세계의 농업용 AI 시장 : 기회, 성장 촉진요인, 산업 동향 분석 및 예측(2025-2034년)

The Global AI in Agriculture Market was valued at USD 4.7 billion in 2024 and is estimated to grow at a CAGR of 26.3% to reach USD 46.6 billion by 2034, driven by the increasing adoption of AI technologies to enhance agricultural productivity, optimize resource utilization, and address labor shortages in farming. AI applications, such as machine learning algorithms, predictive analytics, and automation, are being utilized to improve crop monitoring, disease detection, irrigation management, and yield forecasting.

AI technologies empower farmers to harness real-time insights from vast datasets, helping them optimize resource use, minimize crop losses, and enhance overall yield quality. This precision-driven approach improves operational efficiency while promoting sustainable agricultural methods, such as targeted irrigation, predictive pest management, and soil health monitoring. By integrating AI into everyday farm operations, producers can anticipate challenges, reduce waste, and respond quickly to environmental changes-all essential in meeting the growing global demand for food in a resource-constrained world.

The solution segment dominated the market in 2024, generated USD 3.3 billion, and is projected to reach USD 31 billion by 2034. AI-based solutions encompass a wide range of applications, including crop monitoring, disease detection, precision planting, intelligent irrigation, and yield forecasting. These software platforms analyze data from sensors, drones, and satellite imaging to provide farmers with actionable insights. The scalability and flexibility of AI solutions make them applicable across various crops, geographies, and farming practices, enhancing their affordability and effectiveness compared to individual services. Most AI agricultural solutions are cloud-based and user-friendly, facilitating easy implementation on farms of any size.

Machine learning (ML) held a significant market share of 50% in 2024 and is expected to experience substantial growth. ML algorithms excel at processing large volumes of structured and unstructured data in agriculture, enabling accurate predictions. ML is extensively applied in yield prediction, disease detection, and pest infestation forecasting. These models improve over time as new data is accumulated, making ML a versatile technology that underpins many AI-driven agricultural solutions. From intelligent irrigation and precision farming to market forecasting and automated machinery, most AI systems rely on ML algorithms, enabling real-time decision-making based on live and historical data streams.

North America AI in Agriculture Market held a 36% share in 2024. The U.S. is a global leader in technological innovation, particularly in artificial intelligence and precision agriculture. Major technology firms have invested in AI and machine learning to develop agricultural productivity solutions. The country also boasts a strong research and development ecosystem, with universities and government programs driving agri-tech advancements. These factors, combined with high investments and capabilities, position the U.S. at the forefront of AI applications in agriculture, facilitating its leadership in the global market.

Key players operating in the AI in Agriculture Market include: Gamaya, Corteva, John Deere, Taranis, aWhere, Trimble, IBM, Microsoft, and Bayer Crop Science (Climate LLC). These companies are actively developing and deploying AI-driven solutions to enhance agricultural practices and address the challenges faced by the farming industry. To strengthen their presence in the AI in agriculture market, companies are focusing on several strategic initiatives. These include investing in research and development to create innovative AI solutions tailored to the specific needs of farmers. Collaborations and partnerships with agricultural organizations, research institutions, and government agencies are being pursued to develop and implement AI-driven solutions that address broader challenges such as food security, sustainability, and climate change. Expanding their global footprint by entering new markets and establishing a presence in key regions is another strategy to capture a larger market share.

Table of Contents

Chapter 1 Methodology & Scope

• 1.1 Research design
  • 1.1.1 Research approach
  • 1.1.2 Data collection methods
• 1.2 Base estimates and calculations
  • 1.2.1 Base year calculation
  • 1.2.2 Key trends for market estimates
• 1.3 Forecast model
• 1.4 Primary research & validation
  • 1.4.1 Primary sources
  • 1.4.2 Data mining sources
• 1.5 Market definitions

Chapter 2 Executive Summary

• 2.1 Industry 360⁰ synopsis, 2021 - 2034

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
    • 3.1.1.1 Technology providers
    • 3.1.1.2 Agri-tech equipment manufacturers
    • 3.1.1.3 Crop input companies
    • 3.1.1.4 IOT sensors and image solution providers
    • 3.1.1.5 Agri-tech startups and data integrators
  • 3.1.2 Profit margin analysis
• 3.2 Impact of Trump administration tariffs
  • 3.2.1 Impact on trade
    • 3.2.1.1 Trade volume disruptions
    • 3.2.1.2 Retaliatory measures
  • 3.2.2 Impact on the Industry
    • 3.2.2.1 Price volatility in key materials
    • 3.2.2.2 Supply chain restructuring
    • 3.2.2.3 Price transmission to end markets
  • 3.2.3 Strategic industry responses
    • 3.2.3.1 Supply chain reconfiguration
    • 3.2.3.2 Pricing and product strategies
• 3.3 Technology & innovation landscape
• 3.4 Key news & initiatives
• 3.5 Cost breakdown analysis
• 3.6 Pricing analysis
  • 3.6.1 Product
  • 3.6.2 Region
• 3.7 Patent analysis
• 3.8 Regulatory landscape
• 3.9 Impact forces
  • 3.9.1 Growth drivers
    • 3.9.1.1 Rising demand for precision farming
    • 3.9.1.2 Labor shortages and need for automation
    • 3.9.1.3 Government initiatives and funding support
    • 3.9.1.4 Climate change and the need for risk mitigation
  • 3.9.2 Industry pitfalls & challenges
    • 3.9.2.1 High initial cost of implementing AI technologies
    • 3.9.2.2 Lack of infrastructure and connectivity
• 3.10 Growth potential analysis
• 3.11 Porter's analysis
• 3.12 PESTEL analysis

Chapter 4 Competitive Landscape, 2024

• 4.1 Introduction
• 4.2 Company market share analysis
• 4.3 Competitive positioning matrix
• 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Mn)

• 5.1 Key trends
• 5.2 Solution
• 5.3 Service

Chapter 6 Market Estimates & Forecast, By Technology, 2021 - 2034 ($Mn)

• 6.1 Key trends
• 6.2 Machine learning
• 6.3 Computer vision
• 6.4 Predictive analysis

Chapter 7 Market Estimates & Forecast, By Application, 2021 - 2034 ($Mn)

• 7.1 Key trends
• 7.2 Crop and soil monitoring
• 7.3 Livestock health monitoring
• 7.4 Intelligent spraying
• 7.5 Precision farming
• 7.6 Agriculture robot
• 7.7 Weather data and forecast
• 7.8 Others

Chapter 8 Market Estimates & Forecast, By Deployment mode, 2021 - 2034 ($Mn)

• 8.1 Key trends
• 8.2 Cloud-based
• 8.3 On-premises

Chapter 9 Market Estimates & Forecast, By Farm Size, 2021 - 2034 ($Mn)

• 9.1 Key trends
• 9.2 Small farm
• 9.3 Mid-sized farm
• 9.4 Large farm

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2034 ($Mn, Units)

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

Chapter 11 Company Profiles

• 11.1 AWhere
• 11.2 Bayer Crop Science (Climate LLC)
• 11.3 Benson Hill Biosystems
• 11.4 Blue River Technology
• 11.5 Bluewhite
• 11.6 Carbon Robotics
• 11.7 Corteva Agriscience
• 11.8 Cropin
• 11.9 ec2ce
• 11.10 Ever.Ag (includes Cainthus Corp)
• 11.11 FarmWise
• 11.12 Gamaya
• 11.13 Hippo Harvest
• 11.14 IBM
• 11.15 John Deere
• 11.16 Microsoft
• 11.17 Taranis
• 11.18 Trimble
• 11.19 Tule Technologies
• 11.20 Valmont Industries (Prospera Technologies)