Browsing by Author "Sumana, Sahr Lamin"

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Challenges in Developing Aquaculture for Livelihood Enhancement in Bo City, Southern Sierra Leone
(2023-01-15) Sumana, Sahr Lamin
The study investigated socioeconomic characteristics, aquacultural practices and constraints among smallholder fish farmers in the study location of Bo City, Southern Sierra Leone. The present study employed a survey research design. The smallholder farmers who were growing food for themselves, for sale, and for consumption by others as well as those who were primarily growing it for the market were each given 40 questionnaires in total. The results revealed that most of the respondents were male (90.0%), married (60.0%), lacked training in fish farming (57.5%) and the household income of most (60.0%) ranged from NLe600-NLe1,000. The farmers mostly opined low fish farming experience (70.0%), Semi-intensive fish farming (67.5%), low aquaculture development (57.5%), predominantly seasonal fish pond irrigation (77.5%). All of the respondents’ view fish farming as an economically viable enterprise as well as a profitable (87.5%) entity. The mean rank of constraints was significantly (p ≤ 0.006) differed among fish farmers. The key fish farming constraints included culturing fish, fish feed in culturing fish, less interest, fewer fish farmers, high inputs, high cost of fish pond construction and intensive capital investment that could be exploited for improved productivity. The outcomes of these recent studies imply that fish farmers should receive training and funding for their operations to improve their competitiveness in their smallholder fish businesses as well as in the local and international aquaculture industries. Also, the governments and other practitioner authorities ought to implement policies and initiatives that would support fish farming activities, further research development in aquaculture, the development of the nation, and the fish farmers themselves.
Chemical interactions between filter-feeding mussels and Ulva prolifera: The role of dissolved organic matter and secondary metabolites in growth promotion and competition inhibition of algal species
(Elsevier BV, 2025-09-06) Sumana, Sahr Lamin
This review examines the chemical and ecological interactions between filter-feeding mussels and the green macroalga Ulva prolifera in integrated multi-trophic aquaculture (IMTA) systems. Mussels are crucial for nutrient recycling, as they filter water and release bioavailable compounds such as ammonium (NH 4 + ), urea (CO(NH 2 ) 2 ), and dissolved organic matter (DOM). These compounds promote Ulva growth and enhance microbial activity. In turn, U. prolifera produces sulfated polysaccharides, phenolics, and halogenated metabolites that can influence microbial communities, suppress competitors, and potentially affect mussel physiology at high concentrations. The review emphasizes the interconnectedness of nutrient exchange, DOM cycling, and microbial genes such as pmoA and mcrA, highlighting the novelty of integrating microbial ecology with biogeochemical cycles and ecosystem outcomes, illustrating both the synergies and risks present in co-culture systems. While moderate production of metabolites helps control biofouling and maintain ecosystem stability, excessive DOM or allelochemical accumulation can hinder mussel filtration and lead to hypoxia. Maintaining specific thresholds, such as DOM concentrations below 5 mg C/L and labile-to-refractory DOM ratios above 1:1, is essential for balance. This synthesis integrates microbial ecology, metabolite feedbacks, and biogeochemical processes to provide a framework for resilient IMTA design. It also emphasizes practical strategies like adjusting stocking densities, optimizing water exchange, and employing microbial monitoring tools to promote sustainable IMTA practices. By linking molecular interactions to ecosystem-scale outcomes, the review offers guidance for sustainable aquaculture systems that enhance productivity, minimize environmental risks, and improve resilience in the face of climate stress.
Embodied AI for aquaculture monitoring: From stationary sensing to mobile robotic observation
(Elsevier BV, 2026-06-18) Sumana, Sahr Lamin
Aquaculture currently accounts for over 57% of global fisheries production and is projected to meet more than 60% of seafood demand by 2030. However, its efficiency and sustainability are hindered by challenges such as inadequate environmental monitoring and stock assessment. This review investigates the application of embodied artificial intelligence (AI) in mobile robotic monitoring for intensive aquaculture. It contrasts this approach with conventional AI, which depends on fixed data sources, highlighting how EAI enables autonomous environmental interaction and enhances adaptive behavior within aquaculture systems. Following a systematic review methodology (not a meta-analysis), 118 studies from Web of Science, Scopus, IEEE Xplore, PubMed, and Google Scholar were synthesized after screening against predefined eligibility criteria. This synthesis aimed to examine advances across key areas: mobile robotic platforms, sensor fusion, underwater navigation, adaptive control, and species-specific monitoring applications. The review reveals that most current aquaculture systems are still weakly embodied, operating primarily as mobile sensing platforms with onboard or offboard inference rather than truly autonomous learning agents. Despite this, autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), uncrewed surface vehicles (USVs), and bioinspired robotic systems significantly enhance spatial monitoring coverage and adaptive environmental sampling compared to stationary sensing methods. However, critical operational barriers persist, including underwater image degradation, biofouling, turbidity, light attenuation, sensor drift, limited battery endurance, and challenges in underwater simultaneous localization and mapping (SLAM) and real-time communication. The review further emphasizes that aquaculture is a highly non-linear biological system where EAI-driven adaptive sampling, multimodal sensor fusion, and reinforcement-learning-based control are particularly effective for improving dissolved oxygen (DO) monitoring, feeding optimization, environmental prediction, and welfare assessment. The study also distinguishes classical feedback control from learning-based embodied agents, offering a critical assessment of the current technological maturity of closed-loop aquaculture automation. Looking ahead, future next-generation aquaculture systems are expected to integrate EAI with Agentic AI, digital twins, energy-aware robotics, and circular-economy principles. This integration aims to reduce feed waste, operational costs, and carbon footprints, thereby improving sustainability and ecological resilience.