Easy customer interaction 2. Secure data transfer	1. Very short range 2. Requires NFC-enabled devices	[62]
10.	Digital Twin	Virtual model of logistics system for simulation	Simulation of the oyster and mussel cold chain to predict survival under different routes	Aids in proactive risk management	1. High implementation costs 2. Expertise required	[59]
11.	Biosensors	Analytical sensors for biological signals	1. Mortality and stress detection in shrimps 2. Ammonia buildup in dense prawn consignments	1. Non-destructive 2. Real-time monitoring 3. High sensitivity	Calibration and Fouling issues in seawater	[63]
12.	Smart Packaging	Packaging embedded with sensors or indicators	1. Colorimetric freshness labels for oysters 2. Humidity control liners for mussels	1. Enhances consumer trust 2. Reduces waste	1. Additional cost 2. Disposable challenges	[64,65]

5.2. Advanced Automation Methods in Shellfish Traceability

5.2.1. QR Code

QR code traceability is a digital system where unique QR codes are assigned to shellfish products, allowing stakeholders and consumers to access detailed information on harvest, processing, and distribution by simply scanning the code with a smartphone. It enhances transparency, authenticity, and consumer trust in the supply chain, as shown in Figure 6 [65]. QR-code–based traceability in shellfish supply chains connects unit-level or lot-level identifiers to cloud records spanning harvest, post-harvest handling, processing, logistics, and retail, with early architectures already combining on-pack QR with cryptographic signing to secure data integrity. In oysters, pilot implementations replaced paper tags digitally through QR codes, transparent to consumers, improving recall precision and provenance visibility across various stages like harvesting, depuration, or relaying process, packing, and distribution. Among other bivalves (e.g., mussels, clams, scallops), IoT-to-QR frameworks demonstrate “sea-to-fork” transparency by streaming temperature and location from harvest vessels and distribution to consumer-readable product pages, strengthening cold-chain verification and engagement [65], while market studies show QR as the primary interface through which end users retrieve traceability and sustainability attributes that influence choice [66].

For shrimp and prawns, enterprise systems have mapped pond stocking, feed and chemical use, harvest lots, freezing, and export documentation to QR pages, with user studies reporting positive proven results for frozen shrimp QR disclosures and digital ecosystems formalizing event capture for cold-chain nodes [67]. Grower-oriented mobile apps were developed for Pacific white shrimp and giant freshwater prawn, further illustrating how pond and health records can feed directly into QR-accessible ledgers at processing and retail [67]. In crabs and lobsters, where individualization is challenging, biometric identification (carapace pattern recognition for Chinese mitten crab; image-based grading and ID for Southern Rock Lobster) can generate unique digital identities that anchor QR codes carrying vessel, trap location, grading, live-storage, and export events; moreover, long-term video and behavioral tracking highlight the feasibility of linking analytics to traceability records surfaced through QR at later stages [68]. Finally, to address species substitution and mislabeling risks documented in crab and seafood markets, DNA-assisted schemes now embed authenticated species results like DNA barcodes or hashed certificates behind QR codes to couple authenticity testing with logistics history, extending beyond simple URL redirects to verifiable evidence objects [69]. Collectively, across oysters, mussels, and other bivalves, shrimp, prawns, lobsters, and crabs, QR codes function as the access layer to standardized, event-based traceability records, often enriched by IoT and, increasingly, by biometric or molecular proofs, thereby operationalizing end-to-end visibility without altering existing packaging formats [70].

5.2.2. RFID Technology

Radio-Frequency Identification (RFID) in shellfish traceability leverages tagged units (passive, semi-active, or active tags) and reader infrastructure to automate identity capture and event logging across harvest, depuration, processing, cold-chain logistics, and retail, thereby reducing manual entry errors and increasing temporal resolution of the records [71]. Unlike barcode systems that require line-of-sight, RFID enables bulk reading in wet and cold environments, aids in monitoring of shellfish handling, and can be combined with embedded sensors or with flexible printed electronics to capture continuous or semi-continuous quality data without altering existing packaging workflows [72]. Reliability in wet saline logistics improves with encapsulated tags, reader diversity, and periodic calibration schedules, while redundant data logging mitigates range loss and intermittent connectivity [72].

In oyster supply chains, RFID has been trialed to secure harvest tags, remove theft, and streamline depuration and relabel workflows; field pilots and producer reports indicate that boat-mounted or dock-mounted readers can capture harvest identity at landing and automatically register subsequent depuration, grading and repack events, producing a continuous event trail from the harvest area to reseller and reducing traceback times in contamination events [40]. Studies on drone-mounted or vessel-mounted antennas demonstrate operational feasibility for scanning beds or farm gear, an approach that is particularly useful for dispersed oysters where manual tagging and retrieval are labor-intensive [40]. Practical implementations emphasize the need for robust encapsulation of tags and reader placement strategies to overcome RF attenuation in saline, wet, and metallic environments common to oyster harvest and processing facilities, outlined in Figure 5 [40]. Freshwater and marine mussel applications use RFID for both ecological monitoring and product traceability. In dynamic flow regimes where sediment stability and hydraulic forces influence habitat and handling, antenna design and mobile scanning platforms are essential for reliable capture of mussel identity and location events [68].

For crustaceans such as shrimp and prawns, RFID is used primarily at lot and container levels rather than on individuals, mapping pond and cage identifiers, harvest lots, onboard chilling and freezing events, and subsequent cold-chain checkpoints through processing and export documentation. Grower-facing mobile apps and farm management systems can write pond health, stocking, and treatment records to a tag or linked database at harvest, enabling downstream processors and importers to verify upstream husbandry practices [65,73]. Behavioral analytics can trigger event records like abnormal mortality and handling stress that are then anchored to RFID-identified batches for regulatory reporting and buyer assurance [70]. High-value species such as lobsters and commercially important crab species pose distinct identification challenges because specificity is often required for grading, claims, and anti-fraud measures. Image-based biometric methods and transponder marking have been used to create unique identities that can be associated with RFID or RFID-equivalent markers [40]. Field trials tagging gear (pots, gillnets) with RFID have also been demonstrated to protect fishing assets and simultaneously create an auditable link between catch events through gear ID, GPS, time, and landed lots, information that can be transferred into trader or exporter ledgers to improve traceability continuity [74]. Recent advances address some constraints through flexible printed tags that improve mechanical conformity to wet packaging, chipless, RFID variants that reduce per-unit cost for low-margin seafood, and integrated RFID–IoT gateways that permit real-time sensor fusion (temperature, GPS) with identity events for richer quality datasets [75].

5.2.3. Blockchain

Blockchain is a distributed ledger technology that records time-stamped, cryptographically linked events across an authorized or unauthorized network; when applied to shellfish traceability, it functions as an append-only, proven layer that secures major events and associated metadata while enabling auditable queries by regulators, buyers, and consumers, as depicted in Figure 4 [72,76]. In pragmatic deployments, the blockchain is rarely a stand-alone solution, being an integrity and coordination layer that must be fed by reliable field inputs (RFID, QR, IoT sensors, image, and DNA authentication) [76]. The literature emphasizes that blockchain reduces opportunities for post hoc record tampering and enables conditional logic (smart contracts) for automated actions (release, payment, alerting) but does not eliminate the need for tamper-resistant sensing and robust business processes at each supply-chain node [72,76].

For shrimp and other penaeid crustaceans, blockchain frameworks have been prototyped to support export controls, sanitary certificates, and buyer verification by creating immutable shipment records that link farm production logs, harvest lots, cold-chain logistics, and export documentation into a single traceable object (ShrimpChain). These systems can reduce documentation issues at packing and inspection, facilitating compliance with importing-country sanitary and phytosanitary checks, and provide buyers with verifiable origin and handling data that can increase market value [76].

In bivalve supply chains, blockchain focuses on securing harvest-area authentication, depuration records, and laboratory results tied to E. coli and biotoxin monitoring. Because regulatory actions depend on temporally precise water and toxin data, blockchain entries that reference certified laboratory certificates and time-stamped sensors enable regulators and buyers to rapidly verify whether a lot passed required controls before release [76]. Oyster pilots have shown that an immutable event graph reduces traceback time and clarifies responsibility within complex repacking operations [77,78], and recent algorithmic work proposes optimized data structures and search methods to make fine-grained queries efficient on such graphs [79]. For mussels, coupling remote sensor arrays and edge aggregation with on-chain summaries permits continuous monitoring of production conditions while preserving the privacy of raw sensor streams [80].

For high-value crustaceans that require individual or near-individual assessment, blockchain solutions have been combined with biometric or image-based identity systems to anchor unique identifiers to animals or catch events [81]. Live-lobster pilots that record catch GPS, trap or gear ID, grading, and live-storage conditions on a ledger show that immutable histories facilitate premium market claims and rapid quarantine decisions when health issues arise; algorithmic approaches (e.g., cuckoo-filter indexing) have been proposed to reduce on-chain storage and accelerate lookups while preserving fidelity of live-supply records [81,82]. Recent technical work on lightweight blockchain routing and secure sensor networks aims to lower the cost and energy requirements for sensor-to-ledger pipelines, improving feasibility for distributed aquaculture contexts [81,82]. The equipment-as-a-service and analytical subscription models are viable where connectivity and service networks exist and can be adapted through cooperative cost sharing and open-interface clauses so that coexisting vendor platforms retain explainability and interoperability across the value chain [82]. These subscription-based approaches support cooperative cost sharing among farms and processing facilities, reducing the financial barriers for small and medium enterprises. Furthermore, interoperability between multi-vendor AI and IoT systems is maintained through open-interface clauses and standardized data exchange protocols, allowing seamless communication across platforms and preserving system compatibility over time [76,80,81,82]. Technical advances such as lightweight blockchain routing and secure sensor networks have also reduced the energy and maintenance costs of sensor-to-ledger communication, strengthening the long-term economic and operational viability of subscription-driven automation in the shellfish sector [81,82].

5.2.4. IoT

The Internet of Things (IoT) in shellfish supply chains denotes networks of connected sensors, actuators, gateways, and cloud services that continuously sense, aggregate, and transmit environmental and logistical data like temperature, dissolved oxygen, salinity, pH, GPS, shock, and door-open events across production, post-harvest handling, cold chain, and retail nodes. Precision-aquaculture pilots and system descriptions reveal that IoT implementations typically combine low-power wide-area networks like LoRaWAN, buoy-mounted or cage-mounted sensor arrays, edge microcontrollers for local data filtering, and cloud platforms for storage, analytics, and dashboarding, shown to support real-time decision-making and regulatory reporting in shellfish [82]. Beyond sensor deployment, the IoT-based aquaculture systems primarily rely on reliable communication protocols and scalable data transfer architectures. In marine and coastal farms, long-range, low-power communication standards such as LoRaWAN and NB-IoT are widely employed for transmitting water quality parameters such as temperature, pH, dissolved oxygen, and salinity, and they also act as a central gateway system [19]. Short-range modules like Bluetooth Low Energy (BLE) and ZigBee support communication for the sensors to the node in submerged and raft-based networks. Edge aggregation units like Raspberry Pi 4 and STM32 are often used as gateways to transmit compressed data via LTE, 4G, 5G uplinks to cloud dashboards [9]. LoRaWAN offers high range and low power consumption but limited bandwidth, whereas NB-IoT provides enhanced reliability for dense sensor networks. These architectures enable continuous environmental monitoring and anomaly detection while minimizing energy demand in offshore environments [10,20].

For oysters and other bivalves, IoT systems have been applied to continuous environmental monitoring at the harvest site, to control depuration tanks, and to monitor the cold chain during transport and retail. Field studies and pilot projects demonstrate that farm-level analysis can detect environmental conditions that correlate with increased norovirus and biotoxin risk, enabling preemptive area closures or targeted testing and thereby improving regulatory compliance [82]. Mussel farming leverages IoT for growth monitoring and farm management, with recent precision-aquaculture frameworks employing buoyancy detection, edge-AI for fouling and stock detection [82,83]. Edge-AI approach using YOLO variants reduces uplink bandwidth and enables real-time defect detection at the farm where marine connectivity is intermittent [82,83,84,85].

In shrimp, prawn, and other crustacean aquaculture, IoT applications focus on pond-level water quality monitoring through the assessment of DO, ammonia, and temperature, automated feeders, and predictive analytics for disease and feed optimization. Several studies and pilots show that continuous IoT monitoring combined with machine-learning models enhances farm-level biosecurity and can reduce mortality and feed waste, data that, when recorded to a farm management system, improves the credibility of harvest declarations for buyers and regulators [83,84,85]. For lobsters, crabs, and other higher-value crustaceans, IoT concentrates on welfare and live-storage monitoring through automated feeders, mounting detection, water quality control in grow-out and holding facilities, as well as gear tracking for catching. When implemented with attention to engineering constraints and equitable business models, IoT provides the operational backbone that turns discrete identity tokens (QR, RFID, biometrics) into continuous, verifiable production, strengthening shellfish traceability and regulatory compliance. Modular rollouts with shared gateways or leasing curb CAPEX/OPEX while fitting battery, calibration, and corrosion maintenance into normal farm rounds [20,26,82].

5.3. Automated Environmental Monitoring and Control in Transportation

Most of the shellfish are transported alive, which makes them vulnerable to the environmental conditions, and automation becomes essential for maintaining integrity and quality, thereby reducing mortality rates. The most crucial parameters include temperature, pH, DO, salinity, RH, and CO₂ accumulation, along with ammonia and traces of lactic acid because of the physical stress during handling, varying from species to species. Application of automated monitoring through embedded sensors, RFID tags, wireless sensor networks, and smart labels integrated with cloud-based dashboards allows for real-time feedback, controls, and adjustments in the vital parameters by providing aeration, increasing, or reducing the temperature to prevent economic losses caused by mortality. Although sensors and enclosures add energy or materials, mortality and spoilage avoidance from real-time control and alerts typically outweigh this footprint at the shipment scale [25,62,86].

To begin with, mussels survive in low temperatures, high humidity, and minimal exposure to shocks. Commercially, the mussels are stored in polypropylene boxes, equipped with temperature and humidity loggers, along with time temperature indicators and pH-sensitive labels, aiding in the monitoring of metabolic activity, microbial contamination through spoilage gas detection (NH₃), and the acidity during distribution, avoiding mortality rates caused by CO₂ accumulation and temperature rise during extended storage and transportation [13,86,87]. Oysters are often transported for live shellfish markets, requiring proper cooling, pH, and humidity, which can be achieved through smart RFID-enabled cold chain monitoring systems combined with pH and CO2-sensitive labels, resulting in crate-level temperature tracking and early spoilage identification through RFID signals, ensuring both safety and quality during supply chain transitions [57,59,64,88].

Among crustaceans with high commercial value, lobsters and crabs require control of temperature and DO, achieved through the employment of DO probes coupled with solenoid-driven oxygen dosing to identify and control DO levels, and temperature sensors in the refrigerated seawater systems to prevent hypoxia and heat stress. Further, accelerometer-based monitoring systems are used to detect excessive shocks during road and air transport, providing feedback on their health conditions [89,90]. Advanced traceability frameworks, including IoT and blockchain platforms, have also been proposed for lobster and crab supply chains to automate and maintain transparent quality records [62,91]. For mitten crabs in e-commerce systems, automated route optimization and RFID-based logistics tracking are increasingly applied to manage peak-season storage and transportation surges [80,92].

Shrimp and prawns are highly vulnerable during transport because of their high oxygen demand due to metabolism and sensitivity to water quality changes, as they are highly prone to microbial contamination. Automated transport systems typically incorporate high-frequency DO sensing with variable-rate aeration and oxygen dosing, along with salinity and pH sensors to detect changes caused by overcrowding and prolonged confinement. Ammonia and nitrite accumulation are monitored using ion-selective electrodes and colorimetric strips with digital readout, triggering partial water exchange when the threshold level of nitrogen-derived compounds is exceeded [81,86]. Predictive models, including AI-driven survival forecasting and disease-risk analysis, further enhance logistics planning and reduce losses in intensive shrimp supply chains [82]. For crayfish, transportation systems require a balance between low temperatures to suppress the metabolic activity and ensure high humidity (in damp transport) and sufficient DO if transported along with water. Automated solutions include crate-level temperature and humidity loggers with TTI labels for dry transport, as well as immersed probes for pH, DO, and temperature in wet systems. In addition, GPS and accelerometer-based monitoring can be integrated with smart-grid logistics concepts to secure a continuous power supply for reefers and optimize routes for live shipments [89,93]. Across species, the integration of automated monitoring into transportation offers multiple advantages by closed-loop chilling systems that maintain crate and tank temperatures within strict limits, aeration and oxygen dosing systems that stabilize dissolved oxygen and strip excess CO₂, smart labels that provide an at-a-glance indication of spoilage and stress, and RFID combined with blockchain solutions that enable immutable supply chain traceability [59,62,86].

5.4. Automation for Survival, Quality, and Welfare Monitoring

Monitoring the survival and welfare of live shellfish during transportation extends beyond environmental parameters and the advanced monitoring achieved by incorporating biosensors, image-based systems, and predictive algorithms, which detect early signs of mortality, stress, and compromised quality, enabling automated interventions. For mussels and oysters, mortality rate is often unidentifiable until deterioration becomes transparent, so research has explored the use of biosensors that monitor shell valve activity and low-frequency imaging systems to detect abnormal gaping patterns. Changes in shell closure behavior, recorded via accelerometer-based biosensors attached to individual oysters, provide real-time stress signals under fluctuating temperature and salinity [59]. Image-based quality assessment systems are also being applied at arrival points, where automated cameras classify live and dead mussels based on shell gape and color, reducing the subjectivity of manual inspection [13]. Predictive models trained on temperature, oxygen, and pH histories support adaptive ventilation and routing, improving survival probabilities in live shipments compared with conventional static control [6,21,62].

For lobsters and crabs, survival and welfare monitoring has focused on behavioral and physiological conditions. Video-based tracking systems can detect abnormal movement or prolonged immobility in transport tanks, while biosensors monitoring hemolymph metabolites such as lactate are under development as non-invasive stress indicators [89]. Some studies have also proposed the use of accelerometer-based tags placed within transport crates to monitor activity levels and limb movement, providing a proximity for stress induced by temperature or oxygen fluctuations [90]. In addition, AI-enabled predictive models that integrate temperature history, DO fluctuations, and handling frequency have been trained to forecast survival probability during extended shipments of crabs and lobsters, offering decision support for logistics adjustments [80].

In shrimp and prawn, high-density stocking accelerates the accumulation of metabolic wastes, particularly ammonia and CO₂, making continuous quality monitoring necessary. Automated systems have incorporated pH-sensitive indicators, both in the form of colorimetric smart labels embedded within transport tanks and miniaturized ion-sensitive field-effect transistor (ISFET) pH sensors to track acidification caused by metabolic buildup as it degrades the quality of shrimp [86]. These devices can be coupled with AI-driven models that forecast survival rates by combining pH trends, ammonia levels, and dissolved oxygen consumption data. Machine learning classifiers have further been applied to predict the onset of disease events during live shrimp transport, reducing unexpected mortality and supporting proactive logistics decisions [81]. For crayfish, survival is closely tied to stress from handling, desiccation in damp transport, and oxygen depletion in wet systems. Automated monitoring approaches have included image-based motion detection systems inside transport crates, which flag abnormal inactivity suggestive of mortality. In addition, biosensors for hemolymph stress markers are being investigated for integration with smart monitoring units, while accelerometer-based crate sensors can track agitation levels and link them to rough handling events during road transport [93]. When thresholds are breached, for example, when motion sensors detect mass inactivity or pH sensors register rapid acidification, automated decision triggers send alerts to handlers, prompting interventions such as water exchange, oxygen injections, or route adjustments. The integration of biosensors, imaging systems, and AI predictive models provides a multi-layered approach to monitoring survival and welfare across shellfish species. These systems reduce reliance on delayed manual inspection by identifying stress responses at early stages, while automated decision triggers ensure rapid intervention. This combination of real-time physiological and behavioral monitoring with predictive analytics is a critical step toward improving survival outcomes and minimizing economic losses in live shellfish logistics [62,86,89].

5.5. Automation in Logistics

Automation in logistics has become significant to maintaining the survival and quality of live shellfish throughout the supply chain, where the challenges extend beyond transport conditions to include route optimization, packaging automation, inventory handling, and traceability systems. The integration of artificial intelligence (AI), Internet of Things (IoT), and blockchain platforms has enabled species-specific logistics management, reduced mortality, and improved operational efficiency.

For mussels and oysters, which are typically shipped in insulated boxes without active aeration, logistics automation focuses on packaging automation and cold chain integrity. Automated filling and sealing machines have been employed to pack mussels into corrugated polypropylene boxes with recycled PET insulation or vacuum-packed modified atmosphere packaging (MAP), ensuring uniform packing density and reduced manual handling [87,90]. IoT-enabled cold chain monitoring allows real-time verification of temperature and humidity fluctuations during shipment, with automated alerts triggered when thresholds are exceeded, thereby preventing mass mortality due to desiccation. Inventory automation also facilitates the rapid turnover of live oysters in distribution hubs, minimizing holding time and associated stress.

For lobsters and crabs, which are high-value commodities, logistics automation incorporates sensor-based transport crates, automated water circulation systems, and AI-driven route planning. Transport vehicles equipped with automated dissolved oxygen injection systems, coupled with GPS-linked predictive models, can adjust travel routes or stop for aeration when survival forecasts indicate risk [92]. Packaging automation also includes the use of reinforced, reusable plastic crates with shock-absorbing inserts, loaded by robotic arms to reduce damage handling. Blockchain-based traceability systems have been piloted for lobster supply chains, allowing end-to-end recording of environmental data and custody transfers, thereby guaranteeing product quality and origin authenticity [62].

For shrimps and prawns, logistics automation has been applied to address the challenges of high-density stocking and water quality deterioration. Automated aerated transport tanks are equipped with real-time ammonia, salinity, and pH sensors linked to cloud platforms, where predictive algorithms calculate the time window for safe transport and generate alerts before critical limits are reached [86]. For crayfish, logistics challenges arise from variable consumer markets and long-distance land transport. Automation in this context has focused on route optimization systems that integrate traffic, temperature, and survival models to minimize transit stress [59]. AI-based logistics platforms are being deployed to match supply with demand across fragmented markets, reducing delays that often compromise survival rates. Across all shellfish species, logistics automation is increasingly supported by integrated traceability frameworks, where blockchain and cloud-based platforms record environmental data, location, and handling events in real time [91]. This data-driven logistics ecosystem not only improves survival outcomes but also enhances consumer trust by ensuring transparency from harvest to retail (Figure 7 and Table 2).

Table 2. Overview of Automation in the Shellfish Supply Chain.

Stage	Automation Focus	Technologies	Advantages	References
Hatchery & Spat Transfer	Automated feeding, grading, and spat attachment	GPS-guided feeders, ANN-based shrimp feeders, and an automated oyster spat insertion device	Reduced labor, uniform growth, and efficient stocking	[8,15,18]
Farm Monitoring & Husbandry	Real-time environmental and stock monitoring	IoT sensor networks, underwater drones (BlueROV2), computer-vision “Oystamaran”	Precision aquaculture, early warning of stress events	[19,20,21,22,23,24]
Harvesting	Mechanized lifting, automated grading, and sorting	Hydraulic winches, automated graders, and vision-based oyster sorters	Faster harvest, improved product quality, reduced labor needs	[29,30,31,32]
Processing	Depuration, non-destructive quality inspection, robotic handling	Automated depuration systems, hyperspectral or CT imaging, Coboshell robot, and delta-robot pickers	Enhanced food safety, higher accuracy, and less waste	[36,37,38,45,46]
Traceability & Logistics	Smart supply-chain tracking and live-transport control	QR codes, RFID, blockchain, IoT-enabled cold chain	Transparency, regulatory compliance, survival & welfare monitoring	[47,48,49,79,81]

Table 2. Overview of Automation in the Shellfish Supply Chain.

Stage	Automation Focus	Technologies	Advantages	References
Hatchery & Spat Transfer	Automated feeding, grading, and spat attachment	GPS-guided feeders, ANN-based shrimp feeders, and an automated oyster spat insertion device	Reduced labor, uniform growth, and efficient stocking	[8,15,18]
Farm Monitoring & Husbandry	Real-time environmental and stock monitoring	IoT sensor networks, underwater drones (BlueROV2), computer-vision “Oystamaran”	Precision aquaculture, early warning of stress events	[19,20,21,22,23,24]
Harvesting	Mechanized lifting, automated grading, and sorting	Hydraulic winches, automated graders, and vision-based oyster sorters	Faster harvest, improved product quality, reduced labor needs	[29,30,31,32]
Processing	Depuration, non-destructive quality inspection, robotic handling	Automated depuration systems, hyperspectral or CT imaging, Coboshell robot, and delta-robot pickers	Enhanced food safety, higher accuracy, and less waste	[36,37,38,45,46]
Traceability & Logistics	Smart supply-chain tracking and live-transport control	QR codes, RFID, blockchain, IoT-enabled cold chain	Transparency, regulatory compliance, survival & welfare monitoring	[47,48,49,79,81]

Table 3. Comparative performance of automation technologies in shellfish aquaculture.

Application	Species	Automation	Measured Parameters	Efficiency (%)	Result	Reference
Robotic oyster grading	Crassostrea gigas	Machine-vision system (YOLOv8)	Classification accuracy	100% accuracy in size and shape grading	Eliminated manual sorting errors and improved uniformity	[38]
Shrimp freshness detection	Litopenaeus vannamei	E-nose and SVM classification	VOC analysis	96.29% accuracy for freshness prediction	Real-time spoilage assessment through a non-destructive method	[48]
Crab sex identification	Portunus trituberculatus	Deep learning through YOLOv8 segmentation	Image classification	100% identification accuracy	Automated sorting under variable lighting	[51]
Automated crab meat picking	Portunus trituberculatus	Robotic arm and vision feedback	Extraction efficiency	97.67% picking precision	Reduced meat loss and improved yield	[51]
Smart feeding control	Crassostrea gigas and Mytilus edulis	IoT sensor network and AI feed optimization	Feed conversion ratio (FCR)	20–30% feed savings	Reduced waste and improved growth efficiency	[9]
Water-quality monitoring (LoRaWAN)	Coastal bivalve farms	LoRaWAN and edge gateway	pH, DO, temperature	Continuous 99% uptime in data transfer	Low-power, real-time monitoring for remote sites	[11]
Blockchain traceability	Shellfish in the supply chain	Blockchain and QR integration	Product traceability time	Reduction from 24 h to less than 1 h	Enhanced transparency and data integrity	[13]
Automated bag flipping (Oystamaran)	Crassostrea gigas	Vision-guided robotic system	Labor hour	More than 70% reduction in manual flipping time	Reduced labor exposure and ensured operational safety	[24]
Automated seeding device	Crassostrea gigas	Spat insertion robotic system	Throughput improvement	2.5× faster than manual threading, reduced 4.71% spat damage, and 92.08% fixation rate	Enhanced hatchery-to-farm efficiency	[21,22]

Table 3. Comparative performance of automation technologies in shellfish aquaculture.

Application	Species	Automation	Measured Parameters	Efficiency (%)	Result	Reference
Robotic oyster grading	Crassostrea gigas	Machine-vision system (YOLOv8)	Classification accuracy	100% accuracy in size and shape grading	Eliminated manual sorting errors and improved uniformity	[38]
Shrimp freshness detection	Litopenaeus vannamei	E-nose and SVM classification	VOC analysis	96.29% accuracy for freshness prediction	Real-time spoilage assessment through a non-destructive method	[48]
Crab sex identification	Portunus trituberculatus	Deep learning through YOLOv8 segmentation	Image classification	100% identification accuracy	Automated sorting under variable lighting	[51]
Automated crab meat picking	Portunus trituberculatus	Robotic arm and vision feedback	Extraction efficiency	97.67% picking precision	Reduced meat loss and improved yield	[51]
Smart feeding control	Crassostrea gigas and Mytilus edulis	IoT sensor network and AI feed optimization	Feed conversion ratio (FCR)	20–30% feed savings	Reduced waste and improved growth efficiency	[9]
Water-quality monitoring (LoRaWAN)	Coastal bivalve farms	LoRaWAN and edge gateway	pH, DO, temperature	Continuous 99% uptime in data transfer	Low-power, real-time monitoring for remote sites	[11]
Blockchain traceability	Shellfish in the supply chain	Blockchain and QR integration	Product traceability time	Reduction from 24 h to less than 1 h	Enhanced transparency and data integrity	[13]
Automated bag flipping (Oystamaran)	Crassostrea gigas	Vision-guided robotic system	Labor hour	More than 70% reduction in manual flipping time	Reduced labor exposure and ensured operational safety	[24]
Automated seeding device	Crassostrea gigas	Spat insertion robotic system	Throughput improvement	2.5× faster than manual threading, reduced 4.71% spat damage, and 92.08% fixation rate	Enhanced hatchery-to-farm efficiency	[21,22]

6. Challenges in Automation and Future Trends in the Shellfish Sector

6.1. Structural Constraints in a Fragmented Industry

Shellfish value chains are dominated by small and medium-sized enterprises, which constrains the adoption of capital-intensive sensing and automation despite clear technical upside. At sea, computer vision systems for buoy and asset detection can localize and classify targets in dynamic scenes, yet deployment depends on specialized imaging, ruggedized computing, and life cycle maintenance that are difficult to amortize at small and medium scales [94]. Onshore, standardized depuration imposes parallel infrastructural demands. Microbiological assurance requires water near 30 ± 1 °C, salinity in the range of 25 to 35 psu, and residence on the order of 36 h with size class selection, which implies reliable pumps, chillers, sensors, supervisory control, and trained operators [95]. Field deployments confirm the premium on in situ sensing and regulatory compliance. A ten-buoy LoRa WAN network on the Clyde River recorded a local heat wave with an on-site weather station about 5 °C hotter than a Bureau station 20 km away, a material discrepancy given mortality risk above 30 °C. The same project documented anchoring standards and agency approvals required for lawful placement in working estuaries [20]. Environmental hardening against corrosion, fouling, turbidity, and temperature swings further raises the baseline for automated grading, feeding, and inspection [26]. A practical path for small and medium farms is a laddered roadmap that begins with shared IoT sensing, then adds semi-autonomous handling and finally traceability components, all bound to open data formats for compliance and scale-out across varied sites [9,11,20]. Practically, deployment windows can be anchored to numerical operating limits already enforced in small and medium facilities. Depuration loops run at 30 ± 1 °C, 25 to 35 psu, and approximately 36 h residence with supervised pumps, chillers, and sensor audits [95]. For telemetry, duty-cycled LPWAN such as LoRaWAN or NB IoT with acknowledged uplinks and edge filtering bounds power and backhaul needs on small boats and rafts [11,96]. To manage CAPEX and OPEX, modular kits with sensing first, vision and handling second, and traceability third, paired with service or leasing models and shared gateways, keep maintenance for biofouling, corrosion, and calibration within routine farm visits [26,82].

6.2. Human Capital and Technical Skills Gap

Modern pipelines braid competencies seldom co-located in small and medium teams. Electronic nose platforms require sensor physics, drift governance, chemometrics, and model lifecycle management, framed within HACCP-aligned indicator curation and mobile-facing knowledge bases, an implicit call for embedded information technology and machine learning operations capability [97]. Robotics adds mechatronic integration, fault diagnosis, and preventive maintenance, with the literature emphasizing that seamless integration with existing workflows is itself a learned skill [26]. Even comparatively simple estuarine Internet of Things networks demand radio planning, gateway administration, SDI 12 integration, and packet loss quality assurance to avoid decision-relevant gaps during floods or heat waves [20]. On the biological side, physiological monitoring introduces new specializations. Flexible piezoresistive or bioimpedance sensors for live oysters must be mounted and conditioned without harming the product, while analysts interpret a nonlinear decline in shell closing strength and stress signatures that vary by size and temperature [98,99]. Without targeted upskilling, these cross-domain requirements become practical bottlenecks, even where models already deliver regulatory-grade discrimination in provenance and safety tasks [100,101]. Targeted 6–8 week certificates that blend sensor calibration, data analytics, and basic coding, delivered through university and extension partnerships, are effective in narrowing the skills gap for digital aquaculture operations [6,11,62].

6.3. Research to Deployment Gap in Sensing and Analytics

Laboratory validations are strong but expose translation frictions at scale. Electronic nose pipelines that combine principal component front ends with genetic algorithm optimized back propagation classifiers routinely exceed 95% accuracy across storage temperatures, corroborated by gas chromatography mass spectrometry volatiles such as dimethyl sulfide emergence and concurrent textural softening. Yet authors still flag power budgets, compute requirements, and drift control as barriers, recommending fewer sensors and routine calibration [97]. Field networks introduce additional constraints, including stochastic packet loss tied to terrain and signal-to-noise, asynchronous device clocks, and biofouling, mitigated only through disciplined maintenance, time-aligned sampling, acknowledgements, and interpolation [20]. Physiological analytics show similar nuances. Survival models trained on shell closing strength, temperature, and grade achieve respectable accuracy at 4 °C and 25 °C but degrade in more fragile stock classes, which underscores the need for adaptive modeling and explicit uncertainty treatment if forecasts are to drive logistics decisions [98]. Robotics reviews add underwater maintenance complexity and sensing brittleness under turbidity and temperature swings as common stall points between pilots and routine use [26]. High-performing packaging cells illustrate the same arc. Pruned detectors compressed to about 4 MB can deliver near-edge inference with grasp success above 90% in static tests, yet throughput declines when belt speeds exceed roughly 5 cm per second, necessitating iterative line engineering and workspace redesign [102]. Field readiness should be evidenced by accuracy, precision, repeatability, uptime, and mean time between failures under salinity, turbidity, and vibration stress, with drift governed by reference checks, redundancy, and statistical recalibration such as Kalman filtering and exponential smoothing, and uncertainty quantified for decision transparency [20,21,36].

6.4. Financing Barriers and Cost of Adoption

Sensor-driven, real-time monitoring reframes cost structures rather than removing them. Acquisition covers multi-gas arrays, imaging, and embedded compute. Integration requires enclosures, tanks, and network backbones hardened for saline wet environments. Compliance adds accreditation, audits, and digital record keeping. Sustainment includes calibration, firmware updates, and consumables [96]. We quantify operational energy, embodied materials, and end-of-life impacts for IoT, AI, robotics, and blockchain, and we discuss practical mitigations including duty-cycled sensing, edge inference, solar assistance, modular repair, and take-back [20,95]. The hardware stack carries nontrivial energy and materials overhead, yet duty-cycled telemetry, solar-assisted gateways, and lightweight inference substantially reduce the added footprint, and when loss-prevention is counted, the net contribution to sustainability remains positive at farm scale [21,60]. Qualitatively, added energy and materials for sensors, enclosures, and tags are offset by avoided losses where predictive closures and cold chain control reduce mortality, waste, and repeat handling [21,62,98]. In bivalves, early HAB and temperature alerts limit emergency harvests and rework [21]. In logistics, RFID and IoT crate monitoring curb spoilage and returns [98]. For mussels and for crabs and lobsters, packaging and cooling automation stabilizes humidity and temperature, improving survival and reducing product disposal and transport reruns [87,90]. Even where stacks demonstrably outpace destructive assays, including colorimetric arrays for total volatile basic nitrogen, radio frequency harvested ammonia tags, bioimpedance stress telemetry, and knowledge-engineered HACCP with automated corrective actions, calibration logistics, and data stewardship remain recurring expenses [25,45,99,102,103]. Traceability chemistry offers high evidentiary value but often depends on multicollection mass spectrometry, which is rarely accessible to small and medium enterprises [104]. Bridging these gaps will likely require equipment as a service, analytics subscriptions bundled with hardware, and public–private risk sharing that converts one-time capital expenditure into a predictable operating expense. In addition to financial outlays, the stacks reviewed here carry energy and materials overhead that must be made explicit in adoption plans. The constraints, including power budgets and battery management for embedded sensing, periodic calibration and associated consumables, corrosion and biofouling countermeasures in saline settings, and preventive maintenance for mechatronic cells, translate into ongoing resource use [102]. Followed by that, field-tested LoRaWAN and NB-IoT networks used in coastal aquaculture have demonstrated mean data-transmission reliability above 97%, signal loss rates below 2%, and latency under 1s for small payloads [20,21]. These results align with acceptable error limits, which are less than <5%, defined for precision-monitoring systems, confirming that well-positioned LPWAN infrastructures can sustain continuous operation. Redundant acknowledgements, interpolation, and adaptive retransmission further preserve read-rate continuity under intermittent signal and attenuation events, ensuring reliability across saline and wave-affected nodes. Without governance, efficiency gains can encourage higher throughput or more intensive monitoring, increasing aggregate energy draw, replacement cycles for probes and batteries, and cleaning loads. A practical countermeasure aligned with our findings is to prioritize duty cycled telemetry and calibrated non-destructive sensing that prevent losses and unnecessary handling over capacity expansion, to encode drift governance and handover calibrations so measurements remain comparable without escalating assay frequency, and to favor miniaturized modular hardware that reduces material mass and simplified servicing [20,26,103]. Reporting simple auditable indicators that operators already track, such as the cadence of battery replacements, calibration events aligned to quality assurance visits, and avoided product losses attributable to earlier interventions, keeps environmental accounting proportional to small and medium enterprise practice while anchoring claims in the same compliance record sets used for hazard analysis and critical control point documentation and audits. For small producers, we point to bundled service models with scheduled calibration and maintenance, transparent energy and replacement logs, and payments linked to verified loss-avoidance [20,26].

6.5. Towards Interoperable, Explainable, and Sensor Rich Systems

A pragmatic roadmap can be devised to fulfill this initiative. First, sensor fusion with model parsimony at the edge preserves accuracy while containing compute and battery draw, aligning with duty-cycled deployments from farm to retail [97]. Second, calibration-anchored credibility is essential. Anchoring electronic nose arrays to gas chromatography mass spectrometry signatures and environmental probes to local references stabilizes longitudinal comparability and smooths regulatory acceptance [20,97]. Third, miniaturization, modularity, and power discipline should be treated as first-class constraints, enabling product variants that small and medium enterprises can adopt incrementally and service predictably. Fourth, physiology-aware logistics, enabled by flexible shell closing strength or bioimpedance sensors, brings living animal biology into control loops, supporting temperature and grade-aware handling and event-triggered mitigations during transport [98,99]. Fifth, 24/7 robotic sensing and targeted actuation can extend situational awareness and reduce waste, provided maintenance is engineered into the business model [26]. Finally, farmer-centric visualization and alerting remain decisive. Mobile first dashboards, spatial interpolations, and harvest rule overlays translate high-dimensional streams into timely, auditable actions that anchor both compliance and trust. Our roadmap prioritizes loss-prevention, auditability, and SME affordability over raw throughput, using explainable models, minimal sensor sets, and calibration hand-offs that keep data reliable in harsh marine settings [20]. The sector’s challenge is less a shortage of capable sensors or algorithms than the orchestration of interoperable, explainable, and serviceable systems that survive marine realities, pass audit, and fit the balance sheets of small and medium enterprises. This sequencing emphasizes loss prevention, drift governance, and auditability over increases in raw throughput, which helps curb the tendency of efficiency improvements to drive higher resource use under real operating incentives [20,26]. In practical terms, the pathway leverages constraints like power, calibration, maintenance, and compliance to bound rebound effects while delivering measurable gains in quality and safety. Standardized interfaces between QR, RFID, IoT, and blockchain enable end-to-end traceability while containing compute and power at the edge [91,96]. Where connectivity is sparse or intermittent, use store-and-forward edge gateways with LoRaWAN bursts to dockside or road cell relays, and ensure offline continuity via crate-level QR or RFID that travels with lots [65]. Smart labels for pH or CO₂, time temperature indicators, and on crate loggers backfill gaps so compliance checks and recalls do not depend on continuous links [64,98]. When coverage improves, batch sync to blockchain or cloud restores end-to-end ledgers without data loss or operator burden [76,96].

7. Conclusions

Automation in the shellfish sector is transforming the industry from manual, destructive, and repetitive practices into sustainable, data-driven, precision, and accuracy-based operations. Across the shellfish supply chain, from production to harvesting, processing, traceability, and logistics, the adoption of artificial intelligence, robotics, IoT, blockchain, and other advanced automatic systems has demonstrated the constraints associated with the traditional practices and their replacement capacity, aiding in labor reduction, environmental sustainability, improved product quality, and increased consumer trust. Automation and digitalization directly align with SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water) by enhancing productivity, traceability, and ecosystem protection in aquaculture. These technologies promote circular-economy approaches, minimize waste, and strengthen resilience against climate-driven variability in shellfish farming. Integrating AI-enabled monitoring with IoT-based sustainability dashboards further supports data-driven decisions for long-term food-system resilience. Even though the shellfish sector faces barriers like high capital costs, gaps in technical employment, fragmented supply chains, and challenges associated with the upscaling of products out of highly perishable shellfish that can potentially produce a biotoxin in real-world deployments, these hurdles can be clearly addressed by the application of automatic systems. Moreover, those concerns will also require modular, interoperable solutions: farmer-friendly analytics and visualization tools, innovative financing models, and coordinated policies that incentivize adoption by small and medium enterprises. With targeted mitigation and SME-appropriate financing, these systems can raise productivity and traceability while keeping costs and footprints in check, aligning with SDG 2, SDG 12, and SDG 14.