AI in Sustainable Fisheries and Aquaculture Market Size, Share & Trends Analysis Report, By Species (Finfish, Shellfish, Crustaceans), By Application (Aquaculture Monitoring and Control, Fish Health Monitoring and Disease Detection, Feed Management and Optimization, Water Quality Monitoring, Stock Management and Yield Prediction), By AI Technology, By End-User, By Region, Forecasts, 2024-2031

Report Id: 2763 Pages: 175 Last Updated: 25 September 2024 Format: PDF / PPT / Excel / Power BI
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Segmentation of AI in Sustainable Fisheries and Aquaculture Market

AI in Sustainable Fisheries and Aquaculture Market- By Species

  • Finfish
  • Shellfish
  • Crustaceans

fisheries

AI in Sustainable Fisheries and Aquaculture Market- By Application

  • Aquaculture Monitoring and Control
  • Fish Health Monitoring and Disease Detection
  • Feed Management and Optimization
  • Water Quality Monitoring
  • Stock Management and Yield Prediction

AI in Sustainable Fisheries and Aquaculture Market- By Technology

  • Machine Learning Algorithms
  • Computer Vision Systems
  • Natural Language Processing
  • Robotics and Automation
  • Data Analytics and Predictive Modeling

AI in Sustainable Fisheries and Aquaculture Market- By End-User

  • Fish Farms and Hatcheries
  • Seafood Processing Companies
  • Research Institutions and Universities
  • Government Agencies and Regulatory Bodies
  • Technology Providers and AI Solution Developers)

AI in Sustainable Fisheries and Aquaculture Market- By Region

North America-

  • The US
  • Canada
  • Mexico

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa
  • Rest of Middle East and Africa

Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global AI in Sustainable Fisheries and Aquaculture Market Snapshot

Chapter 4. Global AI in Sustainable Fisheries and Aquaculture Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis

Chapter 5. Market Segmentation 1: by Species Estimates & Trend Analysis
5.1. by Species & Market Share, 2019 & 2031
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Species:

5.2.1. Finfish
5.2.2. Shellfish
5.2.3. Crustaceans

Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2019 & 2031
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Application:

6.2.1. Aquaculture Monitoring and Control
6.2.2. Fish Health Monitoring and Disease Detection
6.2.3. Feed Management and Optimization
6.2.4. Water Quality Monitoring
6.2.5. Stock Management and Yield Prediction

Chapter 7. Market Segmentation 3: By AI Technology Estimates & Trend Analysis
7.1. By AI Technology & Market Share, 2019 & 2031
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following By AI Technology:

7.2.1. Machine Learning Algorithms
7.2.2. Computer Vision Systems
7.2.3. Natural Language Processing
7.2.4. Robotics and Automation
7.2.5. Data Analytics and Predictive Modeling

Chapter 8. Market Segmentation 4: By End-User Estimates & Trend Analysis
8.1. By End-User & Market Share, 2019 & 2031
8.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following By End-User:

8.2.1. Fish Farms and Hatcheries
8.2.2. Seafood Processing Companies
8.2.3. Research Institutions and Universities
8.2.4. Government Agencies and Regulatory Bodies
8.2.5. Technology Providers and AI Solution Developers

Chapter 9. AI in Sustainable Fisheries and Aquaculture Market Segmentation 5: Regional Estimates & Trend Analysis

9.1. North America
9.1.1. North America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Species, 2024-2031
9.1.2. North America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.1.3. North America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by AI Technology, 2024-2031
9.1.4. North America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
9.1.5. North America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

9.2. Europe
9.2.1. Europe AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Species, 2024-2031
9.2.2. Europe AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.2.3. Europe AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by AI Technology, 2024-2031
9.2.4. Europe AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
9.2.5. Europe AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

9.3. Asia Pacific
9.3.1. Asia Pacific AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Species, 2024-2031
9.3.2. Asia Pacific AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.3.3. Asia-Pacific Thermal Cyclers Asia-Pacific AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by AI Technology, 2024-2031
9.3.4. Asia-Pacific AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
9.3.5. Asia Pacific AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

9.4. Latin America
9.4.1. Latin America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Species, 2024-2031
9.4.2. Latin America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.4.3. Latin America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by AI Technology, 2024-2031
9.4.4. Latin America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
9.4.5. Latin America AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

9.5. Middle East & Africa
9.5.1. Middle East & Africa AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Species, 2024-2031
9.5.2. Middle East & Africa AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.5.3. Middle East & Africa AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by AI Technology, 2024-2031
9.5.4. Middle East & Africa AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
9.5.5. Middle East & Africa AI in Sustainable Fisheries and Aquaculture Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

Chapter 10. Competitive Landscape
10.1. Major Mergers and Acquisitions/Strategic Alliances
10.2. Company Profiles

10.2.1. IBM Corporation
10.2.2. Intel Corporation
10.2.3. Microsoft Corporation
10.2.4. XpertSea Solutions Inc.
10.2.5. Aquabyte
10.2.6. Antai Technology
10.2.7. AquacultureTalent
10.2.8. ImpactVision
10.2.9. Aquaculture Analytics
10.2.10. Eruvaka Technologies
10.2.11. AquaByte AI
10.2.12. Deep Trekker Inc.
10.2.13. OptoScale AI
10.2.14. VAKI Aquaculture Systems Ltd.
10.2.15. Fishtek Marine
10.2.16. Scanmar AS
10.2.17. Bluegrove Ltd.
10.2.18. AKVA Group
10.2.19. BioSort AS
10.2.20. Kongsberg Gruppen
10.2.21. InnovaSea Systems, Inc.
10.2.22. Aquabyte AI
10.2.23. Osmo Systems
10.2.24. Umitron
10.2.25. Manolin
10.2.26. Other Market Players

Research Design and Approach

This study employed a multi-step, mixed-method research approach that integrates:

  • Secondary research
  • Primary research
  • Data triangulation
  • Hybrid top-down and bottom-up modelling
  • Forecasting and scenario analysis

This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.

Secondary Research

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.

Sources Consulted

Secondary data for the market study was gathered from multiple credible sources, including:

  • Government databases, regulatory bodies, and public institutions
  • International organizations (WHO, OECD, IMF, World Bank, etc.)
  • Commercial and paid databases
  • Industry associations, trade publications, and technical journals
  • Company annual reports, investor presentations, press releases, and SEC filings
  • Academic research papers, patents, and scientific literature
  • Previous market research publications and syndicated reports

These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.

Secondary Research

Primary Research

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.

Stakeholders Interviewed

Primary interviews for this study involved:

  • Manufacturers and suppliers in the market value chain
  • Distributors, channel partners, and integrators
  • End-users / customers (e.g., hospitals, labs, enterprises, consumers, etc., depending on the market)
  • Industry experts, technology specialists, consultants, and regulatory professionals
  • Senior executives (CEOs, CTOs, VPs, Directors) and product managers

Interview Process

Interviews were conducted via:

  • Structured and semi-structured questionnaires
  • Telephonic and video interactions
  • Email correspondences
  • Expert consultation sessions

Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.

Data Processing, Normalization, and Validation

All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.

The data validation process included:

  • Standardization of units (currency conversions, volume units, inflation adjustments)
  • Cross-verification of data points across multiple secondary sources
  • Normalization of inconsistent datasets
  • Identification and resolution of data gaps
  • Outlier detection and removal through algorithmic and manual checks
  • Plausibility and coherence checks across segments and geographies

This ensured that the dataset used for modelling was clean, robust, and reliable.

Market Size Estimation and Data Triangulation

Bottom-Up Approach

The bottom-up approach involved aggregating segment-level data, such as:

  • Company revenues
  • Product-level sales
  • Installed base/usage volumes
  • Adoption and penetration rates
  • Pricing analysis

This method was primarily used when detailed micro-level market data were available.

Bottom Up Approach

Top-Down Approach

The top-down approach used macro-level indicators:

  • Parent market benchmarks
  • Global/regional industry trends
  • Economic indicators (GDP, demographics, spending patterns)
  • Penetration and usage ratios

This approach was used for segments where granular data were limited or inconsistent.

Hybrid Triangulation Approach

To ensure accuracy, a triangulated hybrid model was used. This included:

  • Reconciling top-down and bottom-up estimates
  • Cross-checking revenues, volumes, and pricing assumptions
  • Incorporating expert insights to validate segment splits and adoption rates

This multi-angle validation yielded the final market size.

Forecasting Framework and Scenario Modelling

Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.

Forecasting Methods

  • Time-series modelling
  • S-curve and diffusion models (for emerging technologies)
  • Driver-based forecasting (GDP, disposable income, adoption rates, regulatory changes)
  • Price elasticity models
  • Market maturity and lifecycle-based projections

Scenario Analysis

Given inherent uncertainties, three scenarios were constructed:

  • Base-Case Scenario: Expected trajectory under current conditions
  • Optimistic Scenario: High adoption, favourable regulation, strong economic tailwinds
  • Conservative Scenario: Slow adoption, regulatory delays, economic constraints

Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.

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Frequently Asked Questions

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

The AI in Sustainable Fisheries and Aquaculture Market is expected to grow at a 12.7% CAGR during the forecast period for 2024-2031.

IBM Corporation, Intel Corporation, Microsoft Corporation, XpertSea Solutions Inc., Aquabyte, Antai Technology, AquacultureTalent, ImpactVision, Aquac

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