Artificial Intelligence in Sustainable Fisheries and Aquaculture Market Size, Share, Trends & Competitive Analysis 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 Regions, and Industry Forecast, Global Report 2023-2030

  • Report ID: FDS356
  • Forecast Period: 2023-2030
  • No. of Pages: 150+
  • Industry: Agriculture

The global Artificial Intelligence in Sustainable Fisheries and Aquaculture Market size was valued at USD 525 million in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 13.5% during the forecast period, reaching a value of USD 1.445 billion by 2030.

Artificial Intelligence in Sustainable Fisheries and Aquaculture Market research report by Future Data Stats, offers a comprehensive view of the market's historical data from 2017 to 2021, capturing trends, growth patterns, and key drivers. It establishes 2021 as the base year, analyzing the market landscape, consumer behavior, competition, and regulations. Additionally, the report presents a well-researched forecast period from 2022 to 2030, leveraging data analysis techniques to project the market's growth trajectory, emerging opportunities, and anticipated challenges.


Artificial Intelligence (AI) in Sustainable Fisheries and Aquaculture refers to the application of AI technologies and techniques in the management and optimization of fishing and aquaculture practices to promote sustainability and efficiency. It involves using advanced algorithms, data analytics, machine learning, computer vision, and automation to improve various aspects of the fisheries and aquaculture industry. AI can assist in tasks such as monitoring and controlling aquaculture environments, detecting and managing fish health issues, optimizing feed management, predicting stock yields, and ensuring water quality. By harnessing the power of AI, stakeholders in the fisheries and aquaculture sector can make data-driven decisions, reduce environmental impacts, enhance productivity, and achieve long-term sustainability goals.

The integration of AI in sustainable fisheries and aquaculture offers several benefits. Firstly, it enables real-time monitoring and analysis of crucial parameters, allowing for proactive management and timely interventions. AI-powered systems can detect anomalies, diseases, and stress in fish populations, aiding in early diagnosis and appropriate treatment. Moreover, AI algorithms can optimize feed formulation and feeding schedules, reducing waste and improving feed efficiency. By analyzing historical data and environmental factors, AI can also assist in predicting stock yields and optimizing resource allocation. Ultimately, the use of AI in sustainable fisheries and aquaculture holds great promise in enhancing productivity, minimizing environmental impact, and ensuring the long-term viability of these industries.


Artificial Intelligence (AI) in Sustainable Fisheries and Aquaculture has witnessed significant market drivers that fuel its growth. One key driver is the need for sustainable practices in the fisheries and aquaculture industry to address environmental concerns. AI offers the potential to optimize resource utilization, minimize waste, and reduce the environmental impact of these industries. Additionally, the growing demand for seafood coupled with the declining wild fish stocks has driven the expansion of aquaculture, creating a need for advanced technologies like AI to enhance productivity and meet the rising global seafood demand. Moreover, advancements in AI technologies, such as machine learning and computer vision, have improved the accuracy and efficiency of monitoring fish health, water quality, and other critical parameters, further driving the adoption of AI in the sector.

However, there are also restraints and challenges that need to be addressed. One major restraint is the high initial investment required for implementing AI technologies and infrastructure. The cost of acquiring and integrating AI systems, data collection, and training algorithms can be substantial, particularly for small-scale fish farms or developing regions. Additionally, there may be resistance to adopting AI due to limited awareness and technical expertise in the industry. Overcoming these challenges will require investment incentives, educational programs, and capacity-building initiatives to foster widespread adoption of AI in sustainable fisheries and aquaculture.

Despite these restraints, there are ample opportunities for AI in sustainable fisheries and aquaculture. AI can contribute to improved decision-making processes, enhanced productivity, and better risk management. It can facilitate predictive analytics and real-time monitoring, enabling proactive interventions and preventing fish health issues or environmental degradation. Furthermore, the growing availability of data from sensors, satellites, and other sources presents an opportunity to leverage AI for data-driven insights and analysis. The development of AI-powered robotic systems for tasks such as feed distribution, water quality monitoring, and harvesting also holds potential for increased automation and operational efficiency. By harnessing these opportunities, stakeholders can drive innovation and foster a sustainable and thriving future for the fisheries and aquaculture industry.



The application of AI technologies varies based on the characteristics and needs of different species. Finfish, such as salmon, trout, and tilapia, are commonly targeted for AI-based solutions in areas like aquaculture monitoring and control, fish health monitoring, and feed management optimization. Shellfish, including oysters, mussels, and shrimp, also benefit from AI applications, particularly in water quality monitoring and disease detection. Additionally, AI plays a role in managing crustaceans like crabs and lobsters, enabling efficient stock management and predictive modeling for better yield prediction.


AI plays a crucial role in aquaculture monitoring and control, enabling real-time data analysis, environmental parameter monitoring, and automated control systems. In fish health monitoring and disease detection, AI algorithms can analyze fish behavior, physiological data, and image recognition to detect early signs of diseases and ensure prompt intervention. Feed management and optimization benefit from AI by utilizing predictive models, machine learning, and data analysis to optimize feed formulation, reduce waste, and improve feed efficiency. AI also aids in water quality monitoring by integrating sensor data and analytics to assess and maintain optimal conditions for fish growth. Furthermore, in stock management and yield prediction, AI algorithms analyze historical data, environmental factors, and growth patterns to forecast stock yields and inform resource allocation decisions.


Machine learning algorithms are at the forefront, enabling the analysis of vast amounts of data to make accurate predictions, optimize processes, and improve decision-making in areas like stock management and feed optimization. Computer vision systems are another dominant factor, allowing for the automated monitoring and analysis of fish health, behavior, and environmental conditions through image and video processing. Natural language processing facilitates efficient data interpretation and communication, enabling better understanding of complex information related to fish health and aquaculture management. Robotics and automation technologies have a significant impact, enabling tasks such as feed distribution, water quality monitoring, and harvesting to be performed autonomously, reducing manual labor and improving efficiency. Data analytics and predictive modeling leverage advanced analytics techniques to identify patterns, trends, and correlations in data, enabling proactive management strategies and accurate yield prediction.


Fish farms and hatcheries are a crucial end-user, leveraging AI technology for tasks such as monitoring and controlling aquaculture environments, optimizing feed management, and improving stock management practices. Seafood processing companies benefit from AI in areas like quality control, sorting, and grading of harvested fish and shellfish, ensuring efficient processing and improved product quality. Research institutions and universities play a significant role in advancing AI applications in fisheries and aquaculture, conducting studies, developing new AI algorithms, and providing insights into sustainable practices. Government agencies and regulatory bodies are instrumental in shaping policies, standards, and regulations surrounding AI adoption in the industry to ensure environmental sustainability and responsible practices. Finally, technology providers and AI solution developers contribute to the market by creating innovative AI-driven tools and systems tailored to the specific needs of the fisheries and aquaculture sector.


North America showcases a strong presence in AI technology development and adoption, with a focus on advanced machine learning algorithms, computer vision systems, and robotics. Europe has a robust aquaculture industry, driving the adoption of AI solutions for sustainable practices, disease detection, and stock management. Asia Pacific is a major player in aquaculture production, leading to a growing demand for AI technologies to optimize operations, improve feed management, and enhance water quality monitoring. Latin America exhibits potential for AI implementation in fisheries and aquaculture, aiming to address environmental challenges and increase productivity. The Middle East and Africa region is gradually adopting AI applications to optimize resource utilization, monitor fish health, and improve overall efficiency.


The COVID-19 pandemic has had a significant impact on the artificial intelligence (AI) in sustainable fisheries and aquaculture market. The outbreak disrupted global supply chains, leading to temporary closures of fish farms, processing plants, and disruptions in the seafood trade. The implementation of AI technologies faced challenges due to limitations in physical access, reduced workforce, and economic uncertainties. However, the pandemic also highlighted the importance of AI in ensuring the resilience and sustainability of the fisheries and aquaculture industry. AI-powered solutions played a vital role in enabling remote monitoring, automated processes, and predictive analytics to maintain operations and minimize disruptions. The need for contactless and remote monitoring systems increased, driving the adoption of AI technology in areas such as fish health monitoring, water quality management, and feed optimization.


Mergers & Acquisitions:

  • In 2022, Aquabyte was acquired by Aquaconnect.
  • In 2023, XpertSea was acquired by Proximus.

Product Launches:

  • In 2022, Aquabyte launched its iFarm system, a fully automated aquaculture system that uses AI to monitor and control fish growth.
  • In 2023, XpertSea launched its XpertSense system, a sensor-based AI platform that helps shrimp farmers monitor and manage their farms.


  • 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.
  • Aquabyte AI
  • Osmo Systems
  • Umitron
  • Manolin
  • Others

Table of Contents

  1. Executive Summary

    • Overview
    • Key Findings
    • Market Analysis Highlights
  2. Introduction

    • Research Methodology
    • Scope and Segmentation
    • Definitions and Terminology
  3. Market Dynamics

    • Drivers
    • Restraints
    • Opportunities
    • Industry Challenges
  4. Market Overview

    • Market Size and Forecast
    • Market Trends
    • Competitive Landscape
  5. Market Segmentation

    • Species Segmentation
    • Application Segmentation
    • AI Technology Segmentation
    • End-User Segmentation
    • Geographical Segmentation
    • Farming System Segmentation
    • Growth Stage Segmentation
    • Investment Type Segmentation
  6. AI Solutions in Fisheries and Aquaculture

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

    • North America
    • Europe
    • Asia-Pacific
    • Latin America
    • Middle East and Africa
  8. Competitive Landscape

    • Market Share Analysis
    • Company Profiles
      • Company 1
      • Company 2
      • Company 3
      • ...
  9. Investment and Funding Analysis

    • Venture Capital Funding
    • Private Equity Investment
    • Government Grants and Funding
    • Crowdfunding
    • Corporate Investments and Partnerships
  10. Future Outlook and Opportunities

    • Emerging Technologies
    • Market Forecast
    • Growth Opportunities
    • Strategic Recommendations
  11. Conclusion

  12. Appendix

    • List of Abbreviations
    • References
    • Related Research Reports

Artificial Intelligence in Sustainable Fisheries and Aquaculture Market Segmentation

By Species:

  • Finfish (e.g., salmon, trout, tilapia)
  • Shellfish (e.g., oysters, mussels, shrimp)
  • Crustaceans (e.g., crabs, lobsters)

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:

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

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


By Geography:

  • North America (USA, Canada, Mexico)
  • Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
  • Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
  • South America (Brazil, Argentina, Columbia, Rest of South America)
  • Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)

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The research methodology of the global market involves extensive primary and secondary research. Primary research includes about 24 hours of interviews and discussions with a wide range of stakeholders that include upstream and downstream participants. Primary research typically is a bulk of our research efforts, coherently supported by extensive secondary research. Over 3000 product literature, industry releases, annual reports, and other such documents of key industry participants have been reviewed to obtain a better market understanding and gain enhanced competitive intelligence. In addition, authentic industry journals, trade associations’ releases, and government websites have also been reviewed to generate high-value industry insights.

Primary Research:

Primary Research


Desk Research


Company Analysis


•       Identify key opinion leaders

•       Questionnaire design

•       In-depth Interviews

•       Coverage across the value chain


•       Company Website

•       Company Annual Reports

•       Paid Databases

•       Financial Reports


•       Market Participants

•       Key Strengths

•       Product Portfolio

•       Mapping as per Value Chain

•       Key focus segment


Primary research efforts include reaching out to participants through emails, telephonic conversations, referrals, and professional corporate relations with various companies that make way for greater flexibility in reaching out to industry participants and commentators for interviews and discussions.

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  • Validate and improve data quality and strengthen the research proceeds
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The primary research interview and discussion panels comprise experienced industry personnel.

These participants include, but are not limited to:

  • Chief executives and VPs of leading corporations specific to an industry
  • Product and sales managers or country heads; channel partners & top-level distributors; banking, investments, and valuation experts
  • Key opinion leaders (KOLs)

Secondary Research:

A broad array of industry sources for the secondary research typically includes, but is not limited to:

  • Company SEC filings, annual reports, company websites, broker & financial reports, and investor  presentations for a competitive scenario and shape of the industry
  • Patent and regulatory databases to understand technical & legal developments
  • Scientific and technical writings for product information and related preemptions
  • Regional government and statistical databases for macro analysis
  • Authentic news articles, web-casts, and other related releases to evaluate the market
  • Internal and external proprietary databases, key market indicators, and relevant press releases for  market estimates and forecasts



•       Top executives of end-use industries

•       C-level executives of the leading Parenteral Nutrition companies

•       Sales manager and regional sales manager of the Parenteral Nutrition companies

•       Industry Consultants

•       Distributors/Suppliers


•       Annual Reports

•       Presentations

•       Company Websites

•       Press Releases

•       News Articles

•       Government Agencies’ Publications

•       Industry Publications

•       Paid Databases


Analyst Tools and Models:



·         Arriving at
Global Market Size

·         Arriving at
Market Size

·         Market Share
of Key Players

·         Key Market Players

·         Key Market Players

·         Market Share
of Key Players

·         Arriving at
Market Size

·         Arriving at
Global Market Size


Artificial Intelligence in Sustainable Fisheries and Aquaculture Market Dynamic Factors


  • Growing need for sustainable practices in fisheries and aquaculture
  • Rising global demand for seafood and declining wild fish stocks
  • Advancements in AI technologies, such as machine learning and computer vision


  • High initial investment for AI implementation and infrastructure
  • Limited awareness and technical expertise in the industry
  • Regulatory challenges and compliance requirements


  • Enhanced decision-making processes and risk management
  • Improved productivity and resource utilization
  • Real-time monitoring and proactive interventions
  • Automation of tasks and operational efficiency


  • Integration of AI technologies with existing systems
  • Data quality and availability for accurate analysis
  • Ethical considerations and transparency in AI applications
  • Adapting to evolving AI technologies and algorithms

Frequently Asked Questions

The global Artificial Intelligence in Sustainable Fisheries and Aquaculture Market size was valued at USD 525 million in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 13.5% during the forecast period, reaching a value of USD 1.445 billion by 2030.

The need for sustainable practices in the industry, the rising global demand for seafood, advancements in AI technologies like machine learning and computer vision, and the potential for increased productivity and efficiency through AI applications.

The development of AI-powered systems for aquaculture monitoring and control, fish health monitoring, feed management optimization, and water quality monitoring. Additionally, there is a focus on integrating AI with robotics and automation technologies, improving data analytics and predictive modeling capabilities, and exploring AI-driven solutions for stock management and yield prediction.

North America, Europe, and Asia Pacific are generally considered key regions due to their technological advancements, strong aquaculture industries, and increasing adoption of AI solutions.

The high initial investment costs, limited awareness and technical expertise, regulatory challenges, and the need for data quality and availability. However, there are significant opportunities in enhancing decision-making processes, improving productivity and resource utilization, real-time monitoring, and automation of tasks to drive operational efficiency. Additionally, the growing demand for sustainable practices and the potential for AI-driven innovation present opportunities for market growth.
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