What are the Implications of AI in the Fisheriesfisheries Industry?
Automatic Fish disease detection.
Fish Category Identification.
The integration of artificial intelligence (AI) into the fisheries sector manifests a myriad of intricate implications that can metamorphose operational paradigms, accentuate sustainability vectors, and potentiate resource optimisation. Here's an esoteric elaboration:
Despite these intricate implications, one must not obfuscate the associated challenges: potential job obsolescence, data sanctity apprehensions, and the imperative for monumental capital infusion into technologically advanced infrastructures and capacitation. The ethical and sustainable genesis of these AI orchestrations remains paramount to ensure holistic benefits to marine ecosystems and dependent communities.
Nagajothi Velayuthapillai There are already some companies using AI in optimizing their fish farming. You can check this link: https://aquabyte.ai/
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RESEARCH FOR PECH COMMITTEE Artificial Intelligence and the fisheries sector
Policy Department for Structural and Cohesion Policies Directorate-General for Internal Policies PE 699.643 – May 2022
AUTHORS AZTI, Marine and Food Research Divisions, Basque Research and Technology Alliance (BRTA): Dr. Jose A. FERNANDES-SALVADOR, Dr. Izaro GOIENETXEA, Dr. Leire IBAIBARRIAGA, Martin ARANDA, Elsa CUENDE,Dr. Giuseppe FOTI, Dr. Idoia OLABARRIETA, Dr. Jefferson MURUA, Dr. Raúl PRELLEZO, Dr. Bruno IÑARRA, Dr. Iñaki QUINCOCES and Dr. Ainhoa CABALLERO University of A Coruña, ‘Salvador de Madariaga’ University Institute for European Studies: Dr. Gabriela A. OANTA and Prof. José Manuel SOBRINO-HEREDIA
"Main opportunities identified are: 1) increased transparency of fishing activity and reduced impact on the environment, thereby improving the public image of the sector; 2) early warning, forecasting and spatial planning systems can help in the planning activities considering trade-off between them; 3) accelerated and increased data acquisition and coverage for stock assessments, sustainability indicators evaluation and other management data needs; 4) increased economic sustainability of the fishing industry, by reducing operational costs; and, 5) the modernisation of fisheries and its subsequent attractiveness to the younger population.
Main obstacles identified are: 1) industry trust and reluctance; 2) initial costs and lack of expertise; 3) legal and bureaucratic uncertainty.
AI can increase efficiency and reduce costs for industry, but there are important barriers such as developing and installation costs, the scarcity of standards, and a lack of multidisciplinary expertise (biological, AI and legal) to develop fit-for-purpose systems.
Although some AI approaches are considered black boxes (e.g. Artificial Neural Networks (ANNs)), there are other suitable AI methods to understand the basis, processes and model forecasts and their uncertainty (e.g. Bayesian Networks (BNs).
Policy recommendations relevant to EU decision-making to achieve a better use of AI systems in the fisheries sector are: 1) add AI references to fisheries legislation and AIA proposal; 2) regulate the role of technological companies; 3) promote multidisciplinary academia and professionals; 4) regulate the role of AI technological providers, ensuring some degree of experience in fisheries to prevent untrustworthy and not-fit-for-purpose AI systems; 5) develop AI good practices guidelinesfor fisheries; 6) create incentives for the development and use of fit-for-purpose fisheries AI systems; 7) improve and promote public and private data sharing; and, 8) promote awareness and collaboration with fisheries sector."
It is interesting to further analyse the demand-supply of this industry. How sustain the fisheries industry?
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