농업용 AI 시장 평가 : 기술별, 제공 제품별, 용도별, 전개 모드별, 농장 규모별, 지역별, 기회, 예측(2017-2031년)

Global AI in agriculture market is projected to witness a CAGR of 24.63% during the forecast period 2024-2031, growing from USD 3.01 billion in 2023 to USD 17.52 billion in 2031. The fast paced expansion of the market is taking place as advanced technologies are being embraced as measures of increasing agricultural productivity and efficiency. For instance, traditional farming practices have been changed through the use of deep learning, robotic vision, and prognostic techniques which are cases of artificial intelligence. With these tools, farmers can practice precision farming where data from sensors, drones, and satellites is analyzed so that irrigation, fertilization, and pest control can be optimized resulting in reduced resources usage and increased yield. Moreover, livestock monitoring, crop management, and soil health analysis are other potential areas where an AI tool plays a pivotal role, helping farmers to make informed decisions based on precise information.

Sustainable farming practices are in high demand in today's world as they are solutions to climate change challenges and the ever-growing global food requirements. Major companies in this field have put a lot of resources into innovation and technology to come up with artificial intelligence systems that specifically address agricultural issues. Also, cloud-based platforms, rising use of robots, and automation tools in farms are expected to support market growth.

At the regional level, North America is leading this market due to advanced technological infrastructure and large investments in agri-tech. Nevertheless, Asia-Pacific is projected to have the highest growth rates as countries such as China and India have been embracing AI technologies as a way of boosting crop production to be self-sufficient in terms of food. The global AI in agriculture market has potential for immense growth which presents enormous possibilities for technology suppliers and farmers.

In March 2024, a new robot called TOOGO from the French company SIZA Robotics was launched and available for pre-order, with delivery expected in 2025. TOOGO is a commercial pre-series of an autonomous vegetable and beet robot.

Growing Need for Sustainable Agricultural Practices Spur the Adoption of AI in Agriculture

Growing requirements for climatic change and safe food production are major factors in AI in agriculture market which drives its growth. With climate change affecting nations and increasing populations at alarming rates, the world has started emphasizing the use of sustainable agricultural systems that would help in ensuring the availability of food whilst reducing the harm done to nature.

Excessive water, fertilizer, and pesticide applications are often part of the traditional farming exercises that lead to soil degradation, water pollution as well as increased amounts of greenhouse gases in the atmosphere. AI technologies provide solutions by making better use of resources, improving soil health and enhancing efficiency in crop yield. For instance, in February 2024, Carbon Autonomous Robotics Systems launched Track LaserWeeder, an extension to the company's LaserWeeder model. It is made to support the LaserWeeder's weight more effectively in muddy areas and soft soils. When the machine is outfitted with tracks instead of wheels, its ground pressure is limited to 6.5 psi. The improvements include multilingual support for the iPad operator app and spatial data intelligence in the Carbon Ops Center.

In addition, AI instruments support observation of plant well-being, forecasting climatic systems and early detection of pests that enable timely measures which minimize the use of chemicals. Since sustainability is a priority for buyers, governments, and farm owners, AI-based eco-friendly agricultural systems will witness a steady rise in demand, increasing the market size.

Labor Shortages and High Labor Costs Fuel the Market Growth

The adoption of AI technologies in agriculture is being driven essentially by factors such as labor shortages and high labor costs. The declining rural workforce and the rising difficulty in finding skilled labor willing to take up intensive farming tasks present as major challenges faced by the farming industry in many parts of the world today. Additionally, this problem is worsened by an increment in aged farming communities and the migration of youthful workers to cities for improved employment prospects.

Consequently, farmers have a hard time due to mounting labor costs. In this case, there are effective solutions such as the use of AI technologies such as robotics and automation that can help in addressing these challenges by automating repetitive and labor-intensive tasks such as planting, weeding, harvesting, and monitoring crops and livestock. It reduces the reliance on human work and makes it more efficient. In May 2024, DigiFarm AS, a Norwegian company, created an artificial intelligence model capable of autonomously detecting field boundaries. A deep neural network is trained to recognize boundaries and other field features, such trees, grain, and water. With 57 countries providing 4 million hectares of training data, their model has grown to be very vast and requires a substantial amount of processing power for training.

Natural Language Processing Technology Holds a Substantial Market Share

Natural Language Processing (NLP) technology has a huge market share in the AI in agriculture market as it is able to improve communication and decision-making in the farming community. NLP allows user-friendly interfaces and voice-activated systems that can recognize oral language, thus making complex AI applications available to farmers who are less technically skilled. It enables farmers to have conversations with artificial intelligence systems so that they can have access to important information such as weather predictions, pest and disease alerts, and crop management.

In addition, natural language processing can be integrated into virtual assistants and chatbots offering real-time substitutes to aid farmers make choices quickly. Furthermore, it allows conversion of specialist farming knowledge into several dialects, thus creating a broad scope of its application over diverse territories and among different languages. There is a growing need for agriculture-driven AI solutions that are seamless and intuitive together with NLP advantages, including better accessibility and increased data interpretation, contributing to its great acceptance rate in the larger landscape of agricultural AI. In March 2024, Bayer AG launched a GenAI system pilot program planned to help agronomists and farmers in their day-to-day work. The launch is an expert system that can swiftly and precisely respond to agronomy, farm management, and Bayer agricultural goods inquiries. The intuitive technology responds to natural language and produces expert knowledge in a matter of seconds, as opposed to a laborious procedure.

North America Holds the Largest Market Share

North America is dominating global AI in agriculture market due to several reasons. The region has a large technology base and is one of the leading regions in the world that adopt modern technologies in various industries including agriculture. As a result, farmers in North America are advocating for the use of artificial intelligence to optimize their farming practices, increase yields, and minimize costs through using machine learning, computer vision, predictive analytics and other forms of AI. The existence of large agritech firms along with startups in the United States and Canada has contributed to a rapid progress and coverage of inventive AI solutions to suit demands by farmers across the continent. In February 2024, Deere & Company launched its AI-powered weed-sensing system, See & Spray Premium, which triggers individual spray nozzles when target weeds are seen by boom-mounted cameras scanning a crop, covering more than 2,100 square feet every second.

In addition, the robust government backing of sustainable farming methods and technological breakthroughs, for instance, subsidies to promote precision farming, is helping farmers buy AI instruments. Furthermore, automation and AI solutions that answer issues of labor scarcity and elevated production expenses are helping economies to grow in this sector. Thus, North America dominates the agricultural AI market with influences that shape the world's activities.

Future Market Scenario (2024 - 2031F)

As AI technologies evolve, their use in agriculture is expected to become more advanced, incorporating features such as real-time data analysis, predictive modeling, and automated decision-making.

The launch of robots and machine tools is expected to drastically change the way farming is done by enhancing efficiency and minimizing reliance on human labor especially in areas that are characterized by acute shortages of workers.

The amalgamation of AI with other advancing technologies such as Internet of Things (IoT), cloud computing, and blockchain will result in the rise of intelligent agricultural systems that avail intelligent and informed choices to farmers.

Key Players Landscape and Outlook

The key players in global AI in agriculture market are a mix of established technology firms, specialized agri-tech companies, and innovative startups, competing to offer advanced AI solutions tailored for the agricultural sector. These players are heavily investing in research and development to create cutting-edge technologies such as machine learning, computer vision, predictive analytics, and robotics that cater to various agricultural needs, including crop management, soil monitoring, pest detection, and yield prediction. For instance, in July 2024, A.A.A Taranis Visual Ltd. introduced Ag Assistant, driven by a generative artificial intelligence model with a deep grasp of agronomy that incorporates data sources from multiple modalities, including text, voice, and images.

The competitive landscape is expected to intensify as new entrants bring innovative solutions to the market, driving further advancements and adoption of AI in agriculture. Key players are focusing on developing scalable cloud-based platforms that integrate AI with IoT devices, providing real-time insights and fostering precision farming practices. As the demand for sustainable and efficient farming solutions continues to grow, the outlook for the AI in agriculture market remains robust, with significant opportunities for players to expand their presence globally and drive technological innovation.

Table of Contents

1. Project Scope and Definitions

2. Research Methodology

3. Executive Summary

4. Voice of Customer

• 4.1. Demographics (Age/Cohort Analysis - Baby Boomers and Gen X, Millennials, Gen Z; Gender; Income - Low, Mid and High; Geography; Nationality; etc.)
• 4.2. Market Awareness and Product Information
• 4.3. Brand Awareness and Loyalty
• 4.4. Factors Considered in Purchase Decision
  • 4.4.1. Cost
  • 4.4.2. Return on Investment
  • 4.4.3. Ease of Use and Integration
  • 4.4.4. Scalability and Flexibility
  • 4.4.5. Reliability
  • 4.4.6. Accuracy
  • 4.4.7. Support and Training
  • 4.4.8. Compliance with Regulations
  • 4.4.9. Technology Compatibility
  • 4.4.10. Vendor Reputation and Experience
• 4.5. Existing or Intended User

5. Global AI in Agriculture Market Outlook, 2017-2031F

• 5.1. Market Size Analysis & Forecast
  • 5.1.1. By Value
• 5.2. Market Share Analysis & Forecast
  • 5.2.1. By Technology
    • 5.2.1.1. Machine Learning
    • 5.2.1.2. Computer Vision
    • 5.2.1.3. Predictive Analytics
    • 5.2.1.4. Natural Language Processing (NLP)
    • 5.2.1.5. Robotics and Automation
  • 5.2.2. By Offering
    • 5.2.2.1. Hardware
    • 5.2.2.2. Software
    • 5.2.2.3. Services
  • 5.2.3. By Application
    • 5.2.3.1. Precision Farming
    • 5.2.3.2. Livestock Monitoring
    • 5.2.3.3. Drone Analytics
    • 5.2.3.4. Agricultural Robots
    • 5.2.3.5. Weather Forecasting
    • 5.2.3.6. Others
  • 5.2.4. By Deployment Mode
    • 5.2.4.1. Cloud-based
    • 5.2.4.2. On-premises
  • 5.2.5. By Farm Size
    • 5.2.5.1. Small and Medium Farms
    • 5.2.5.2. Large Farms
  • 5.2.6. By Region
    • 5.2.6.1. North America
    • 5.2.6.2. Europe
    • 5.2.6.3. Asia-Pacific
    • 5.2.6.4. South America
    • 5.2.6.5. Middle East and Africa
  • 5.2.7. By Company Market Share Analysis (Top 5 Companies and Others - By Value, 2023)
• 5.3. Market Map Analysis, 2023
  • 5.3.1. By Technology
  • 5.3.2. By Offering
  • 5.3.3. By Application
  • 5.3.4. By Deployment Mode
  • 5.3.5. By Farm Size
  • 5.3.6. By Region

6. North America AI in Agriculture Market Outlook, 2017-2031F*

• 6.1. Market Size Analysis & Forecast
  • 6.1.1. By Value
• 6.2. Market Share Analysis & Forecast
  • 6.2.1. By Technology
    • 6.2.1.1. Machine Learning
    • 6.2.1.2. Computer Vision
    • 6.2.1.3. Predictive Analytics
    • 6.2.1.4. Natural Language Processing (NLP)
    • 6.2.1.5. Robotics and Automation
  • 6.2.2. By Offering
    • 6.2.2.1. Hardware
    • 6.2.2.2. Software
    • 6.2.2.3. Services
  • 6.2.3. By Application
    • 6.2.3.1. Precision Farming
    • 6.2.3.2. Livestock Monitoring
    • 6.2.3.3. Drone Analytics
    • 6.2.3.4. Agricultural Robots
    • 6.2.3.5. Weather Forecasting
    • 6.2.3.6. Others
  • 6.2.4. By Deployment Mode
    • 6.2.4.1. Cloud-based
    • 6.2.4.2. On-premises
  • 6.2.5. By Farm Size
    • 6.2.5.1. Small and Medium Farms
    • 6.2.5.2. Large Farms
  • 6.2.6. By Country Share
    • 6.2.6.1. United States
    • 6.2.6.2. Canada
    • 6.2.6.3. Mexico
• 6.3. Country Market Assessment
  • 6.3.1. United States AI in Agriculture Market Outlook, 2017-2031F*
    • 6.3.1.1. Market Size Analysis & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share Analysis & Forecast
      • 6.3.1.2.1. By Technology
        • 6.3.1.2.1.1. Machine Learning
        • 6.3.1.2.1.2. Computer Vision
        • 6.3.1.2.1.3. Predictive Analytics
        • 6.3.1.2.1.4. Natural Language Processing (NLP)
        • 6.3.1.2.1.5. Robotics and Automation
      • 6.3.1.2.2. By Offering
        • 6.3.1.2.2.1. Hardware
        • 6.3.1.2.2.2. Software
        • 6.3.1.2.2.3. Services
      • 6.3.1.2.3. By Application
        • 6.3.1.2.3.1. Precision Farming
        • 6.3.1.2.3.2. Livestock Monitoring
        • 6.3.1.2.3.3. Drone Analytics
        • 6.3.1.2.3.4. Agricultural Robots
        • 6.3.1.2.3.5. Weather Forecasting
        • 6.3.1.2.3.6. Others
      • 6.3.1.2.4. By Deployment Mode
        • 6.3.1.2.4.1. Cloud-based
        • 6.3.1.2.4.2. On-premises
      • 6.3.1.2.5. By Farm Size
        • 6.3.1.2.5.1. Small and Medium Farms
        • 6.3.1.2.5.2. Large Farms
  • 6.3.2. Canada
  • 6.3.3. Mexico

All segments will be provided for all regions and countries covered

7. Europe AI in Agriculture Market Outlook, 2017-2031F

• 7.1. Germany
• 7.2. France
• 7.3. Italy
• 7.4. United Kingdom
• 7.5. Russia
• 7.6. Netherlands
• 7.7. Spain
• 7.8. Turkey
• 7.9. Poland

8. Asia-Pacific AI in Agriculture Market Outlook, 2017-2031F

• 8.1. India
• 8.2. China
• 8.3. Japan
• 8.4. Australia
• 8.5. Vietnam
• 8.6. South Korea
• 8.7. Indonesia
• 8.8. Philippines

9. South America AI in Agriculture Market Outlook, 2017-2031F

• 9.1. Brazil
• 9.2. Argentina

10. Middle East and Africa AI in Agriculture Market Outlook, 2017-2031F

• 10.1. Saudi Arabia
• 10.2. UAE
• 10.3. South Africa

11. Demand Supply Analysis

12. Value Chain Analysis

13. Porter's Five Forces Analysis

14. PESTLE Analysis

15. Macro-economic Indicators

16. Profit Margin Analysis

17. Market Dynamics

• 17.1. Market Drivers
• 17.2. Market Challenges

18. Market Trends and Developments

19. Case Studies

20. Competitive Landscape

• 20.1. Competition Matrix of Top 5 Market Leaders
• 20.2. Company Ecosystem Analysis (Startup v/s SME v/s Large-scale)
• 20.3. SWOT Analysis for Top 5 Players
• 20.4. Key Players Landscape for Top 10 Market Players
  • 20.4.1. Microsoft Corporation
    • 20.4.1.1. Company Details
    • 20.4.1.2. Key Management Personnel
    • 20.4.1.3. Products and Services
    • 20.4.1.4. Financials (As Reported)
    • 20.4.1.5. Key Market Focus and Geographical Presence
    • 20.4.1.6. Recent Developments/Collaborations/Partnerships/Mergers and Acquisition
  • 20.4.2. Deere & Company
  • 20.4.3. IBM Corporation
  • 20.4.4. Ever.Ag Corporation
  • 20.4.5. Prospera Technologies Ltd.
  • 20.4.6. Raven Industries, Inc.
  • 20.4.7. Tule Technologies Inc.
  • 20.4.8. Trimble Inc.
  • 20.4.9. A.A.A Taranis Visual Ltd.
  • 20.4.10. Gamaya SA

Companies mentioned above DO NOT hold any order as per market share and can be changed as per information available during research work.

21. Strategic Recommendations

22. About Us and Disclaimer