The research team, led by Syed Ariful Haque of Sultan Qaboos University, surveyed 40 of the 45 retailers active at Barka market between March and June 2025, a coverage rate of nearly 89 percent. Through face-to-face interviews, direct field observations, and organoleptic assessments of fish quality based on appearance, odour, colour, and texture, the team distinguished between physical loss, where fish is discarded outright with no recoverable value, and quality loss, where degraded fish sells at a steep discount. Both were converted into monetary terms using prevailing market prices for comparable good-quality fish of the same species on the same trading day.

The scale of the problem became clear when the researchers applied ordinary least squares regression to the data. Their final nine-predictor model explained 84.2 percent of the variation in daily monetary loss, a remarkably strong fit for a field study. Among the most striking results: retailers who delayed icing after purchase lost roughly 22.8 rials more per day than those who iced promptly, while every additional hour of selling time added about 8.6 rials in losses. Conversely, retailers who washed fish immediately after purchase lost about 16.8 rials less per day, those who maintained the recommended ice-to-fish ratio lost about 14.4 rials less, and those who had received training in fish handling or post-harvest management lost roughly 26 rials less than their untrained counterparts.

The training figure deserves particular attention, because it was rare. Only 7.5 percent of surveyed retailers had ever received formal instruction in fish handling, quality management, or loss reduction, and just 7.5 percent maintained the recommended ice-fish ratio. A staggering 90 percent admitted to delaying icing after receiving their fish. In a market where ambient temperatures in northern Oman climb to 48 degrees Celsius during the summer months, these gaps in the cold chain are not minor operational lapses; they are the central mechanism by which value evaporates.

The species-level picture added further texture. The researchers documented 36 species traded at the market, with yellowfin tuna commanding the widest coverage, handled by 25 retailers and averaging 70.3 kilograms of good-quality fish per retailer. Red seabream suffered the highest mean percentage monetary loss at 8.67 percent, followed by kingfish at 6.16 percent, longtail tuna at 5 percent, yellowfin tuna at 4.91 percent, and golden trevally at 4.37 percent. Thirteen species recorded no monetary losses at all during the survey. The authors are careful to stress that these rankings are descriptive snapshots rather than evidence that certain species are inherently more fragile; retailer representation varied enormously across species, and individual traders handled multiple species, making statistical comparison inappropriate.

When retailers themselves were asked what drove their losses, their answers converged on the same operational chokepoints the regression had flagged. On a five-point agreement scale, the strongest consensus concerned delays in auctioning, which received the lowest mean score of 1.48, followed closely by insufficient water-supply facilities for frequent washing at 1.63 and failure to maintain the proper ice-fish ratio at 1.68. Extended hours of fish selling rounded out the top concerns. At the other end of the spectrum, the time fish cutters took to process fish was rated least problematic, with a mean score of 3.33. A Friedman test confirmed that these ratings differed significantly across items, though the researchers found no statistically significant correlations among the perceived causes after correcting for multiple comparisons.

Some of the demographic findings were counterintuitive and illuminate the human dimension of the problem. Older retailers actually lost more money per day, with each additional year of age associated with a 0.3-rial increase in daily losses. But field observations explained the paradox: many older traders had entered fish selling only after retiring from other professions, so their chronological age masked relatively little market experience. Fish-business experience itself, measured in years of trading, was associated with lower losses, as were higher monthly income from fish trading and handling a wider variety of species. The income relationship is nuanced, the authors caution, since income may reflect business organization and market position rather than a direct protective effect, and transaction volume was not directly measured in the model.

The study’s authors frame their results within a global context of mounting concern over fish loss and waste in low- and middle-income countries, where small-scale fisheries underpin nutrition, livelihoods, and food security. Reducing post-harvest losses, they note, enhances food availability and economic resilience without increasing fishing pressure on wild stocks, making post-harvest management a cornerstone of sustainable aquatic food systems. The findings align with evidence from Bangladesh, Ethiopia, Tanzania, Kenya, and Zambia, where marketing delays, inadequate icing, and poor handling have repeatedly emerged as dominant loss drivers. The work also connects to broader sustainability goals, including Sustainable Development Goal Target 12.3 on halving food waste and the FAO’s Blue Transformation agenda.

Importantly, the team resists overstating what a cross-sectional survey of 40 retailers can prove. The regression coefficients describe adjusted associations, not causal effects, and the small sample size, sparse representation of certain practices, and seasonal scope of the study, which captured only the transition into summer rather than the full annual cycle, all counsel caution. A sensitivity analysis excluding the single most influential respondent left every significant predictor intact, and diagnostic tests found no problematic heteroskedasticity or multicollinearity, but the authors explicitly call for larger, longitudinal, multi-market studies before drawing national conclusions about Oman’s fish losses.

Even so, the practical roadmap that emerges is refreshingly concrete. The researchers propose a phased intervention strategy beginning with low-cost measures: market-based training on immediate icing and hygiene, optimization of auction scheduling to cut the delays retailers identified as their worst problem, and an audit of water access to ensure retailers can wash fish frequently. Medium-term priorities include assessing ice supply reliability and piloting insulated storage and display systems, with any investment evaluated against measurable indicators such as fish temperature, ice consumption, rejected weight, and monetary loss. Only after those foundations are secured do the authors recommend capital-intensive infrastructure and advanced chilling technologies such as slurry ice. In a market where a handful of operational habits, washing promptly, icing on time, selling faster, could together shift losses by tens of rials per retailer per day, the cheapest technology may simply be better organization of the hours between the auction block and the customer’s hand.

Subject of Research: Post-harvest fish loss and its operational and market determinants in a small-scale retail fish market in Oman

Article Title: Systemic drivers of post-harvest fish loss in small-scale retail markets: Insights from Barka fish market, Oman

Article References: Haque, S. A., Jufaili, S. M. A., Adhoobi, A. S. A., Darmaki, H. A. A. A., Al-Owaisi, Y., Waheibi, W. A. A., & Rahman, M. C. (2026). Systemic drivers of post-harvest fish loss in small-scale retail markets: Insights from Barka fish market, Oman. Results in Engineering, 32, Article 113221. https://doi.org/10.1016/j.rineng.2026.113221

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113221

Keywords: post-harvest fish loss, small-scale fisheries, Oman, Barka fish market, cold chain, icing, food waste, fish quality, retail markets, food security, sustainable fisheries, regression analysis

The review, authored by Shao-Wei Ho, Ji-Yu Wu, Yu-Wei Chen, Chieh-Kai Yang and Wen-Ping Tsai of National Cheng Kung University in Taiwan, synthesizes recent advances across the major domains of marine science. Rather than cataloguing individual algorithms, the authors emphasize a common pattern: machine learning and deep learning models are enabling scientists to extract actionable knowledge from vast, high-dimensional and often messy ocean observations at scales that were previously unimaginable. Where traditional conservation relied on labor-intensive field surveys, laboratory analyses and satellite remote sensing that were slow, expensive and geographically constrained, AI-driven systems now process satellite imagery, acoustic recordings, underwater video and sensor streams in near real time. One striking example cited in the review: machine learning-based processing of coral reef imagery can run roughly two hundred times faster than manual analysis, allowing assessments that once took months to be completed in days.

At the technical heart of this transformation sit a handful of architectures, each suited to a different kind of ocean problem. Convolutional neural networks, or CNNs, excel at interpreting grid-like image data, learning hierarchical spatial features that progress from simple edges and textures to complex objects, which makes them the workhorse for classifying fish, corals and benthic invertebrates in underwater photographs. Long Short-Term Memory networks, a specialized form of recurrent neural network, use gating mechanisms to selectively retain information over long intervals, making them ideal for time-series forecasting of wave heights, tides, salinity and dissolved oxygen. Random Forest ensembles, which average predictions from many decorrelated decision trees, offer interpretable models for forecasting fish-habitat suitability and estimating chlorophyll-a concentrations. Segmentation networks such as U-Net and its nested variant U2-Net perform pixel-level delineation of coral reef boundaries, sea ice and oil slicks, while generative adversarial networks fill gaps in satellite time series and super-resolve ocean-color imagery.

The review’s analysis of the literature reveals just how dominant vision-based AI has become. CNN-based frameworks accounted for nearly sixty percent of image-based biodiversity studies, and their reported performance is remarkable. One deep learning system achieved 94.9 percent test accuracy in coral and fish recognition, exceeding the 89.3 percent accuracy of human experts on the same task. Coral image classification studies reported overall accuracies of 94.5 percent, with some classes reaching one hundred percent, and large-scale habitat mapping efforts achieved 83 to 94 percent similarity to expert assessments. Platforms such as TagLab, which applies CNN-based segmentation to annotate coral reef orthomosaics, documented roughly a ninety percent increase in identification speed compared with manual routines, while systems like AquaVision automatically detect invasive fish species in the Mediterranean and continuously update their models as new imagery arrives.

Acoustic monitoring is undergoing a parallel revolution. Marine mammals rely on vocalizations as their primary mode of communication, and passive acoustic monitoring systems now use machine learning classifiers to distinguish species-specific calls with high precision, enabling long-term, non-invasive surveillance of whales and dolphins. Deep learning extends this capability across broader frequency bands, enriching the analysis of entire underwater soundscapes. Meanwhile, autonomous underwater vehicles equipped with AI-based object detection are mapping deep-sea habitats inaccessible to divers, and few-shot learning techniques that generalize from limited labeled samples are helping researchers detect rare and endangered species in ecologically sparse datasets. Initiatives such as Seagrass Finder use deep learning on AUV video to map eelgrass, a critical resource for blue carbon accounting.

Fisheries management, long a battleground between productivity and sustainability, may be the domain where AI delivers the most immediate governance benefits. AI-powered electronic monitoring systems installed on fishing vessels use onboard cameras and deep learning algorithms to identify catch composition in real time, reduce bycatch and verify compliance with regulations. The review highlights AI-RCAS, a real-time catch analysis system that combines YOLOv10 object detection with ByteTrack tracking algorithms on embedded Jetson boards, analyzing catches in situ to support enforcement of total allowable catch limits. A lightweight MobileNet-based classifier reported up to 97 percent species-level accuracy in electronic monitoring pipelines, an edge-efficient design suited to resource-constrained vessels. Beyond enforcement, machine learning models trained on environmental and biological data forecast fish stock fluctuations, and reinforcement learning is being used to design adaptive harvest control rules that balance catch efficiency with conservation needs under uncertainty. Platforms like Global Fishing Watch apply pattern recognition to vessel tracking data to expose illegal, unreported and unregulated fishing, promoting real-time transparency across the global fleet.

In aquaculture, the fastest-growing food production sector on the planet, AI is optimizing everything from feeding to disease prevention. Smart feeding systems that monitor fish appetite, movement and water conditions have achieved feed cost reductions of twenty to thirty percent while improving growth rates. Computer vision algorithms detect early visual signs of disease such as lesions, discoloration and erratic swimming, while time-series models trained on water temperature, pH and oxygen data predict outbreaks before clinical symptoms appear. Hybrid deep learning architectures combining CNNs, LSTM networks and attention mechanisms have been proposed to predict nitrate concentrations in recirculating aquaculture systems, and Internet of Things platforms with edge AI continuously monitor water quality, triggering alerts before dangerous thresholds are breached.

Pollution detection and climate impact assessment round out the review’s application landscape. Deep learning models applied to Sentinel-2 and synthetic aperture radar imagery can distinguish oil slicks from optical lookalikes; one hyperspectral framework integrating CNN classification with DBSCAN clustering achieved 92.12 percent mean pixel accuracy while processing each image in under seven hundred milliseconds. AI models also classify floating plastics from hyperspectral satellite imagery, and machine learning has even outperformed humans in microplastic characterization, revealing labeling errors in infrared spectroscopy data. On the climate front, deep learning forecasters reported prediction accuracies exceeding 96 percent for variables such as dissolved oxygen and temperature in coastal time series, and AI-driven ecosystem models project species range shifts and potential collapse thresholds under different emissions scenarios, giving governments and conservation groups the foresight needed for proactive, climate-resilient planning.

The authors are careful, however, not to oversell the technology. Marine AI faces a web of intertwined challenges: ocean data remain fragmented, sparse and geographically imbalanced, especially in polar regions, the deep sea and developing coastal nations; models trained in one environment often degrade when transferred to waters with different turbidity, light or species composition; and the computational demands of deep learning raise both cost barriers and genuine carbon footprint concerns that could undermine the sustainability goals the technology serves. Many biodiversity-rich but technologically underserved regions lack the connectivity and infrastructure for real-time AI deployment, and the opacity of black-box models erodes trust among policymakers and coastal communities whose livelihoods depend on AI-informed decisions. The review calls for explainable AI techniques such as SHAP and Grad-CAM, human-in-the-loop oversight, lightweight and energy-efficient architectures, and federated learning approaches that train models across decentralized networks of buoys, gliders and autonomous vehicles without shipping raw data to central servers, preserving both privacy and bandwidth.

Looking forward, the most promising frontier may be the fusion of machine learning with physical ocean models through differentiable parameter learning and physics-informed neural networks, approaches that embed conservation laws directly into the training process and can cut calibration costs by orders of magnitude. Combined with multi-modal data integration spanning satellites, sonar, environmental DNA and in-situ sensors, and with federated edge intelligence deployed directly at sea, the authors argue that AI can shift marine governance from reactive crisis response to anticipatory, data-driven stewardship. The stakes could hardly be higher: healthy oceans underpin Sustainable Development Goal 14 and the wellbeing of millions. What this comprehensive review makes clear is that the algorithms are ready. The harder work now lies in building the open data infrastructure, ethical safeguards and international cooperation needed to put them to work for the ocean, everywhere, equitably and at scale.

Subject of Research: Applications of artificial intelligence, machine learning and deep learning techniques for sustainable marine resource management

Article Title: Leveraging artificial intelligence (AI) techniques for sustainable marine resources

Article References: Ho, S.-W., Wu, J.-Y., Chen, Y.-W., Yang, C.-K., & Tsai, W.-P. (2026). Leveraging artificial intelligence (AI) techniques for sustainable marine resources. Blue Biotechnology, 3(1), Article 4. https://doi.org/10.1186/s44315-026-00054-0

Image Credits: AI Generated

DOI: 10.1186/s44315-026-00054-0

Keywords: artificial intelligence, machine learning, deep learning, marine biodiversity, sustainable fisheries, ocean conservation, aquaculture, marine pollution, climate change, coral reefs, federated learning, blue biotechnology