농업 분야 AI 시장 기회, 성장 촉진요인, 업계 동향 분석 및 예측(2026-2035년)

The Global AI in Agriculture Market was valued at USD 4.2 billion in 2025 and is estimated to grow at a CAGR of 18.8% to reach USD 23.9 billion by 2035.

The market is expanding rapidly as the agriculture industry embraces digital transformation and precision farming technologies to improve productivity and resource efficiency. Artificial intelligence is reshaping modern farming by enabling data-driven decision-making through advanced analytics and intelligent automation. AI-powered platforms process real-time information collected from multiple connected sources, allowing farmers to improve crop monitoring, irrigation management, yield forecasting, livestock supervision, pest identification, disease detection, and soil health management. Increasing pressure to enhance food production while addressing labor shortages, changing climate conditions, and sustainability goals is accelerating the adoption of AI-enabled agricultural solutions worldwide. Growing investments in digital farm management, precision agriculture, controlled-environment farming, and smart agricultural infrastructure are further strengthening market growth. Agricultural businesses are increasingly implementing AI technologies to optimize field operations, improve resource utilization, automate critical farming activities, and support more efficient production planning. AI is also playing a significant role in advancing sustainable farming practices by helping reduce unnecessary resource consumption, improve water efficiency, enhance soil productivity, lower agricultural emissions, and support long-term environmental resilience.

The hardware segment accounted for 45.9% share in 2025 and is expected to grow at a CAGR of 18.1% through 2035. Hardware remains the largest segment because it provides the essential physical infrastructure required to support AI-powered farming operations. Increasing investments in connected agricultural equipment, intelligent sensing technologies, automated machinery, and precision farming systems continue to drive demand for advanced hardware solutions. As commercial farming operations accelerate digital transformation, spending on intelligent field equipment and connected devices continues to exceed investments in software platforms.

The machine learning and deep learning segment held a 32% share in 2025 and is projected to register a CAGR of 19.1% between 2026 and 2035. This segment maintains its leading position because advanced learning algorithms enable accurate analysis of large volumes of agricultural data, supporting better decision-making across farming operations. The ability to continuously improve prediction accuracy, optimize operational planning, and generate intelligent recommendations is encouraging wider adoption of machine learning and deep learning technologies throughout the agriculture sector.

United States AI in Agriculture Market accounted for 86.5% share in 2025, generating USD 1.3 billion. Market expansion is supported by the country's advanced agricultural sector, increasing adoption of precision farming technologies, and growing investments in artificial intelligence, cloud computing, and digital agriculture solutions. Commercial farming operations continue to implement AI-powered technologies to improve operational efficiency, optimize resource management, enhance crop productivity, and strengthen long-term agricultural sustainability. Continued innovation by technology providers and agricultural equipment manufacturers is also contributing to broader commercialization of AI-enabled farming solutions across the country.

Major companies operating in the Global AI in Agriculture Market include Accenture, AGCO, CNH Industrial, DJI Agriculture, John Deere, Kubota, Microsoft, The Climate, Trimble Agriculture, and XAG. Companies operating in the AI in Agriculture Market are strengthening their competitive positions by investing in advanced artificial intelligence technologies, smart farming platforms, and precision agriculture solutions. Many organizations are expanding their product portfolios with intelligent analytics, automation capabilities, connected agricultural equipment, and cloud-based farm management systems that improve productivity and resource efficiency. Strategic partnerships, acquisitions, and collaborations with technology providers, equipment manufacturers, and agricultural organizations continue to accelerate innovation and expand market reach. Companies are also increasing research and development investments to improve data analytics, machine learning capabilities, autonomous farming technologies, and digital integration.

Table of Contents

Chapter 1 Methodology

• 1.1 Research approach
• 1.2 Quality Commitments
  • 1.2.1 GMI AI policy & data integrity commitment
    • 1.2.1.1 Source consistency protocol
• 1.3 Research Trail & Confidence Scoring
  • 1.3.1 Research Trail Components
  • 1.3.2 Scoring Components
• 1.4 Data Collection
  • 1.4.1 Partial list of primary sources
• 1.5 Data mining sources
  • 1.5.1 Paid sources
    • 1.5.1.1 Sources, by region
• 1.6 Base estimates and calculations
  • 1.6.1 Base year calculation
• 1.7 Forecast Model
  • 1.7.1 Quantified market impact analysis
    • 1.7.1.1 Mathematical impact of growth parameters on forecast
• 1.8 Research transparency addendum
  • 1.8.1 Source attribution framework
  • 1.8.2 Quality assurance metrics
  • 1.8.3 Our commitment to trust

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis, 2022 – 2035
• 2.2 Key market trends
  • 2.2.1 Offering
  • 2.2.2 Technology
  • 2.2.3 Application
  • 2.2.4 Deployment Mode
  • 2.2.5 End Use
  • 2.2.6 Regional
• 2.3 TAM Analysis, 2026-2035
• 2.4 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
  • 3.1.2 Profit margin analysis
  • 3.1.3 Cost structure
  • 3.1.4 Value addition at each stage
  • 3.1.5 Factor affecting the value chain
  • 3.1.6 Disruptions
• 3.2 Industry impact forces
  • 3.2.1 Growth drivers
    • 3.2.1.1 Rising adoption of precision agriculture and smart farming
    • 3.2.1.2 Increasing labor shortages driving farm automation
    • 3.2.1.3 Growing availability of agricultural data and IOT connectivity
    • 3.2.1.4 Increasing focus on sustainable agriculture and resource optimization
    • 3.2.1.5 Government support and rising investments in Agtech and ai innovation
  • 3.2.2 Industry pitfalls and challenges
    • 3.2.2.1 High initial investment and implementation costs
    • 3.2.2.2 Limited digital infrastructure and rural connectivity
  • 3.2.3 Market opportunities
    • 3.2.3.1 Expansion of generative ai-powered farm advisory platforms
    • 3.2.3.2 Increasing adoption of ai-enabled autonomous agricultural machinery
    • 3.2.3.3 Growing integration of ai with climate-smart and regenerative agriculture
    • 3.2.3.4 Rising demand for ai-based agricultural supply chain traceability
    • 3.2.3.5 Rapid digital transformation across emerging agricultural economies
• 3.3 Growth potential analysis
• 3.4 Technology and Innovation landscape
  • 3.4.1 Current technological trends
  • 3.4.2 Emerging technologies
• 3.5 Pricing Analysis (Driven by Primary Research)
  • 3.5.1 Historical Price Trend Analysis, 2022-2025
  • 3.5.2 Pricing Strategy by Player Type (Premium / Value / Cost-plus)
• 3.6 Regulatory landscape
  • 3.6.1 North America
    • 3.6.1.1 U.S.: AI Governance, Precision Agriculture Initiatives & USDA Digital Agriculture Programs
    • 3.6.1.2 Canada: Artificial Intelligence Regulation, Smart Farming Policies & Sustainable Agriculture Frameworks
  • 3.6.2 Europe
    • 3.6.2.1 Germany: AI Act Compliance, Digital Farming Strategy & Precision Agriculture Policies
    • 3.6.2.2 UK: AI Regulation, Agri-Tech Strategy & Autonomous Agricultural Machinery Framework
    • 3.6.2.3 France: Digital Agriculture Roadmap & AI Innovation in Sustainable Farming
    • 3.6.2.4 Italy: Smart Agriculture Initiatives, Farm Digitalization & AI Adoption Programs
  • 3.6.3 Asia Pacific
    • 3.6.3.1 India: Digital Agriculture Mission, AI for Agriculture Initiatives & Drone Regulations
    • 3.6.3.2 China: Smart Agriculture Development Plan, AI Innovation Policies & Agricultural Modernization
    • 3.6.3.3 Japan: Society 5.0, Smart Agriculture Promotion & Agricultural Robotics Framework
    • 3.6.3.4 Australia: National AI Strategy, Digital Agriculture Programs & Precision Farming Standards
  • 3.6.4 Latin America
    • 3.6.4.1 Brazil: National IoT Plan, Digital Agriculture Strategy & Precision Farming Programs
    • 3.6.4.2 Mexico: Smart Agriculture Policies, Agricultural Digitalization & AI Innovation Initiatives
    • 3.6.4.3 Argentina: Precision Agriculture Promotion, AgTech Innovation & Digital Farming Frameworks
  • 3.6.5 Middle East & Africa (MEA)
    • 3.6.5.1 Israel: AI Innovation, Precision Agriculture Technologies & Smart Irrigation Policies
    • 3.6.5.2 South Africa: Digital Agriculture Strategy & AI-Enabled Agricultural Modernization
    • 3.6.5.3 Kenya: Digital Agriculture Strategy, AI Advisory Services & Smart Farming Initiatives
    • 3.6.5.4 UAE: National AI Strategy 2031, Smart Agriculture Programs & Food Security Initiatives
    • 3.6.5.5 Saudi Arabia: Vision 2030, Smart Agriculture Transformation & AI-Driven Food Security Program
• 3.7 Porter’s analysis
• 3.8 PESTEL analysis
• 3.9 Patent Landscape (Driven by Primary Research)
• 3.10 Use cases
  • 3.10.1 Commercialized
  • 3.10.2 Early adoption
  • 3.10.3 Pilot stage
  • 3.10.4 Emerging
• 3.11 Impact of AI & Generative AI on the Market (Driven by Primary Research)
  • 3.11.1 AI-Driven Disruption of Existing Business Models
  • 3.11.2 GenAI Use Cases & Adoption Roadmap by Segment
  • 3.11.3 Risks, Limitations & Regulatory Considerations
• 3.12 Infrastructure and deployment landscape, 2022-2025 (driven by primary research)
  • 3.12.1 AI solution deployment penetration by region and buyer segment
  • 3.12.2 Scalability constraints and infrastructure investment trends
• 3.13 Forecast assumptions & scenario analysis (Driven by Primary Research)
  • 3.13.1 Base Case - Key Macro & Industry Variables Driving CAGR
  • 3.13.2 Optimistic Scenarios - Favourable macro and industry tailwinds
  • 3.13.3 Pessimistic Scenario - Macroeconomic slowdown or industry headwinds

Chapter 4 Competitive Landscape, 2025

• 4.1 Introduction
• 4.2 Company market share analysis, 2022-2026
  • 4.2.1 North America
  • 4.2.2 Europe
  • 4.2.3 Asia Pacific
  • 4.2.4 Latin America
  • 4.2.5 MEA
• 4.3 Competitive analysis of major market players
• 4.4 Competitive positioning matrix
• 4.5 Key developments
  • 4.5.1 Mergers & acquisitions
  • 4.5.2 Partnerships & collaborations
  • 4.5.3 New Product Launches
  • 4.5.4 Expansion Plans and funding
• 4.6 Company Tier Benchmarking
  • 4.6.1 Tier Classification Criteria & Qualifying Thresholds
  • 4.6.2 Tier Positioning Matrix by Revenue, Geography & Innovation

Chapter 5 Market Estimates and Forecast, By Offering, 2022 – 2035 ($ Million)

• 5.1 Key trends
• 5.2 Hardware
  • 5.2.1 Sensors and IoT Devices
  • 5.2.2 Drones and Unmanned Aerial Vehicles (UAVs)
  • 5.2.3 Robots and Autonomous Machinery
  • 5.2.4 Imaging, Scanning, and Vision Devices
• 5.3 Software
  • 5.3.1 AI Platforms and Analytics Tools
  • 5.3.2 Farm Management Information Systems (FMIS)
  • 5.3.3 Supply Chain and Logistics Management Software
  • 5.3.4 Decision Support and Predictive Intelligence Software
• 5.4 Services
  • 5.4.1 Consulting and System Integration Services
  • 5.4.2 Managed and Subscription-Based AI Services
  • 5.4.3 Training, Support, and After-Sales Services

Chapter 6 Market Estimates and Forecast, By Technology, 2022 – 2035 ($ Million)

• 6.1 Key trends
• 6.2 Machine Learning and Deep Learning
• 6.3 Computer Vision and Image Recognition
• 6.4 Predictive Analytics and Data Science
• 6.5 Natural Language Processing (NLP)
• 6.6 Generative AI and Foundation Models

Chapter 7 Market Estimates and Forecast, By Application, 2022 – 2035 ($ Million)

• 7.1 Key trends
• 7.2 Field Production and Crop Management
  • 7.2.1 Precision Farming and Variable Rate Application
  • 7.2.2 Crop Monitoring, Disease and Pest Detection
  • 7.2.3 Yield Prediction and Optimization
  • 7.2.4 Irrigation and Water Management
  • 7.2.5 Soil Health and Fertility Management
• 7.3 Livestock and Animal Husbandry
  • 7.3.1 Health Monitoring and Disease Detection
  • 7.3.2 Feed Optimization and Nutrition Management
  • 7.3.3 Reproductive Cycle Management
• 7.4 Harvest and Post-Harvest Management
  • 7.4.1 Harvest Timing Optimization
  • 7.4.2 Post-Harvest Quality Inspection and Grading
  • 7.4.3 Food Loss and Waste Detection and Reduction
• 7.5 Storage and Cold Chain Management
  • 7.5.1 Intelligent Warehouse and Storage Monitoring
  • 7.5.2 Cold Chain Condition and Temperature Management
  • 7.5.3 Inventory Optimization and Shelf-Life Prediction
• 7.6 Supply Chain, Logistics and Distribution
  • 7.6.1 Route Optimization and Fleet Management
  • 7.6.2 Demand Forecasting and Market Intelligence
  • 7.6.3 Agri-Supply Chain Traceability and Transparency
  • 7.6.4 Last-Mile Distribution Optimization
• 7.7 Farm Management and Decision Support
  • 7.7.1 Farm Management Information Systems (FMIS)
  • 7.7.2 Agricultural Risk Assessment and Crop Insurance
  • 7.7.3 Market Price Intelligence and Trading Support

Chapter 8 Market Estimates and Forecast, By Deployment Mode, 2022 – 2035 ($ Million)

• 8.1 Key trends
• 8.2 Cloud-Based
• 8.3 On-Premise
• 8.4 Hybrid and Edge

Chapter 9 Market Estimates and Forecast, By End Use, 2022 – 2035 ($ Million)

• 9.1 Key trends
• 9.2 Large Agricultural Enterprises and Agribusinesses
• 9.3 Small and Medium Farms and Cooperatives
• 9.4 Food Processors and Packers
• 9.5 Logistics and Distribution Operators
• 9.6 Retailers and Foodservice Companies
• 9.7 Government and Research Institutions
• 9.8 Others

Chapter 10 Market Estimates & Forecast, By Region, 2022 - 2035 ($ Million)

• 10.1 Key trends
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 France
  • 10.3.4 Italy
  • 10.3.5 Spain
  • 10.3.6 Russia
  • 10.3.7 Netherlands
  • 10.3.8 Belgium
• 10.4 Asia Pacific
  • 10.4.1 China
  • 10.4.2 India
  • 10.4.3 Japan
  • 10.4.4 Australia
  • 10.4.5 South Korea
  • 10.4.6 Singapore
  • 10.4.7 Thailand
  • 10.4.8 Indonesia
• 10.5 Latin America
  • 10.5.1 Brazil
  • 10.5.2 Mexico
  • 10.5.3 Argentina
• 10.6 MEA
  • 10.6.1 South Africa
  • 10.6.2 Saudi Arabia
  • 10.6.3 UAE

Chapter 11 Company Profiles

• 11.1 Global Players
  • 11.1.1 Accenture
  • 11.1.2 AGCO
  • 11.1.3 Carbon Robotics
  • 11.1.4 CNH Industrial
  • 11.1.5 DJI Agriculture
  • 11.1.6 John Deere
  • 11.1.7 Kubota
  • 11.1.8 Microsoft
  • 11.1.9 The Climate
  • 11.1.10 Trimble Agriculture
• 11.2 Regional Players
  • 11.2.1 Ag Leader Technology
  • 11.2.2 CLAAS
  • 11.2.3 CropIn Technology
  • 11.2.4 Naio Technologies
  • 11.2.5 SDF (SAME Deutz-Fahr)
  • 11.2.6 Semios
  • 11.2.7 XAG
• 11.3 Emerging Players
  • 11.3.1 Ceres AI
  • 11.3.2 CropX Technologies
  • 11.3.3 Taranis