세계의 지능형 농업 시장 예측 : 농장 규모별, 기술별, 용도별, 지역별 분석(-2030년)

According to Stratistics MRC, the Global Intelligent Agriculture Market is accounted for $18.08 billion in 2024 and is expected to reach $34.93 billion by 2030 growing at a CAGR of 11.6% during the forecast period. Intelligent agriculture, also known as smart farming, refers to the use of advanced technologies, data analytics, and automation to optimize agricultural practices. It incorporates tools such as Internet of Things (IoT) devices, drones, sensors, artificial intelligence (AI), and machine learning to monitor and manage various aspects of farming, from crop growth to livestock management. These technologies enable farmers to make more informed decisions, improve crop yields, reduce resource wastage and enhance sustainability.

According to the World Bank, technology-based agriculture systems (including smart farming techniques) can significantly increase smallholder farmers' income. In regions such as Sub-Saharan Africa and South Asia, the World Bank estimates that digital agriculture could help reduce poverty for over 500 million people.

Market Dynamics:

Driver:

Rising demand for food security

Rising global demand for food security is driving the rapid growth of Intelligent Agriculture, an innovative approach that leverages technology to enhance agricultural productivity and sustainability. With the world's population projected to reach nearly 10 billion by 2050, ensuring a stable and efficient food supply has become a critical concern. Intelligent Agriculture uses advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), robotics, and data analytics to optimize farming practices. These technologies enable real-time monitoring of soil health, crop conditions, and weather patterns, helping farmers make informed decisions on irrigation, fertilization, and pest control. By improving resource efficiency and reducing waste, Intelligent Agriculture can increase yields while minimizing environmental impact.

Restraint:

High initial investment costs

High initial investment costs are a significant barrier to the widespread adoption of intelligent agriculture technologies. These advanced farming solutions, which include automated machinery, drones, sensors, and artificial intelligence (AI) systems, require substantial upfront capital for both the equipment and infrastructure. For many small and medium-sized farmers, this high financial burden is prohibitive, especially in regions where access to financing or subsidies is limited. In addition to the purchase of the technologies themselves, farmers often face additional costs for training, maintenance, and integration with existing systems. As a result, the shift towards more efficient, data-driven farming practices becomes more challenging for those without sufficient financial resources.

Opportunity:

Integration of Agri-tech with financial services

The integration of agri-tech with financial services is revolutionizing intelligent agriculture by providing farmers with access to advanced tools and financial products that improve productivity and sustainability. Agri-tech innovations, such as IoT sensors, AI-driven analytics, and precision farming tools, collect real-time data on crop health, soil conditions, and weather patterns. This data is valuable for farmers to make informed decisions. When coupled with financial services like digital payment platforms, crop insurance, and credit systems, farmers can access the necessary capital to invest in technology, reduce risks, and increase efficiency.

Threat:

Shortage of skilled labor

The shortage of skilled labor is a significant challenge in the advancement of intelligent agriculture, which relies heavily on technology and automation to improve farming practices. As the industry adopts more advanced systems such as AI, IoT, drones, and data analytics, there is a growing need for skilled workers who can operate, maintain, and optimize these technologies. However, many agricultural workers lack the necessary expertise in these high-tech fields. This gap results in inefficiencies, as farmers struggle to fully harness the potential of modern tools. The labor shortage is further compounded by an aging workforce and the limited appeal of agricultural careers among younger generations.

Covid-19 Impact:

The COVID-19 pandemic significantly impacted intelligent agriculture by disrupting global supply chains, labor availability, and market access. While the agriculture sector is essential, the restrictions and lockdowns led to labor shortages, especially in tasks like planting, harvesting, and packaging, which were often reliant on seasonal or migrant workers. This labor deficit hindered the timely production and distribution of crops, affecting food supply. The pandemic prompted a greater reliance on technology in agriculture, such as precision farming, drones, and IoT-based monitoring systems, to compensate for the workforce shortage and improve efficiency.

The Medium Scale Farms segment is expected to be the largest during the forecast period

Medium Scale Farms segment is expected to dominate the largest share over the estimated period, by adopting advanced technologies to optimize productivity, sustainability, and resource management. These farms, which are larger than small family-run operations but smaller than large industrial farms, are increasingly integrating Internet of Things (IoT) devices, sensors, drones, and artificial intelligence (AI) into their farming practices. These technologies help in real-time monitoring of crop health, soil moisture, and weather conditions, allowing farmers to make data-driven decisions that improve yields while minimizing waste.

The Crop Monitoring segment is expected to have the highest CAGR during the forecast period

Crop Monitoring segment is estimated to grow at a rapid pace during the forecast period. Crop monitoring is a key aspect of intelligent agriculture, significantly enhancing the efficiency and productivity of modern farming. Through the use of advanced technologies such as satellite imagery, drones, and IoT sensors, farmers can continuously monitor crop health, soil moisture, temperature, and other vital factors in real-time. This data-driven approach allows for precise decision-making, enabling farmers to optimize irrigation, apply fertilizers and pesticides more efficiently, and detect early signs of diseases or pest infestations. Furthermore, crop monitoring systems often incorporate AI and machine learning algorithms to analyze large volumes of data, providing predictive insights and recommendations for improved crop management.

Region with largest share:

Asia Pacific region is poised to hold the largest share of the market throughout the extrapolated period. With the rapid growth of the agricultural sector and increasing challenges like climate change, population growth, and resource scarcity, governments are prioritizing smart farming solutions. Policies and investments are being made to promote the adoption of technologies such as Internet of Things (IoT), Artificial Intelligence (AI), drones, and robotics. These innovations help optimize resource use, enhance crop yields, improve supply chain management, and reduce environmental impacts. Governments are also fostering public-private partnerships, providing subsidies, grants, and tax incentives to encourage technological adoption among farmers.

Region with highest CAGR:

Europe region is estimated to witness the highest CAGR during the projected time frame. As technology continues to reshape the agricultural sector, European farmers and startups are increasingly adopting innovations like precision farming, AI-driven analytics, and IoT-enabled devices to optimize crop production, reduce resource consumption, and increase efficiency. The surge in investment has facilitated the emergence of cutting-edge solutions that enhance sustainability, improve supply chain management, and boost food security. This capital influx supports both established AgTech companies and emerging startups, enabling them to scale their operations and expand their market reach. With increased access to funding, European agriculture is becoming more data-driven and technologically advanced, allowing for smarter, more resilient farming practices.

Key players in the market

Some of the key players in Intelligent Agriculture market include AG Leader Technology, AGCO Corporation, BASF Agricultural Solutions, Bayer AG, John Deere, Kubota Corporation, Raven Industries, Syngenta AG, Trimble Inc, Topcon Positioning Systems, KUKA AG, JH Robotics, Inc and Kawasaki Heavy Industries.

Key Developments:

In July 2024, AGCO to Launch Autonomous Tractor Retrofit Kits for Mixed-fleets. The retrofit kits will enable select Fendt and John Deere tractors to autonomously perform grain cart and tillage functions, with more tractor compatibility and functions coming in the future.

In March 2024, Kubota Launches RTV520 Crossroads Edition. This refresh to the 2020 model adds a handful of factory-installed features based on popular request. This new limited edition of the RTV520 comes with a package of factory-installed creature comforts.

Farm Sizes Covered:

• Small Scale Farms
• Medium Scale Farms
• Large Scale Farms

Technologies Covered:

• Internet of Things (IoT)
• Artificial Intelligence (AI)
• Robotics and Automation
• Cloud Computing
• Other Technologies

Applications Covered:

• Precision Farming
• Livestock Monitoring
• Crop Monitoring
• Smart Greenhouses
• Agricultural Drones

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Technology Analysis
• 3.7 Application Analysis
• 3.8 Emerging Markets
• 3.9 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Intelligent Agriculture Market, By Farm Size

• 5.1 Introduction
• 5.2 Small Scale Farms
• 5.3 Medium Scale Farms
• 5.4 Large Scale Farms

6 Global Intelligent Agriculture Market, By Technology

• 6.1 Introduction
• 6.2 Internet of Things (IoT)
• 6.3 Artificial Intelligence (AI)
• 6.4 Robotics and Automation
• 6.5 Cloud Computing
• 6.6 Other Technologies

7 Global Intelligent Agriculture Market, By Application

• 7.1 Introduction
• 7.2 Precision Farming
• 7.3 Livestock Monitoring
• 7.4 Crop Monitoring
• 7.5 Smart Greenhouses
• 7.6 Agricultural Drones

8 Global Intelligent Agriculture Market, By Geography

• 8.1 Introduction
• 8.2 North America
  • 8.2.1 US
  • 8.2.2 Canada
  • 8.2.3 Mexico
• 8.3 Europe
  • 8.3.1 Germany
  • 8.3.2 UK
  • 8.3.3 Italy
  • 8.3.4 France
  • 8.3.5 Spain
  • 8.3.6 Rest of Europe
• 8.4 Asia Pacific
  • 8.4.1 Japan
  • 8.4.2 China
  • 8.4.3 India
  • 8.4.4 Australia
  • 8.4.5 New Zealand
  • 8.4.6 South Korea
  • 8.4.7 Rest of Asia Pacific
• 8.5 South America
  • 8.5.1 Argentina
  • 8.5.2 Brazil
  • 8.5.3 Chile
  • 8.5.4 Rest of South America
• 8.6 Middle East & Africa
  • 8.6.1 Saudi Arabia
  • 8.6.2 UAE
  • 8.6.3 Qatar
  • 8.6.4 South Africa
  • 8.6.5 Rest of Middle East & Africa

9 Key Developments

• 9.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 9.2 Acquisitions & Mergers
• 9.3 New Product Launch
• 9.4 Expansions
• 9.5 Other Key Strategies

10 Company Profiling

• 10.1 AG Leader Technology
• 10.2 AGCO Corporation
• 10.3 BASF Agricultural Solutions
• 10.4 Bayer AG
• 10.5 John Deere
• 10.6 Kubota Corporation
• 10.7 Raven Industries
• 10.8 Syngenta AG
• 10.9 Trimble Inc
• 10.10 Topcon Positioning Systems
• 10.11 KUKA AG
• 10.12 JH Robotics, Inc
• 10.13 Kawasaki Heavy Industries