7 research outputs found
Avenues and means for smart mariculture
Globally, aquaculture is one of the fast-growing production sectors using water
productivity concepts. The term aquaculture refers to the practice of farming/ cultivating
aquatic organisms that include finfish, shellfish and microscopic and macroscopic plants both
in freshwater and saltwater in controlled conditions under human management.
Farming/cultivation implies intervention in the rearing process to enhance production,
breeding, nursery rearing, stocking, feeding, protection from predators, etc. It also implies
individual or corporate ownership, the planning, development and operation of culture
systems, sites, facilities and practices, and production and transport. The social and financial
significance of aquaculture is growing consistently at >6% in recent years. India has immense
potential for aquaculture development, and the sector contributed ≈70% to its total fish
production in 2020
Recommended from our members
Emerging Technologies in Fisheries Science: A Transdisciplinary Report
The Pacific Coast Groundfish Fishery harvests a diverse and large grouping of fishes, but it did not become heavily fished until around WWII. This makes the groundfish fishery a comparatively young fishery. Despite its youth, it is one of the largest and most lucrative fisheries in Oregon—with a current harvest value of approximately $48 million per year, which is exceeded only by the Dungeness crab fishery. Northeastern Pacific Coast Groundfish species are also important for recreational and tribal purposes, although it is difficult to compare these to the commercial industry. With over 90 different species to consider, this commercial fishery is complex, and there are many different stakeholder groups involved, each with their own goals, values, and perspectives.
Fishing regulations greatly impact local stakeholders, some of whom rely on the fishery for their livelihoods. These local stakeholders are dependent on accurate stock assessment surveys and models so that the fishing regulations are appropriate. Some stakeholders feel that regulations tend to be overly cautious to compensate for the large amount of uncertainty involved with managing a fishery and estimating a fish population. To reduce this uncertainty and the need to err so heavily on the side of caution, stock assessment surveys could include innovative technologies and novel datasets. For example, these stock assessments do not currently use automated video surveillance on their bottom trawl surveys, an emerging form of machine learning.
As understood by the NSF-funded National Research Traineeship (NRT) training, there are three interwoven core concepts: 1) Big Data (BD), 2) Coupled Natural-Human (CNH) systems, and 3) Risk and Uncertainty (R&U) analysis and communication. Big Data refers to any high volume of data with high throughput. Coupled Natural-Human systems are the biological and human worlds, as well as their overlap and interaction. Risk is the potential and likelihood of an unfavorable event, and uncertainty refers to the unknowns of a likelihood, process, or analysis. This project chose to investigate these three concepts within the framework of emerging technologies and fisheries science. Emerging technologies are those dealing with BD, since this is a relatively new area of study, and this project specifically focused on computer vision within machine learning. This technology was applied to the realm of fisheries science and ultimately management, which is the study of a coupled natural-human system. Changing oceans conditions mean that Northeastern Pacific groundfish are at risk and their future is uncertain. Therefore, this project set out to determine how the influence of big data, machine learning, ecological inference, and environmental decision making overlap.
The story of the life and study of these fishes in a newly Americanized sea is ready for a closer examination. It is for these reasons combined that Pacific coast groundfish fishery science provides a robust platform in which to explore the autonomous capacity of technology and data production at the intersection of environmental science and decision making. More specifically, to what extent are large, ecological datasets informing the production and application of emerging technologies in fisheries science, and how are these new technologies and sampling methods being integrated into fisheries management frameworks? A case study in which to explore this concept can be found in the testimony of a flatfish, or rather, the complex, ecologically and economically important assemblages of numerous groundfish species in the northeastern Pacific Ocean where flatfish are found
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa