AI in Agriculture Market - Global Size, Share, Trend Analysis, Opportunity and Forecast, 2019–2030, Segmented By Offerings (Hardware, Software, AI-as-a-Service (AIaaS), Services); By Technology (Machine Learning (ML), Computer Vision, Predictive Analytics, Natural Language Processing (NLP)); By Deployment (Cloud, On-Premises, Hybrid); By Applications (Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots, Labor Management, Crop Management, Irrigation Management); By End User (Farmers/Growers, Agriculture Cooperatives, Food Processing Companies); By Region (North America, Europe, Asia Pacific, Latin America, Middle East and Africa)
Global AI in Agriculture Market Size Exploding 4.1X at Accelerating CAGR of 11.67% to Touch Whopping USD 8.34 Billion by 2030
Global AI in Agriculture Market is booming primarily due to a heightened focus on strengthening food security by enhancing food production, governments’ increasingly supportive initiatives and funding, and rapid adoption of technological advancements in agriculture.
BlueWeave Consulting, a leading strategic consulting and market research firm, in its recent study, estimated Global AI in Agriculture Market size by value at USD 2.03 billion in 2023. During the forecast period between 2024 and 2030, BlueWeave expects Global AI in Agriculture Market size to expand at a CAGR of 11.67% reaching a value of USD 8.34 billion by 2030. The AI in Global Agriculture Market is propelled by its increasing role in addressing pressing challenges like climate change and the rising demand driven by population growth. With the global population projected to reach 9.8 billion by 2050 and with the limited arable land, AI integration becomes essential for scaling food production. AI, supported by IoT and big data, is transforming agriculture through enhanced crop monitoring, precision farming, predictive analytics, and yield optimization. Innovations like AI-powered drones, robots, and wireless sensors are deployed for tasks, such as predictive analysis, pest detection, and soil monitoring. Collaborations such as Microsoft's AI Sowing App and initiatives by companies like Nature Fresh Farms exemplify AI's transformative impact, improving efficiency and predicting harvest yields. The widespread adoption not only enhances operational efficiency but also drives demand for IoT devices, solidifying AI's role in sustainable agriculture in the coming years.
Opportunity - Development of innovative AI applications for vertical farming, aquaculture, and livestock management
As the world population continues to grow and food demand escalates, the need for efficient farming methods becomes increasingly critical to maximize production on limited land. Artificial Intelligence (AI) is at the forefront of this agricultural revolution, transforming traditional practices across vertical farming, aquaculture, and livestock management. AI-driven applications are tailored to each sector's unique challenges, from monitoring crop health and automating feeding systems in vertical farms, to overseeing water quality and fish health in aquaculture, and implementing precision feeding and health monitoring in livestock management. These advancements not only enhance operational efficiency but also promote sustainable farming practices, crucial for meeting future food demands while minimizing environmental impact. Optima Planta, founded in 2017 by Lennart Sör, exemplifies this trend with their AI and bio-informatics-driven approach to vertical farming. Based in Uppsala, their research and development facility pioneers AI technologies to achieve substantial efficiency gains in Controlled Environment Agriculture (CEA), projecting up to 100% improvement. Optima Planta's ADA (Agriculture Data Assistant) system optimizes environmental conditions with sensors for pH, humidity, and temperature, enhancing yields by 25%–50% under controlled parameters. Collaborations with Swedish universities drive ongoing innovation, underscoring AI's transformative potential in modern farming practices as Optima Planta seeks partnerships and investment to scale their technology.
North America at Forefront in Adopting AI for Agriculture
North America, the home to technologically advanced United States and Canada, is the leading region in Global AI in Agriculture Market. During the forecast period, the region is also going to sustain its leadership position due to increasing investments in automation, adoption of IoT, and governments’ increasing supportive measures for domestic AI development. Agricultural technology firms are actively exploring AI solutions, deploying drones, robots, and intelligent monitoring systems.
Impact of Escalating Geopolitical Tensions on Global AI in Agriculture Market
Intensifying geopolitical tensions can have a multifaceted impact on Global AI in Agriculture Market. Conflicts between countries disrupt supply chains, impede international collaborations, and heighten regulatory uncertainties, leading to market volatility. Nations experiencing instability may prioritize domestic food security over technological imports, thereby altering market dynamics and growth trajectories. Moreover, increased global uncertainty may erode investor confidence, thereby restricting funding for AI innovations in agriculture. As geopolitical tensions continue, strategic partnerships and regulatory frameworks assume critical roles in navigating the evolving landscape of AI adoption in agriculture worldwide.
Competitive Landscape
Global AI in Agriculture Market is highly fragmented, with numerous players serving the market. The key players dominating Global AI in Agriculture Market include Microsoft Corporation, IBM Corporation, Granular Inc, Prospera Technologies Ltd, Gamaya SA, ec2ce, PrecisionHawk Inc, Cainthus Corp, Tule Technologies Inc, Deere & Company, and AgEagle Aerial Systems Inc. The key marketing strategies adopted by the players are facility expansion, product diversification, alliances, collaborations, partnerships, and acquisitions to expand their customer reach and gain a competitive edge in the overall market.
The report's in-depth analysis provides information about growth potential, upcoming trends, and Global AI in Agriculture Market statistics. It also highlights the factors driving forecasts of total market size. The report promises to provide recent technology trends in Global AI in Agriculture Market along with industry insights to help decision-makers make sound strategic decisions. Furthermore, the report also analyses the growth drivers, challenges, and competitive dynamics of the market.
- 1. Research Framework
- 1.1. Research Objective
- 1.2. Product Overview
- 1.3. Market Segmentation
- 2. Executive Summary
- 3. Global AI in Agriculture Market Insights
- 3.1. Industry Value Chain Analysis
- 3.2. DROC Analysis
- 3.2.1. Growth Drivers
- 3.2.1.1. Growing demand for food production
- 3.2.1.2. Government initiatives and funding
- 3.2.1.3. Advancements in technology
- 3.2.2. Restraints
- 3.2.2.1. High initial investment
- 3.2.2.2. Limited technical expertise
- 3.2.2.3. Data privacy and security concerns
- 3.2.3. Opportunities
- 3.2.3.1. Expansion into emerging markets
- 3.2.3.2. Development of new ai applications for vertical farming, aquaculture, and livestock management.
- 3.2.3.3. Improved supply chain management
- 3.2.4. Challenges
- 3.2.4.1. Integration with existing systems
- 3.2.4.2. Regulatory and ethical issues
- 3.2.5. Technological Advancements/Recent Developments
- 3.3. Regulatory Framework
- 3.4. Porter’s Five Forces Analysis
- 3.4.1. Bargaining Power of Suppliers
- 3.4.2. Bargaining Power of Buyers
- 3.4.3. Threat of New Entrants
- 3.4.4. Threat of Substitutes
- 3.4.5. Intensity of Rivalry
- 4. Global AI in Agriculture Market: Marketing Strategies
- 5. Global AI in Agriculture Market: Pricing Analysis
- 6. Global AI in Agriculture Market: Geography Analysis
- 6.1. Global AI in Agriculture Market, Geographical Analysis, 2023
- 6.2. Global AI in Agriculture, Market Attractiveness Analysis, 2024–2030
- 7. Global AI in Agriculture Market Overview
- 7.1. Market Size & Forecast, 2019–2030
- 7.1.1. By Value (USD Billion)
- 7.2. Market Share and Forecast
- 7.2.1. By Offerings
- 7.2.1.1. Hardware
- 7.2.1.2. Software
- 7.2.1.3. AI-as-a-Service (AIaaS)
- 7.2.1.4. Service
- 7.2.2. By Technology
- 7.2.2.1. Machine Learning
- 7.2.2.2. Computer Vision
- 7.2.2.3. Predictive Analytics
- 7.2.2.4. Natural Language Processing (NLP)
- 7.2.3. By Deployment
- 7.2.3.1. Cloud
- 7.2.3.2. On-Premises
- 7.2.3.3. Hybrid
- 7.2.4. By Applications
- 7.2.4.1. Precision Farming
- 7.2.4.2. Livestock Monitoring
- 7.2.4.3. Drone Analytics
- 7.2.4.4. Agriculture Robots
- 7.2.4.5. Labor Management
- 7.2.4.6. Crop Management
- 7.2.4.7. Irrigation Management
- 7.2.4.8. Others
- 7.2.5. By End User
- 7.2.5.1. Farmers/Growers
- 7.2.5.2. Agriculture Cooperatives
- 7.2.5.3. Food Processing Companies
- 7.2.5.4. Others
- 7.2.6. By Region
- 7.2.6.1. North America
- 7.2.6.2. Europe
- 7.2.6.3. Asia Pacific (APAC)
- 7.2.6.4. Latin America (LATAM)
- 7.2.6.5. Middle East and Africa (MEA)
- 8. North America AI in Agriculture Market
- 8.1. Market Size & Forecast, 2019–2030
- 8.1.1. By Value (USD Billion)
- 8.2. Market Share & Forecast
- 8.2.1. By Offerings
- 8.2.2. By Technology
- 8.2.3. By Deployment
- 8.2.4. By Applications
- 8.2.5. By End User
- 8.2.6. By Country
- 8.2.6.1. United States
- 8.2.6.1.1. By Offerings
- 8.2.6.1.2. By Technology
- 8.2.6.1.3. By Deployment
- 8.2.6.1.4. By Applications
- 8.2.6.1.5. By End User
- 8.2.6.2. Canada
- 8.2.6.2.1. By Offerings
- 8.2.6.2.2. By Technology
- 8.2.6.2.3. By Deployment
- 8.2.6.2.4. By Applications
- 8.2.6.2.5. By End User
- 9. Europe AI in Agriculture Market
- 9.1. Market Size & Forecast, 2019–2030
- 9.1.1. By Value (USD Billion)
- 9.2. Market Share & Forecast
- 9.2.1. By Offerings
- 9.2.2. By Technology
- 9.2.3. By Deployment
- 9.2.4. By Applications
- 9.2.5. By End User
- 9.2.6. By Country
- 9.2.6.1. Germany
- 9.2.6.1.1. By Offerings
- 9.2.6.1.2. By Technology
- 9.2.6.1.3. By Deployment
- 9.2.6.1.4. By Applications
- 9.2.6.1.5. By End User
- 9.2.6.2. United Kingdom
- 9.2.6.2.1. By Offerings
- 9.2.6.2.2. By Technology
- 9.2.6.2.3. By Deployment
- 9.2.6.2.4. By Applications
- 9.2.6.2.5. By End User
- 9.2.6.3. Italy
- 9.2.6.3.1. By Offerings
- 9.2.6.3.2. By Technology
- 9.2.6.3.3. By Deployment
- 9.2.6.3.4. By Applications
- 9.2.6.3.5. By End User
- 9.2.6.4. France
- 9.2.6.4.1. By Offerings
- 9.2.6.4.2. By Technology
- 9.2.6.4.3. By Deployment
- 9.2.6.4.4. By Applications
- 9.2.6.4.5. By End User
- 9.2.6.5. Spain
- 9.2.6.5.1. By Offerings
- 9.2.6.5.2. By Technology
- 9.2.6.5.3. By Deployment
- 9.2.6.5.4. By Applications
- 9.2.6.5.5. By End User
- 9.2.6.6. Belgium
- 9.2.6.6.1. By Offerings
- 9.2.6.6.2. By Technology
- 9.2.6.6.3. By Deployment
- 9.2.6.6.4. By Applications
- 9.2.6.6.5. By End User
- 9.2.6.7. Russia
- 9.2.6.7.1. By Offerings
- 9.2.6.7.2. By Technology
- 9.2.6.7.3. By Deployment
- 9.2.6.7.4. By Applications
- 9.2.6.7.5. By End User
- 9.2.6.8. The Netherlands
- 9.2.6.8.1. By Offerings
- 9.2.6.8.2. By Technology
- 9.2.6.8.3. By Deployment
- 9.2.6.8.4. By Applications
- 9.2.6.8.5. By End User
- 9.2.6.9. Rest of Europe
- 9.2.6.9.1. By Offerings
- 9.2.6.9.2. By Technology
- 9.2.6.9.3. By Deployment
- 9.2.6.9.4. By Applications
- 9.2.6.9.5. By End User
- 10. Asia Pacific AI in Agriculture Market
- 10.1. Market Size & Forecast, 2019–2030
- 10.1.1. By Value (USD Billion)
- 10.2. Market Share & Forecast
- 10.2.1. By Offerings
- 10.2.2. By Technology
- 10.2.3. By Deployment
- 10.2.4. By Applications
- 10.2.5. By End User
- 10.2.6. By Country
- 10.2.6.1. China
- 10.2.6.1.1. By Offerings
- 10.2.6.1.2. By Technology
- 10.2.6.1.3. By Deployment
- 10.2.6.1.4. By Applications
- 10.2.6.1.5. By End User
- 10.2.6.2. India
- 10.2.6.2.1. By Offerings
- 10.2.6.2.2. By Technology
- 10.2.6.2.3. By Deployment
- 10.2.6.2.4. By Applications
- 10.2.6.2.5. By End User
- 10.2.6.3. Japan
- 10.2.6.3.1. By Offerings
- 10.2.6.3.2. By Technology
- 10.2.6.3.3. By Deployment
- 10.2.6.3.4. By Applications
- 10.2.6.3.5. By End User
- 10.2.6.4. South Korea
- 10.2.6.4.1. By Offerings
- 10.2.6.4.2. By Technology
- 10.2.6.4.3. By Deployment
- 10.2.6.4.4. By Applications
- 10.2.6.4.5. By End User
- 10.2.6.5. Australia & New Zealand
- 10.2.6.5.1. By Offerings
- 10.2.6.5.2. By Technology
- 10.2.6.5.3. By Deployment
- 10.2.6.5.4. By Applications
- 10.2.6.5.5. By End User
- 10.2.6.6. Indonesia
- 10.2.6.6.1. By Offerings
- 10.2.6.6.2. By Technology
- 10.2.6.6.3. By Deployment
- 10.2.6.6.4. By Applications
- 10.2.6.6.5. By End User
- 10.2.6.7. Malaysia
- 10.2.6.7.1. By Offerings
- 10.2.6.7.2. By Technology
- 10.2.6.7.3. By Deployment
- 10.2.6.7.4. By Applications
- 10.2.6.7.5. By End User
- 10.2.6.8. Singapore
- 10.2.6.8.1. By Offerings
- 10.2.6.8.2. By Technology
- 10.2.6.8.3. By Deployment
- 10.2.6.8.4. By Applications
- 10.2.6.8.5. By End User
- 10.2.6.9. Vietnam
- 10.2.6.9.1. By Offerings
- 10.2.6.9.2. By Technology
- 10.2.6.9.3. By Deployment
- 10.2.6.9.4. By Applications
- 10.2.6.9.5. By End User
- 10.2.6.10. Rest of APAC
- 10.2.6.10.1. By Offerings
- 10.2.6.10.2. By Technology
- 10.2.6.10.3. By Deployment
- 10.2.6.10.4. By Applications
- 10.2.6.10.5. By End User
- 11. Latin America AI in Agriculture Market
- 11.1. Market Size & Forecast, 2019–2030
- 11.1.1. By Value (USD Billion)
- 11.2. Market Share & Forecast
- 11.2.1.1. By Offerings
- 11.2.1.2. By Technology
- 11.2.1.3. By Deployment
- 11.2.1.4. By Applications
- 11.2.1.5. By End User
- 11.2.2. By Country
- 11.2.2.1. Brazil
- 11.2.2.1.1. By Offerings
- 11.2.2.1.2. By Technology
- 11.2.2.1.3. By Deployment
- 11.2.2.1.4. By Applications
- 11.2.2.1.5. By End User
- 11.2.2.2. Mexico
- 11.2.2.2.1. By Offerings
- 11.2.2.2.2. By Technology
- 11.2.2.2.3. By Deployment
- 11.2.2.2.4. By Applications
- 11.2.2.2.5. By End User
- 11.2.2.3. Argentina
- 11.2.2.3.1. By Offerings
- 11.2.2.3.2. By Technology
- 11.2.2.3.3. By Deployment
- 11.2.2.3.4. By Applications
- 11.2.2.3.5. By End User
- 11.2.2.4. Peru
- 11.2.2.4.1. By Offerings
- 11.2.2.4.2. By Technology
- 11.2.2.4.3. By Deployment
- 11.2.2.4.4. By Applications
- 11.2.2.4.5. By End User
- 11.2.2.5. Rest of LATAM
- 11.2.2.5.1. By Offerings
- 11.2.2.5.2. By Technology
- 11.2.2.5.3. By Deployment
- 11.2.2.5.4. By Applications
- 11.2.2.5.5. By End User
- 12. Middle East & Africa AI in Agriculture Market
- 12.1. Market Size & Forecast, 2019–2030
- 12.1.1. By Value (USD Billion)
- 12.2. Market Share & Forecast
- 12.2.1. By Offerings
- 12.2.2. By Technology
- 12.2.3. By Deployment
- 12.2.4. By Applications
- 12.2.5. By End User
- 12.2.6. By Country
- 12.2.6.1. Saudi Arabia
- 12.2.6.1.1. By Offerings
- 12.2.6.1.2. By Technology
- 12.2.6.1.3. By Deployment
- 12.2.6.1.4. By Applications
- 12.2.6.1.5. By End User
- 12.2.6.2. UAE
- 12.2.6.2.1. By Offerings
- 12.2.6.2.2. By Technology
- 12.2.6.2.3. By Deployment
- 12.2.6.2.4. By Applications
- 12.2.6.2.5. By End User
- 12.2.6.3. Qatar
- 12.2.6.3.1. By Offerings
- 12.2.6.3.2. By Technology
- 12.2.6.3.3. By Deployment
- 12.2.6.3.4. By Applications
- 12.2.6.3.5. By End User
- 12.2.6.4. Kuwait
- 12.2.6.4.1. By Offerings
- 12.2.6.4.2. By Technology
- 12.2.6.4.3. By Deployment
- 12.2.6.4.4. By Applications
- 12.2.6.4.5. By End User
- 12.2.6.5. South Africa
- 12.2.6.5.1. By Offerings
- 12.2.6.5.2. By Technology
- 12.2.6.5.3. By Deployment
- 12.2.6.5.4. By Applications
- 12.2.6.5.5. By End User
- 12.2.6.6. Nigeria
- 12.2.6.6.1. By Offerings
- 12.2.6.6.2. By Technology
- 12.2.6.6.3. By Deployment
- 12.2.6.6.4. By Applications
- 12.2.6.6.5. By End User
- 12.2.6.7. Algeria
- 12.2.6.7.1. By Offerings
- 12.2.6.7.2. By Technology
- 12.2.6.7.3. By Deployment
- 12.2.6.7.4. By Applications
- 12.2.6.7.5. By End User
- 12.2.6.8. Rest of MEA
- 12.2.6.8.1. By Offerings
- 12.2.6.8.2. By Technology
- 12.2.6.8.3. By Deployment
- 12.2.6.8.4. By Applications
- 12.2.6.8.5. By End User
- 13. Competitive Landscape
- 13.1. List of Key Players and Their Applications
- 13.2. Global AI in Agriculture Company Market Share Analysis, 2023
- 13.3. Competitive Benchmarking, By Operating Parameters
- 13.4. Key Strategic Developments (Mergers, Acquisitions, Partnerships, etc.)
- 14. Impact of Escalating Geopolitical Tensions on Global AI in Agriculture Market
- 15. Company Profiles (Company Overview, Financial Matrix, Competitive Landscape, Key Personnel, Key Competitors, Contact Address, Strategic Outlook, and SWOT Analysis)
- 15.1. Microsoft Corporation
- 15.2. IBM Corporation
- 15.3. Granular Inc.
- 15.4. Prospera Technologies Ltd
- 15.5. Gamaya SA
- 15.6. ec2ce
- 15.7. PrecisionHawk Inc.
- 15.8. Cainthus Corp.
- 15.9. Tule Technologies Inc.
- 15.10. Deere & Company
- 15.11. AgEagle Aerial Systems Inc
- 15.12. Other Prominent Players
- 16. Key Strategic Recommendations
- 17. Research Methodology
- 17.1. Qualitative Research
- 17.1.1. Primary & Secondary Research
- 17.2. Quantitative Research
- 17.3. Market Breakdown & Data Triangulation
- 17.3.1. Secondary Research
- 17.3.2. Primary Research
- 17.4. Breakdown of Primary Research Respondents, By Region
- 17.5. Assumptions & Limitations
- *Financial information of non-listed companies can be provided as per availability.
- **The segmentation and the companies are subject to modifications based on in-depth secondary research for the final deliverable
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