농업용 AI 시장 보고서 : 제공별, 기술별, 용도별, 지역별(2025-2033년)

The global AI in agriculture market size reached USD 2.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 13.0 Billion by 2033, exhibiting a growth rate (CAGR) of 19.48% during 2025-2033. The market is experiencing steady growth driven by the increasing need for precision agriculture techniques to enhance crop yield, optimize resource utilization, and mitigate environmental impact, the growing adoption of IoT devices and sensors for data-driven farming practices, and the demand for advanced AI algorithms and machine learning models to analyze vast agricultural datasets.

AI in Agriculture Market Analysis:

Market Growth and Size: The market is witnessing stable growth, driven by the increasing need for precision agriculture to enhance crop yield. The market size is projected to reach billions of dollars within the next few years.

Technological Advancements: AI technologies like machine learning and computer vision are advancing rapidly. Besides, integration of IoT, drones, and remote sensing for data collection is also gaining traction.

Industry Applications: AI in Agriculture is applied across various segments, including crop monitoring, soil analysis, predictive analytics, and autonomous machinery. It is also used for livestock monitoring, disease detection, and smart farming practices.

Geographical Trends: Asia Pacific is a prominent player in the AI in Agriculture market, driven by its large agricultural sector and technological advancements. North America is also a significant contributor, particularly in the adoption of precision agriculture technologies.

Competitive Landscape: The market is characterized by a mix of established players and startups offering AI solutions for agriculture. Leading companies are investing in research and development to stay competitive.

Challenges and Opportunities: Challenges include the high initial cost of AI implementation and the need for education and training for farmers. Besides, opportunities lie in addressing global food security issues and developing sustainable agriculture practices through AI.

Future Outlook: The future of AI in Agriculture looks promising, with continued innovation and adoption expected. AI technologies will play a pivotal role in addressing the challenges of feeding a growing global population while minimizing environmental impact.

AI in Agriculture Market Trends:

Increased efficiency and productivity

One of the primary factors driving the adoption of AI in the agriculture market is the significant increase in efficiency and productivity it offers. Traditional farming methods often involve a lot of guesswork and manual labor, which can be both time-consuming and prone to errors. AI technologies, such as machine learning algorithms and data analytics, enable farmers to make more informed decisions about planting, watering, and harvesting. For example, AI-powered systems can analyze soil conditions, weather patterns, and crop health, optimizing irrigation schedules and reducing water waste. This precision farming approach leads to higher crop yields and better resource management, making agriculture more sustainable and profitable in the long term.

Labor shortage and cost reduction

Labor shortages in agriculture are a global issue, with many regions facing difficulties in securing a stable and skilled workforce for farming activities. AI and automation offer a solution to this challenge by reducing the reliance on manual labor. Robotics, for instance, can perform tasks like weeding, harvesting, and even fruit picking, which are labor-intensive and often hard to staff. Additionally, AI systems can monitor and manage large areas of farmland, reducing the need for a large on-site workforce. This automation not only addresses the labor shortage but also contributes to cost reduction. By automating routine tasks, farmers can lower their operational costs and focus their resources on more strategic aspects of farm management.

Technological advancements

The rapid advancements in AI and machine learning technologies are key drivers in the AI in Agriculture market. These technologies have evolved significantly, offering sophisticated solutions for complex agricultural challenges. Machine learning algorithms can process vast amounts of data from various sources, including weather patterns, crop health, and soil conditions, to provide precise and real-time insights for farmers. The integration of AI in agricultural machinery, like autonomous tractors and drones, enhances field operations, reducing labor costs and increasing efficiency. This continuous evolution of AI technologies makes them increasingly accessible and applicable in agriculture, thus fueling the market growth.

Need for sustainable agriculture practices

Sustainability is a major concern in modern agriculture, and AI plays a crucial role in promoting sustainable practices. AI-driven tools help in monitoring and reducing the environmental impact of farming by optimizing the use of resources. For instance, AI systems can provide precise data on the amount of water or pesticides needed for specific crop areas, minimizing wastage and environmental harm. Additionally, AI can assist in crop rotation and soil health management, which are essential for long-term sustainability. By reducing the carbon footprint and promoting eco-friendly practices, AI is not just an agricultural tool but also a means to combat climate change.

Government initiatives

Governments worldwide are recognizing the potential of AI in revolutionizing agriculture and are thus providing substantial support through initiatives and funding. These initiatives often include subsidies for AI agricultural technology, research and development grants, and educational programs for farmers. This government backing not only stimulates market growth but also encourages innovation and adoption of AI in agriculture. By providing financial and policy support, governments play a significant role in driving the AI in Agriculture market, making it more accessible and beneficial for farmers and the agricultural industry as a whole.

AI in Agriculture Industry Segmentation:

Breakup by Offering:

Software

Hardware

AI-as-a-Service

Services

Software accounts for the majority of the market share

The software segment holds the largest share in the AI in Agriculture market, driven by the increasing adoption of AI-powered farm management software, data analytics platforms, and AI-driven decision support systems by farmers and agricultural enterprises. These software solutions offer features such as crop monitoring, yield prediction, pest detection, and irrigation management, enabling precise and data-driven farming practices. With the integration of AI algorithms, farmers can make informed decisions, optimize resource utilization, and enhance crop yields while minimizing environmental impact. This segment is expected to witness sustained growth as technology providers continue to develop advanced software solutions tailored to the evolving needs of the agriculture sector.

The hardware segment encompasses AI-related physical devices and components used in agricultural applications, including sensors, drones, autonomous machinery, and AI-specific hardware accelerators. These technologies are essential for data collection, monitoring, and autonomous operations on the farm. The adoption of precision agriculture techniques has driven the demand for hardware solutions, allowing farmers to gather real-time data and automate various farming tasks. As technological advancements continue to enhance hardware capabilities, the hardware segment is poised for significant growth in the AI in agriculture market.

AI-as-a-Service is a segment that offers AI capabilities through cloud-based platforms and services to agricultural stakeholders. It enables farmers and organizations to access AI tools, machine learning models, and data analytics on a subscription or pay-per-use basis. This approach lowers the entry barriers for adopting AI in agriculture, particularly for smaller farms and businesses with limited IT infrastructure. AI-as-a-Service provides scalability and flexibility, allowing users to harness the power of AI without significant upfront investments in software or hardware. This segment is witnessing increased adoption due to its convenience and cost-effective nature.

The services segment includes consulting, implementation, training, and support services provided by AI solution providers and agricultural technology companies. Services play a crucial role in assisting farmers and organizations in the successful adoption and utilization of AI technologies in agriculture. Consulting services help assess specific needs, while implementation and training services ensure a smooth transition to AI-driven practices. Ongoing support services are essential for troubleshooting and optimizing AI systems. As the AI in Agriculture market matures, the demand for specialized services is expected to grow, providing added value to customers seeking to maximize the benefits of AI technologies in farming operations.

Breakup by Technology:

Machine Learning

Computer Vision

Predictive Analytics

Machine learning holds the largest share in the industry

Machine learning stands out as the largest segment. Machine learning algorithms are widely used for data analysis, pattern recognition, and decision-making in agriculture. They enable farmers to harness the power of data from various sources, including sensors, satellites, and drones, to make informed decisions about planting, irrigation, pest control, and crop health. Machine learning models help optimize resource allocation, predict crop yields, and even identify early signs of diseases or nutrient deficiencies in crops. The scalability and versatility of machine learning make it a fundamental technology driving precision agriculture and sustainable farming practices.

Computer vision is another crucial segment within the market. It involves the use of AI algorithms to interpret visual data from cameras and sensors. In agriculture, computer vision is employed for tasks like crop monitoring, weed detection, and fruit quality assessment. By analyzing images and videos, computer vision systems can identify anomalies in crops, enabling timely interventions. It also plays a significant role in autonomous farming by guiding robotic equipment and ensuring accurate plant and soil analysis. As technology continues to advance, computer vision applications in agriculture are becoming more sophisticated and accurate.

Predictive analytics is a vital component of AI in agriculture market. It involves the use of historical and real-time data to forecast future trends and outcomes in agriculture. Predictive models assist farmers in making proactive decisions related to weather, pest control, and crop management. By analyzing data on weather patterns, soil conditions, and past crop performance, predictive analytics can help optimize planting schedules and predict potential crop diseases or yield fluctuations. This segment contributes to risk mitigation and efficient resource allocation in agriculture, making it an essential tool for farmers seeking to maximize their yields and profitability.

Breakup by Application:

Precision Farming

Agriculture Robots

Livestock Monitoring

Drone Analytics

Labor Management

Others

Precision farming represents the leading market segment

Precision farming, also known as precision agriculture, dominates the market segment with its focus on optimizing crop management practices. This segment involves the use of AI-driven technologies for precise monitoring of soil conditions, weather, and crop health. It enables farmers to make data-driven decisions about irrigation, fertilization, and pest control, ultimately leading to increased crop yield and resource efficiency. Precision farming also contributes to sustainability by reducing resource wastage and environmental impact, making it the largest and most influential segment in the AI in Agriculture market.

Agriculture robots play a vital role in automating various farming tasks, from planting and harvesting to weeding and monitoring. These robots leverage AI and machine learning to enhance efficiency and reduce labor requirements. They are particularly valuable in addressing labor shortages and increasing productivity in the agriculture sector. Agriculture robots continue to evolve, offering new opportunities for precision and automation in farming practices.

The livestock monitoring segment focuses on using AI to enhance the management and well-being of farm animals. It involves the use of sensors, wearables, and data analytics to monitor the health, behavior, and productivity of livestock. This segment ensures that animals receive proper care, leading to improved livestock yield, disease prevention, and overall animal welfare. It is an essential component of modern livestock farming practices.

Drone analytics in agriculture involves the use of unmanned aerial vehicles (UAVs) equipped with sensors and cameras to collect data on crop health, soil conditions, and pest infestations. AI-powered analytics then process this data to provide valuable insights to farmers. This segment offers a bird's-eye view of farms, enabling quick and efficient decision-making. It is particularly beneficial for large-scale farms and those with challenging terrain.

The labor management segment focuses on optimizing workforce management on farms using AI-driven solutions. It helps farmers allocate labor resources efficiently, plan tasks, and improve overall labor productivity. By automating routine administrative tasks and providing insights into labor utilization, this segment ensures that farm labor is utilized effectively and that the workforce is better managed, ultimately contributing to cost savings and improved farm operations.

Breakup by Region:

North America

United States

Canada

Asia-Pacific

China

Japan

India

South Korea

Australia

Indonesia

Others

Europe

Germany

France

United Kingdom

Italy

Spain

Russia

Others

Latin America

Brazil

Mexico

Others

Middle East and Africa

North America leads the market, accounting for the largest AI in agriculture market share

The market research report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America accounted for the largest market share.

North America dominates the market, driven by advanced technological infrastructure, a strong focus on precision farming, and extensive investments in AI adoption. The region benefits from the presence of leading AI solution providers, and the agriculture sector's willingness to embrace innovative technologies like AI for crop monitoring, soil analysis, and autonomous machinery. Favorable government policies and incentives also contribute to the market's growth in North America.

In Europe, the market is experiencing steady growth, with countries like Germany, France, and the Netherlands leading in adoption. The region places emphasis on sustainable farming practices and environmental conservation, making AI a valuable tool for optimizing resource utilization and minimizing ecological impact. European farmers are increasingly using AI for crop management, livestock monitoring, and predictive analytics to enhance overall agricultural productivity.

The Asia Pacific region is witnessing remarkable growth in the AI in agriculture market, driven by its vast agricultural sector, population growth, and increasing demand for food security. Countries like China and India are adopting AI-driven technologies for precision agriculture, crop monitoring, and smart farming practices. The region's agricultural innovation, combined with government support, positions Asia Pacific as a significant player in the global AI in Agriculture market.

Latin America is emerging as a promising market for AI in agriculture, with countries like Brazil and Argentina leading the adoption curve. The region's rich agricultural resources and growing awareness of the benefits of AI are driving its expansion. Latin American farmers are utilizing AI for crop optimization, disease detection, and yield prediction, aiming to increase productivity and ensure food sustainability.

The Middle East and Africa region are gradually embracing AI in agriculture, with a focus on addressing water scarcity and extreme climatic conditions. AI solutions for precision irrigation and crop management are gaining traction in arid regions. Additionally, governments and international organizations are investing in AI-powered projects to improve agricultural practices and ensure food security in this region.

Leading Key Players in the AI in Agriculture Industry:

Key players in the market are actively engaged in several strategic initiatives to maintain their competitive edge. These initiatives include significant investments in research and development to enhance AI algorithms and machine learning models tailored for agriculture. Companies are also focusing on partnerships and collaborations with agricultural technology firms, research institutions, and government agencies to expand their market reach and access to valuable data sources. Moreover, they are launching new AI-powered solutions and platforms that offer farmers advanced tools for crop monitoring, predictive analytics, and autonomous machinery. Additionally, key players are providing training and support services to educate farmers on the effective use of AI technologies in agriculture, fostering broader adoption.

The market research report has provided a comprehensive analysis of the competitive landscape. Detailed profiles of all major companies have also been provided. Some of the key players in the market include

A.A.A Taranis Visual Ltd.

Ag Leader Technology

AgEagle Aerial Systems Inc.

Bayer AG

Deere & Company

Gamaya

Granular Inc. (DuPont de Nemours, Inc.)

International Business Machines Corporation

Raven Industries Inc.

Trimble Inc.

Oracle Corporation

Wipro Limited

Key Questions Answered in This Report

• 1. What was the size of the global AI in agriculture market in 2024?
• 2. What is the expected growth rate of the global AI in agriculture market during 2025-2033?
• 3. What are the key factors driving the global AI in agriculture market?
• 4. What has been the impact of COVID-19 on the global AI in agriculture market?
• 5. What is the breakup of the global AI in agriculture market based on the offering?
• 6. What is the breakup of the global AI in agriculture market based on the technology?
• 7. What is the breakup of the global AI in agriculture market based on the application?
• 8. What are the key regions in the global AI in agriculture market?
• 9. Who are the key players/companies in the global AI in agriculture market?

Table of Contents

1 Preface

2 Scope and Methodology

• 2.1 Objectives of the Study
• 2.2 Stakeholders
• 2.3 Data Sources
  • 2.3.1 Primary Sources
  • 2.3.2 Secondary Sources
• 2.4 Market Estimation
  • 2.4.1 Bottom-Up Approach
  • 2.4.2 Top-Down Approach
• 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

• 4.1 Overview
• 4.2 Key Industry Trends

5 Global AI in Agriculture Market

• 5.1 Market Overview
• 5.2 Market Performance
• 5.3 Impact of COVID-19
• 5.4 Market Forecast

6 Market Breakup by Offering

• 6.1 Software
  • 6.1.1 Market Trends
  • 6.1.2 Market Forecast
• 6.2 Hardware
  • 6.2.1 Market Trends
  • 6.2.2 Market Forecast
• 6.3 AI-as-a-Service
  • 6.3.1 Market Trends
  • 6.3.2 Market Forecast
• 6.4 Services
  • 6.4.1 Market Trends
  • 6.4.2 Market Forecast

7 Market Breakup by Technology

• 7.1 Machine Learning
  • 7.1.1 Market Trends
  • 7.1.2 Market Forecast
• 7.2 Computer Vision
  • 7.2.1 Market Trends
  • 7.2.2 Market Forecast
• 7.3 Predictive Analytics
  • 7.3.1 Market Trends
  • 7.3.2 Market Forecast

8 Market Breakup by Application

• 8.1 Precision Farming
  • 8.1.1 Market Trends
  • 8.1.2 Market Forecast
• 8.2 Agriculture Robots
  • 8.2.1 Market Trends
  • 8.2.2 Market Forecast
• 8.3 Livestock Monitoring
  • 8.3.1 Market Trends
  • 8.3.2 Market Forecast
• 8.4 Drone Analytics
  • 8.4.1 Market Trends
  • 8.4.2 Market Forecast
• 8.5 Labor Management
  • 8.5.1 Market Trends
  • 8.5.2 Market Forecast
• 8.6 Others
  • 8.6.1 Market Trends
  • 8.6.2 Market Forecast

9 Market Breakup by Region

• 9.1 North America
  • 9.1.1 United States
    • 9.1.1.1 Market Trends
    • 9.1.1.2 Market Forecast
  • 9.1.2 Canada
    • 9.1.2.1 Market Trends
    • 9.1.2.2 Market Forecast
• 9.2 Asia-Pacific
  • 9.2.1 China
    • 9.2.1.1 Market Trends
    • 9.2.1.2 Market Forecast
  • 9.2.2 Japan
    • 9.2.2.1 Market Trends
    • 9.2.2.2 Market Forecast
  • 9.2.3 India
    • 9.2.3.1 Market Trends
    • 9.2.3.2 Market Forecast
  • 9.2.4 South Korea
    • 9.2.4.1 Market Trends
    • 9.2.4.2 Market Forecast
  • 9.2.5 Australia
    • 9.2.5.1 Market Trends
    • 9.2.5.2 Market Forecast
  • 9.2.6 Indonesia
    • 9.2.6.1 Market Trends
    • 9.2.6.2 Market Forecast
  • 9.2.7 Others
    • 9.2.7.1 Market Trends
    • 9.2.7.2 Market Forecast
• 9.3 Europe
  • 9.3.1 Germany
    • 9.3.1.1 Market Trends
    • 9.3.1.2 Market Forecast
  • 9.3.2 France
    • 9.3.2.1 Market Trends
    • 9.3.2.2 Market Forecast
  • 9.3.3 United Kingdom
    • 9.3.3.1 Market Trends
    • 9.3.3.2 Market Forecast
  • 9.3.4 Italy
    • 9.3.4.1 Market Trends
    • 9.3.4.2 Market Forecast
  • 9.3.5 Spain
    • 9.3.5.1 Market Trends
    • 9.3.5.2 Market Forecast
  • 9.3.6 Russia
    • 9.3.6.1 Market Trends
    • 9.3.6.2 Market Forecast
  • 9.3.7 Others
    • 9.3.7.1 Market Trends
    • 9.3.7.2 Market Forecast
• 9.4 Latin America
  • 9.4.1 Brazil
    • 9.4.1.1 Market Trends
    • 9.4.1.2 Market Forecast
  • 9.4.2 Mexico
    • 9.4.2.1 Market Trends
    • 9.4.2.2 Market Forecast
  • 9.4.3 Others
    • 9.4.3.1 Market Trends
    • 9.4.3.2 Market Forecast
• 9.5 Middle East and Africa
  • 9.5.1 Market Trends
  • 9.5.2 Market Breakup by Country
  • 9.5.3 Market Forecast

10 SWOT Analysis

• 10.1 Overview
• 10.2 Strengths
• 10.3 Weaknesses
• 10.4 Opportunities
• 10.5 Threats

11 Value Chain Analysis

12 Porters Five Forces Analysis

• 12.1 Overview
• 12.2 Bargaining Power of Buyers
• 12.3 Bargaining Power of Suppliers
• 12.4 Degree of Competition
• 12.5 Threat of New Entrants
• 12.6 Threat of Substitutes

13 Price Analysis

14 Competitive Landscape

• 14.1 Market Structure
• 14.2 Key Players
• 14.3 Profiles of Key Players
  • 14.3.1 A.A.A Taranis Visual Ltd.
    • 14.3.1.1 Company Overview
    • 14.3.1.2 Product Portfolio
  • 14.3.2 Ag Leader Technology
    • 14.3.2.1 Company Overview
    • 14.3.2.2 Product Portfolio
  • 14.3.3 AgEagle Aerial Systems Inc.
    • 14.3.3.1 Company Overview
    • 14.3.3.2 Product Portfolio
    • 14.3.3.3 Financials
  • 14.3.4 Bayer AG
    • 14.3.4.1 Company Overview
    • 14.3.4.2 Product Portfolio
    • 14.3.4.3 Financials
    • 14.3.4.4 SWOT Analysis
  • 14.3.5 Deere & Company
    • 14.3.5.1 Company Overview
    • 14.3.5.2 Product Portfolio
    • 14.3.5.3 Financials
    • 14.3.5.4 SWOT Analysis
  • 14.3.6 Gamaya
    • 14.3.6.1 Company Overview
    • 14.3.6.2 Product Portfolio
  • 14.3.7 Granular Inc. (DuPont de Nemours, Inc.)
    • 14.3.7.1 Company Overview
    • 14.3.7.2 Product Portfolio
  • 14.3.8 International Business Machines Corporation
    • 14.3.8.1 Company Overview
    • 14.3.8.2 Product Portfolio
    • 14.3.8.3 Financials
    • 14.3.8.4 SWOT Analysis
  • 14.3.9 Raven Industries Inc.
    • 14.3.9.1 Company Overview
    • 14.3.9.2 Product Portfolio
  • 14.3.10 Trimble Inc.
    • 14.3.10.1 Company Overview
    • 14.3.10.2 Product Portfolio
    • 14.3.10.3 Financials
    • 14.3.10.4 SWOT Analysis
  • 14.3.11 Oracle Corporation
    • 14.3.11.1 Company Overview
    • 14.3.11.2 Product Portfolio
    • 14.3.11.3 Financials
    • 14.3.11.4 SWOT Analysis
  • 14.3.12 Wipro Limited
    • 14.3.12.1 Company Overview
    • 14.3.12.2 Product Portfolio
    • 14.3.12.3 Financials

Kindly, note that this only represents a partial list of companies, and the complete list has been provided in the report