The AI in Sustainable Fisheries and Aquaculture Market Size was valued at USD 573.3 Mn in 2023 and is predicted to reach USD 1,462.8 Mn by 2031 at a 12.7% CAGR during the forecast period for 2024-2031.
AI in sustainable fisheries and aquaculture states combining AI technologies and techniques into industrial procedures and systems to increase automation, real-time decision-making, and inclusive operational efficiency. Hence, the adoption of AI in sustainable fisheries and aquaculture is expected to increase in the near future as concerns grow over the rapid usage of deep learning, machine learning, computer vision, and natural language processing. Increasing demand for seafood owing to declining wild fish stock is expected to drive global AI's growth in the sustainable fisheries and aquaculture market. AI provides the potential to optimize resource utilization, reduce waste, and decrease the environmental impact in various industries. These are other factors expected to augment the target market growth. The increasing adoption of AI in fisheries and aquaculture industries globally is expected to boost market expansion in the coming years.
However, the reduction in physical access and environmental uncertainties of AI in sustainable fisheries and aquaculture, a temporary ban on fisheries and aquaculture, coupled with the COVID-19 outbreak, are factors that may limit the growth of the target market during the forecast period. Furthermore, increasing R&D activities, government initiatives to use sustainable components for production, and investments by prominent players are expected to create lucrative growth opportunities in revenue for players operating in the global AI in Sustainable Fisheries and Aquaculture market over the forecast period.
The AI in the sustainable fisheries and aquaculture market is segmented based on species, application, technology, and end-user. The market is segmented based on species: finfish, shellfish, and crustaceans. The market is segmented by application into aquaculture monitoring and control, fish health monitoring and disease detection, feed management and optimization, water quality monitoring, stock management and yield prediction. The technology segment includes machine learning algorithms, computer vision systems, natural language processing, robotics and automation, data analytics and predictive modelling. The end-user segment includes fish farms and hatcheries, seafood processing companies, research institutions and universities, government agencies and regulatory bodies, and technology providers and AI solution developers.
The machine learning algorithm segment is expected to hold a major share in the global AI in sustainable fisheries and aquaculture market in 2023. This is attributed to the growing usage of technology for accurate predictions, improved decision-making, and optimized processes. Thus, there is a rise in the adoption of AI technology in fisheries and aquaculture industries.
The fish farms and hatcheries segment is projected to grow at a rapid rate in the global AI in sustainable fisheries and aquaculture market owing to the need to monitor and control the aquaculture environment, observe feed management, and upgrade stock management. Hence, with the growing popularity of AI-based products, there is an increase in demand for AI in sustainable fisheries and aquaculture in the end-user sector, especially in countries such as the US, Germany, the UK, China, and India.
The North America AI in the sustainable fisheries and aquaculture market is expected to register the highest market share in terms of revenue in the near future. This can be attributed to the strong focus on the environment in the region, with the increasing adoption of AI in sustainable fisheries and aquaculture with advanced computer vision systems, machine learning algorithms, and robotics. In addition, the fisheries industry in the region is focusing on the production of AI in sustainable fisheries and aquaculture to develop sustainable and environmental-friendly solutions. Growing demand for AI technology components across industries and widespread adoption of AI in sustainable fisheries and aquaculture in the production of quality products in the region are factors increasing the growth of the target market in the region. In addition, Asia Pacific is projected to grow at a rapid rate in the global AI in sustainable fisheries and aquaculture market due to growing concerns about the environment, rapid industrialization, government initiatives, and increasing funding in various industries.
| Report Attribute | Specifications |
| Market Size Value In 2023 | USD 573.3 Mn |
| Revenue Forecast In 2031 | USD 1,462.8 Mn |
| Growth Rate CAGR | CAGR of 12.7% from 2024 to 2031 |
| Quantitative Units | Representation of revenue in US$ Million and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Species, By Application, By Technology, By End User |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| Competitive Landscape | IBM Corporation, Intel Corporation, Microsoft Corporation, XpertSea Solutions Inc., Aquabyte, Antai Technology, AquacultureTalent, ImpactVision, Aquaculture Analytics, Eruvaka Technologies, AquaByte AI, Deep Trekker Inc., OptoScale AI, VAKI Aquaculture Systems Ltd., Fishtek Marine, Scanmar AS, Bluegrove Ltd., AKVA Group, BioSort AS, Kongsberg Gruppen, InnovaSea Systems, Inc., Osmo Systems, Umitron, Manolin, and Others |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
AI in Sustainable Fisheries and Aquaculture Market- By Species
AI in Sustainable Fisheries and Aquaculture Market- By Application
AI in Sustainable Fisheries and Aquaculture Market- By Technology
AI in Sustainable Fisheries and Aquaculture Market- By End-User
AI in Sustainable Fisheries and Aquaculture Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.