美国农业人工智慧市场评估：依技术、产品、应用、部署模式、农场规模、地区、机会、预测，2017-2031年

预计2024-2031年期间，美国农业人工智慧市场规模将以20.55%的年复合成长率扩大，从2023年的3.9991亿美元成长到2031年的17.8361亿美元。由于技术进步和精准农业方法的日益采用，市场不断成长，导致该行业取得巨大发展。在各种农业作业中，引进机器学习、电脑视觉和机器人等人工智慧技术，以提高作物生产力，同时最佳化作物管理并降低成本。

这些技术代表天气预报、土壤条件、虫害、植物健康等，帮助农民做出基于事实的决策。更重要的是，人工智慧整合到无人驾驶拖拉机和智慧浇水系统等农业设备中，这些设备越来越流行用于自动化劳动密集任务以提高生产力。

因此，对永续农业技术的需求不断成长，以及由于人口成长而对生产粮食以扩大粮食供应的兴趣日益浓厚，是支持市场的驱动力。基于人工智慧的系统实现了生态友善农业，有助于限制用水、化肥用量和农药的使用。知名产业参与者致力于与科技公司和新创公司合作，进行人工智慧相关研究，并针对新出现的问题提出创新解决方案。

此外，政府支持和资金的增加推动农业技术的创新。然而，市场成长可能会因高昂的启动成本、资料隐私挑战以及提高农民技术技能的需求而受到阻碍。总而言之，由于技术的不断进步和农民意识的提高，农业部门的转型，美国农业人工智慧市场预计将在未来几年显着成长。

本报告研究了美国农业人工智慧市场，并提供了市场概况，以及依技术、产品、应用、部署模式、农场规模、地区的趋势，以及进入市场的公司等。

目录

第1章 专案范围与定义

第2章 研究方法

第3章 执行摘要

第4章 顾客回馈

第5章 美国农业人工智慧市场展望，2017-2031

• 市场规模分析与预测
• 市占率分析与预测
• 2023年市场地图分析
  • 依技术
  • 依提供
  • 依用途
  • 依部署模式
  • 依农场规模
  • 依地区

第6章 供需分析

第7章 价值链分析

第8章 波特五力分析

第9章 PESTLE分析

第10章 宏观经济指标

第11章 获利能力分析

第12章 市场动态

第13章 市场趋势与发展

第14章 个案研究

第15章 竞争态势

• 前 5 名市场领导者的竞争矩阵
• 企业生态系分析（新创企业、中小企业与大公司）
• 前 5 名的公司的SWOT 分析
• 前10名主要企业状况
  • Deere & Company
  • Trimble Inc.
  • Microsoft Corporation
  • IBM Corporation
  • Raven Industries, Inc.
  • A.A.A Taranis Visual Ltd.
  • Bayer AG
  • Syngenta Corporation
  • Tule Technologies Inc.
  • Climate LLC

第16章 策略建议

第17章 查询及免责声明

United States AI in agriculture market is projected to witness a CAGR of 20.55% during the forecast period 2024-2031, growing from USD 399.91 million in 2023 to USD 1783.61 million in 2031. The market is growing due to technological evolution and increasing adoption of precision farming methods, setting the industry for enormous developments. In varied agricultural operations, AI technologies, including machine learning, computer vision, and robots, are being introduced with an aim of optimizing crop management and improving farm productivity while reducing costs.

With weather forecast, soil status, pest invasions, and plant health represented, these technologies make it possible for farmers to arrive at their decisions based on facts. More importantly, AI is being integrated into farming instruments such as driverless tractors and intelligent watering systems that are becoming increasingly popular to automate laborious operations with the aim of increasing productivity.

Thus, increasing demand for sustainable agricultural techniques alongside growing focus on production to expand food availability due to population growth are the driving forces behind the market. Eco-friendly farming methods are possible through AI-based systems which help in limiting water usage, fertilizer amounts, and pesticide applications. Prominent participants in the industry are emphasizing on conducting artificial intelligence related studies by cooperating with tech companies or startups so as to come up with innovative resolutions of arising issues.

Furthermore, agri-tech innovations are being driven by the rising government support and funding. However, market growth could be impeded through high start-up costs, data privacy challenges, and the need for improved technical skills among farmers. To summarize, the United States AI in agriculture market is projected to grow substantially in the forecast years, transforming the farming sector due to continuous technological advancements and growing consciousness among farmers.

In July 2024, an innovative AI-based model, Ag Assistant, was introduced by A.A.A Taranis Visual Ltd. It is powered by a generative artificial intelligence model that integrates data in several modalities, such as text, voice, and images, and has a thorough understanding of agronomy.

Data-Driven Decision Making Fuels the Market Growth

The farming industry has been greatly altered by decisions made using analysis of data. In today's world, farmers are able to optimize their operations and maximize output by using vast amounts greenhouses. It is made possible through modern tools such as artificial intelligence (AI), which use machine learning and data analytics techniques that allow agricultural producers collect, analyze, or deduce information relating to various aspects such as weather forecasts, soil sensors, satellite images, crop health monitors, and others. Consequently, this information provides insights into every aspect of farming from planting to harvesting, enabling farmers to make more informed decisions.

As an instance, AI can forecast the best time for planting, suggest accurate amounts of water and fertilizers, and detect possible insect invasions before severe outbreaks. With this knowledge at hand, farmers are able to improve crop production, cut wastage, and make better use of their resources. Besides that, forecasting market trends and demand using AI-based predictive analytics enables farmers to plan their production accordingly. In fact, in March 2024, UCF researchers led a project aimed to develop AI-powered agricultural technologies. To enable more precise and knowledgeable agricultural decision-making, the study will explore technologies that enhance field operations in the sector.

Making choices based on data raises profit margins and encourages sustainable agricultural practices due to reduced resource usage and lower environmental damage. Consequently, data-driven decision-making forms an integral part of contemporary agriculture, boosting the adoption of artificial intelligence in the sector.

Technological Advancements in AI Help in Market Expansion

The field of agriculture is undergoing a transformation, owing to the emerging AI technology that raises yield levels, cuts down expenses, and promotes sustainable practices. Some of the advancements being made in this area include machine learning, deep learning, computer vision, and robotics which are leading to more accurate and smarter agricultural approaches. For instance, farmers can monitor their crop health status and soil conditions from a distance using computer vision system, while prediction of possible pest invasion or disease occurrence, done by algorithmic machines which utilize machine learning process on such information.

In addition, self-directing farm machinery such as robotized harrows have been developed, which make it plausible for them to finish heavy labor works with more precision and pace than human laborers. Moreover, AI drones help monitor areas from above alongside spraying liquids leading to efficient use of resources and minimizing direct human involvement.

These technological advancements have made it possible for the scalability of AI solutions to be improved, thus making them more accessible and affordable to farms of different sizes. Furthermore, as AI technology continue evolving, it will lead to more creative ideas in the agricultural sector enabling better harvests at lower prices while attaining environmental sustainability objectives in a more efficient way. For instance, in April 2024, South Africa's Aerobotics PTY Ltd introduced AI based platform for helping farmers in the United States to improve fruit and nut crop yields. The technology further helps in efficiently utilizing the water resources.

Hardware Holds a Substantial Market Share

The United States AI in agriculture market is dominated by hardware components, which account for a significant portion of market share due to rising usage of sophisticated farming tools and apparatus fitted with AI technologies. Modern farms depend heavily on essential hardware components such as sensors, drones, robotics, autonomous tractors, and smart irrigation systems. These devices have AI functionalities that allow precision agriculture through collection and analysis of real-time data on soil health, weather patterns, crop conditions, and pest occurrence.

For instance, environmental condition monitoring and data-driven irrigation optimization, fertilization, and pest control enable precision farming. Drones and robotics are widely used to survey large areas, plant seeds, and apply fertilizers or pesticides with high accuracy. Farmers are increasingly turning to these AI driven hardware solutions because they help reduce labor costs, raise productivity and enhance crop yield.

Additionally, farmers find it easier and reasonable to use such technologically improved hardware, hence facilitating their use more often. With the growing need for farming practices, hardware will increasingly be instrumental in expanding the AI in agriculture market.

In February 2024, to combat weeds, Deere & Company introduced See & Spray Premium, an AI-powered weed-sensing system that uses boom-mounted cameras to scan a crop covering more than 2,100 square feet every second, triggering individual spray nozzles when target weeds are seen.

Future Market Scenario (2024 - 2031F)

The use of artificial intelligence enabled flying devices, robot harvesters, and driverless farm vehicles to be the primary techniques used on farms to improve their overall productivity and reduce dependence on manual labor.

The combination of AI with other advanced technologies such as IoT, blockchain, and cloud computing will lead to development of better farming programs that will provide complete services starting from soil preparation to harvesting crops and distributing them in the market.

Key Players Landscape and Outlook

The landscape of key players in the United States AI in agriculture market is characterized by a mix of established agricultural equipment manufacturers, technology giants, and innovative startups. These companies are actively investing in research and development to enhance their AI capabilities, focusing on areas such as precision farming, autonomous machinery, and predictive analytics. Established players are leveraging their extensive distribution networks and existing customer bases to integrate AI solutions into traditional farming equipment, thereby offering more advanced and efficient products to farmers.

Technology-based companies, on the contrary, are entering the market with innovative AI platforms that provide data-driven insights, optimize farm management practices, and enhance crop yield and quality. For instance, in November 2023, Microsoft Corporation and Bayer AG strategically collaborated to launch Leaf Agriculture and OneSoil to develop availability of data for farm machinery and bringing new solutions for in-season crop detection, respectively. Both of the AgPowered Services are fueled by AI, transforming the agricultural sector globally.

The outlook for key players is positive, with continuous innovation expected to drive competition and collaboration in the market. As the demand for sustainable and efficient farming practices grow, these companies are well-positioned to capitalize on the opportunities in the evolving AI in agriculture landscape.

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. United States 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. Northeast
    • 5.2.6.2. Midwest
    • 5.2.6.3. West
    • 5.2.6.4. South
  • 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

All segments will be provided for all regions covered

6. Demand Supply Analysis

7. Value Chain Analysis

8. Porter's Five Forces Analysis

9. PESTLE Analysis

10. Macro-economic Indicators

11. Profit Margin Analysis

12. Market Dynamics

• 12.1. Market Drivers
• 12.2. Market Challenges

13. Market Trends and Developments

14. Case Studies

15. Competitive Landscape

• 15.1. Competition Matrix of Top 5 Market Leaders
• 15.2. Company Ecosystem Analysis (Startup v/s SME v/s Large-scale)
• 15.3. SWOT Analysis for Top 5 Players
• 15.4. Key Players Landscape for Top 10 Market Players
  • 15.4.1. Deere & Company
    • 15.4.1.1. Company Details
    • 15.4.1.2. Key Management Personnel
    • 15.4.1.3. Products and Services
    • 15.4.1.4. Financials (As Reported)
    • 15.4.1.5. Key Market Focus and Geographical Presence
    • 15.4.1.6. Recent Developments/Collaborations/Partnerships/Mergers and Acquisition
  • 15.4.2. Trimble Inc.
  • 15.4.3. Microsoft Corporation
  • 15.4.4. IBM Corporation
  • 15.4.5. Raven Industries, Inc.
  • 15.4.6. A.A.A Taranis Visual Ltd.
  • 15.4.7. Bayer AG
  • 15.4.8. Syngenta Corporation
  • 15.4.9. Tule Technologies Inc.
  • 15.4.10. Climate LLC

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

16. Strategic Recommendations

17. About Us and Disclaimer

List of Tables

• Table 1. Pricing Analysis of Products from Key Players
• Table 2. Competition Matrix of Top 5 Market Leaders
• Table 3. Mergers & Acquisitions/ Joint Ventures (If Applicable)
• Table 4. About Us - Regions and Countries Where We Have Executed Client Projects

List of Figures

• Figure 1. United States AI in Agriculture Market, By Value, In USD Million, 2017-2031F
• Figure 2. United States AI in Agriculture Market Share (%), By Technology, 2017-2031F
• Figure 3. United States AI in Agriculture Market Share (%), By Offering, 2017-2031F
• Figure 4. United States AI in Agriculture Market Share (%), By Application, 2017-2031F
• Figure 5. United States AI in Agriculture Market Share (%), By Deployment Mode, 2017-2031F
• Figure 6. United States AI in Agriculture Market Share (%), By Farm Size, 2017-2031F
• Figure 7. United States AI in Agriculture Market Share (%), By Region, 2017-2031F
• Figure 8. By Technology Map-Market Size (USD Million) & Growth Rate (%), 2023
• Figure 9. By Offering Map-Market Size (USD Million) & Growth Rate (%), 2023
• Figure 10. By Application Size Map-Market Size (USD Million) & Growth Rate (%), 2023
• Figure 11. By Deployment Mode Map-Market Size (USD Million) & Growth Rate (%), 2023
• Figure 12. By Farm Size Map-Market Size (USD Million) & Growth Rate (%), 2023
• Figure 13. By Region Map-Market Size (USD Million) & Growth Rate (%), 2023