农业人工智慧市场机会、成长动力、产业趋势分析及 2025 - 2034 年预测

2024年，全球农业人工智慧市场规模达47亿美元，预计2034年将以26.3%的复合年增长率成长，达到466亿美元。这得归功于人工智慧技术日益普及，旨在提高农业生产力、优化资源利用率并解决农业劳动力短缺问题。机器学习演算法、预测分析和自动化等人工智慧应用正被用于改善作物监测、病害检测、灌溉管理和产量预测。

人工智慧技术使农民能够从海量资料集中获取即时洞察，从而优化资源利用，最大限度地减少作物损失，并提高整体产量品质。这种精准驱动的方法不仅提高了营运效率，还促进了永续农业方法的发展，例如定向灌溉、预测性病虫害管理和土壤健康监测。透过将人工智慧融入日常农场运营，生产者可以预测挑战、减少浪费并快速应对环境变化——所有这些对于在资源受限的世界中满足日益增长的全球粮食需求至关重要。

2024年，解决方案领域占据市场主导地位，产值达33亿美元，预计2034年将达到310亿美元。基于人工智慧的解决方案涵盖广泛的应用，包括作物监测、病害检测、精准种植、智慧灌溉和产量预测。这些软体平台分析来自感测器、无人机和卫星影像的资料，为农民提供切实可行的建议。人工智慧解决方案的可扩展性和灵活性使其适用于各种作物、地理和耕作方式，与单一服务相比，其经济性和有效性更高。大多数人工智慧农业解决方案基于云端技术，操作简便，方便在任何规模的农场轻鬆实施。

机器学习 (ML) 在 2024 年占据了 50% 的市场份额，预计将实现大幅成长。 ML 演算法擅长处理农业领域的大量结构化和非结构化资料，从而实现精准预测。 ML 广泛应用于产量预测、疾病检测和虫害预测。随着新资料的积累，这些模型会不断改进，使 ML 成为一种多功能技术，为许多人工智慧驱动的农业解决方案奠定了基础。从智慧灌溉和精准农业到市场预测和自动化机械，大多数人工智慧系统都依赖 ML 演算法，从而能够基于即时和历史资料流进行即时决策。

2024年，北美农业人工智慧市场占36%的份额。美国在技术创新方面处于全球领先地位，尤其是在人工智慧和精准农业领域。大型科技公司已投资人工智慧和机器学习，以开发农业生产力解决方案。美国也拥有强大的研发生态系统，大学和政府计画推动农业技术的进步。这些因素，加上高额的投资和强大的研发能力，使美国在农业人工智慧应用领域处于领先地位，并巩固了其在全球市场的领导地位。

农业人工智慧市场的主要参与者包括：Gamaya、Corteva、John Deere、Taranis、aWhere、Trimble、IBM、Microsoft 和 Bayer Crop Science (Climate LLC)。这些公司正在积极开发和部署人工智慧驱动的解决方案，以加强农业实践并应对农业面临的挑战。为了加强在农业人工智慧市场的地位，各公司正专注于多项策略性倡议。其中包括投资研发，以创建针对农民特定需求的创新人工智慧解决方案。正在与农业组织、研究机构和政府机构建立合作与伙伴关係，以开发和实施人工智慧驱动的解决方案，以应对粮食安全、永续性和气候变迁等更广泛的挑战。透过进入新市场和在关键地区建立业务来扩大其全球影响力是另一种占领更大市场份额的策略。

目录

第一章：方法论与范围

第二章：执行摘要

第三章：行业洞察

• 产业生态系统分析
  • 供应商格局
    • 技术提供者
    • 农业技术设备製造商
    • 农作物投入品公司
    • 物联网感测器和影像解决方案提供商
    • 农业科技新创企业和资料整合商
  • 利润率分析
• 川普政府关税的影响
  • 对贸易的影响
    • 贸易量中断
    • 报復措施
  • 对产业的影响
    • 主要材料价格波动
    • 供应链重组
    • 价格传导至终端市场
  • 策略产业反应
    • 供应链重组
    • 定价和产品策略
• 技术与创新格局
• 重要新闻和倡议
• 成本細項分析
• 定价分析
  • 产品
  • 地区
• 专利分析
• 监管格局
• 衝击力
  • 成长动力
    • 精准农业需求不断成长
    • 劳动力短缺和自动化需求
    • 政府措施和资金支持
    • 气候变迁和降低风险的必要性
  • 产业陷阱与挑战
    • 实施人工智慧技术的初始成本高
    • 缺乏基础设施和连结性
• 成长潜力分析
• 波特的分析
• PESTEL分析

第四章：竞争格局

• 介绍
• 公司市占率分析
• 竞争定位矩阵
• 战略展望矩阵

第五章：市场估计与预测：按组件，2021 - 2034 年

• 主要趋势
• 解决方案
• 服务

第六章：市场估计与预测：依技术分类，2021 - 2034 年

• 主要趋势
• 机器学习
• 电脑视觉
• 预测分析

第七章：市场估计与预测：按应用，2021 - 2034 年

• 主要趋势
• 作物和土壤监测
• 牲畜健康监测
• 智慧喷涂
• 精准农业
• 农业机器人
• 资料
• 其他的

第八章：市场估计与预测：依部署模式，2021 - 2034 年

• 主要趋势
• 基于云端
• 本地

第九章：市场预估与预测：依农场规模，2021 - 2034

• 主要趋势
• 小农场
• 中型农场
• 大型农场

第十章：市场估计与预测：按地区，2021 - 2034 年

• 主要趋势
• 北美洲
  • 我们
  • 加拿大
• 欧洲
  • 英国
  • 德国
  • 法国
  • 义大利
  • 西班牙
  • 俄罗斯
  • 北欧人
• 亚太地区
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲
  • 东南亚
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
• MEA
  • 阿联酋
  • 沙乌地阿拉伯
  • 南非

第 11 章：公司简介

• AWhere
• Bayer Crop Science (Climate LLC)
• Benson Hill Biosystems
• Blue River Technology
• Bluewhite
• Carbon Robotics
• Corteva Agriscience
• Cropin
• ec2ce
• Ever.Ag (includes Cainthus Corp)
• FarmWise
• Gamaya
• Hippo Harvest
• IBM
• John Deere
• Microsoft
• Taranis
• Trimble
• Tule Technologies
• Valmont Industries (Prospera Technologies)

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)