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市场调查报告书
商品编码
1995798

农业人工智慧市场：策略性洞察与预测（2026-2031 年）

AI for Agriculture Market - Strategic Insights and Forecasts (2026-2031)
出版日期: | 出版商: Knowledge Sourcing Intelligence | 英文 144 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

全球农业人工智慧市场预计将从 2026 年的 32 亿美元成长到 2031 年的 93 亿美元，复合年增长率为 23.8%。

人工智慧正成为推动现代农业转型发展的核心要素。这个市场策略性地处于三大因素的交会点：粮食安全压力、气候变迁、农业管理的数位化。海量的即时农业数据，涵盖土壤、天气、作物生长和牲畜生产力等方面，正在改变整个价值链的决策方式。人工智慧工具将这些资料流转化为可用于预测和营运的洞察，从而有助于提高产量品质、投入效率和资源管理水准。各国政府和研究机构也支持数位化农业项目，以提高生产力和韧性。因此，智慧系统在农业管理的应用正从实验阶段走向商业农业整体的系统化实施。

市场驱动因素

全球对农产品需求的持续成长仍然是推动农业成长的主要动力。人口成长和消费模式的改变给生产力和效率带来了越来越大的压力。人工智慧技术可以帮助优化种植决策、监测作物健康并改善资源配置。精密农业工具能够实现基于数据的肥料、灌溉和病虫害防治管理，从而提高产量稳定性并减少浪费。

政府对农业研发和数位基础设施的投入也促进了技术的普及应用。创新计画和实地试验的资助支持了先进耕作方法的开发，并加速了技术转移到农民的基础上。无人机、感测器和数据分析平台的日益普及进一步提高了作业可视性和决策的准确性。

畜牧监测也是一股强大的驱动力。以人工智慧为基础的系统能够即时追踪畜牧的健康指标、采食行为和环境状况。这些功能可以提高生产力、降低疾病风险，并实现更有效率的畜群管理。

市场限制因素

农民缺乏相关意识和技术知识仍然是一个主要障碍。在许多地区，人工智慧的实际价值尚未被充分认识，导致其应用推广落后。实施人工智慧通常需要培训、基础设施升级和系统整合支援。

区域间数位化准备程度的差异也限制了成长。在通讯基础设施有限或技术普及率低的地区，部署难度更高。供应商需要增加对教育、客製化和服务支援的投入，以扩大市场覆盖范围。

对技术和细分市场的洞察

市场按技术、应用和地理划分。机器学习、电脑视觉和预测分析构成核心技术领域。这些工具能够实现疾病的自动检测、产量预测和资源最佳化。

应用领域包括农业机器人、精密农业、无人机分析、牲畜监测和气象追踪。精密农业是其主要应用案例，可实现逐块田地管理和即时决策。电脑视觉和预测分析有助于监测作物生长、病虫害活动和环境状况。人工智慧机器人透过精准除草和提高喷洒精度来减少农药用量。

从区域来看，北美地区在自动化、物联网（IoT）融合以及研发投入的推动下，正经历强劲的成长。然而，随着数位农业成为开发中国家的战略重点，其应用正在全球范围内不断扩展。

竞争格局与策略展望

竞争格局包括技术开发公司、农业技术公司和多元化农业解决方案供应商。市场参与企业专注于产品创新、分析能力和平台整合。与研究机构和农业组织的策略合作有助于商业化和业务拓展。

各公司正在投资机器人技术、感测器网路和基于云端的决策平台。预计市场将继续在硬体、软体和分析生态系统之间开展合作，以提供整合的农场管理解决方案。

重点

人工智慧正透过数据驱动的决策和自动化改变农业生产。儘管普及应用面临许多障碍，但持续的创新、政府的支持以及提高生产力的压力将推动市场持续扩张。智慧农业技术正成为现代农业不可或缺的一部分。

本报告的主要益处

  • 深入分析：获得跨地区、客户群、政策、社会经济因素、消费者偏好和产业领域的详细市场洞察。
  • 竞争格局：了解主要企业的策略趋势，并确定最佳的市场进入方式。
  • 市场驱动因素与未来趋势：我们评估影响市场的关键成长要素和新兴趋势。
  • 实用建议：我们支援制定策略决策以开发新的收入来源。
  • 适合各类读者：非常适合Start-Ups、研究机构、顾问公司、中小企业和大型企业。

我们的报告的使用范例

产业和市场洞察、机会评估、产品需求预测、打入市场策略、区域扩张、资本投资决策、监管分析、新产品开发和竞争情报。

报告范围

  • 2021年至2025年的历史数据和2026年至2031年的预测数据
  • 成长机会、挑战、供应链前景、法律规范与趋势分析
  • 竞争定位、策略和市场占有率评估
  • 细分市场和区域销售成长及预测评估
  • 公司简介，包括策略、产品、财务状况和主要发展动态。

目录

第一章：引言

  • 市场概览
  • 市场的定义
  • 调查范围
  • 市场区隔
  • 货币
  • 先决条件
  • 基准年及预测年调查期
  • 相关人员的主要收益

第二章：调查方法

  • 调查设计
  • 研究过程

第三章执行摘要

  • 主要发现
  • 首席体验长的观点

第四章 市场动态

  • 市场驱动因素
  • 市场限制因素
  • 波特五力分析
  • 产业价值链分析
  • 分析师意见

第五章：农业人工智慧市场：依技术划分

  • 机器学习
  • 电脑视觉
  • 预测分析

第六章：人工智慧农业市场：按应用领域划分

  • 农业机器人
  • 精密农业
  • 无人机分析
  • 牲畜监测
  • 天气追踪
  • 其他的

第七章：农业人工智慧市场：按地区划分

  • 北美洲
    • 透过技术
    • 透过使用
    • 国家
      • 我们
      • 加拿大
      • 墨西哥
  • 南美洲
    • 透过技术
    • 透过使用
    • 国家
      • 巴西
      • 阿根廷
      • 其他的
  • 欧洲
    • 透过技术
    • 透过使用
    • 国家
      • 德国
      • 法国
      • 英国
      • 西班牙
      • 其他的
  • 中东和非洲
    • 透过技术
    • 透过使用
    • 国家
      • 沙乌地阿拉伯
      • 以色列
      • UAE
      • 其他的
  • 亚太地区
    • 透过技术
    • 透过使用
    • 国家
      • 中国
      • 日本
      • 印度
      • 韩国
      • 印尼
      • 越南
      • 泰国
      • 其他的

第八章：竞争环境与分析

  • 主要企业及策略分析
  • 市占率分析
  • 合併、收购、协议和合作关係
  • 竞争环境仪錶板

第九章：公司简介

  • Gamaya SA
  • IBM Corporation
  • Trimble Inc.
  • Bayer AG
  • Prospera Technologies Ltd.
  • PrecisionHawk Inc.
  • Cainthus Corp.
  • AGCO Corporation
  • Deere & Company
  • Farmers Edge Inc.
简介目录
Product Code: KSI061615529

The Global AI for Agriculture market is forecast to grow at a CAGR of 23.8%, reaching USD 9.3 billion in 2031 from USD 3.2 billion in 2026.

Artificial intelligence is becoming a core enabler of modern agricultural transformation. The market is strategically positioned at the intersection of food security pressures, climate variability, and the digitalization of farm operations. Large volumes of real-time agricultural data related to soil, weather, crop health, and livestock performance are reshaping decision-making across the value chain. AI tools convert these data streams into predictive and operational insights, improving yield quality, input efficiency, and resource management. Governments and research institutions are also supporting digital agriculture initiatives to strengthen productivity and resilience. The integration of intelligent systems into farming operations is therefore transitioning from experimental deployment to structured implementation across commercial agriculture.

Market Drivers

Rising global demand for agricultural output remains a primary growth catalyst. Population growth and changing consumption patterns are increasing pressure on productivity and efficiency. AI technologies help optimize planting decisions, monitor crop health, and improve resource allocation. Precision farming tools enable data-driven management of fertilizers, irrigation, and pest control, which improves yield stability and reduces waste.

Government investment in agricultural research and digital infrastructure is also strengthening adoption. Funding for innovation programs and field demonstrations supports the development of advanced cultivation methods and accelerates technology transfer to farmers. The growing use of drones, sensors, and data analytics platforms further enhances operational visibility and decision accuracy.

Livestock monitoring represents another strong driver. AI-based systems track animal health indicators, feeding behavior, and environmental conditions in real time. These capabilities improve productivity, reduce disease risks, and enable more efficient herd management.

Market Restraints

Limited awareness and technical understanding among farmers remain a major barrier. In many regions, the practical value of AI is not fully understood, slowing adoption. Implementation often requires training, infrastructure upgrades, and system integration support.

Regional disparities in digital readiness also constrain growth. Adoption is more difficult in areas with limited connectivity or lower technology penetration. Vendors must invest in education, customization, and service support to expand market reach.

Technology and Segment Insights

The market is structured across technology, application, and geography. Machine learning, computer vision, and predictive analytics form the core technological segments. These tools enable automated disease detection, yield forecasting, and resource optimization.

Application segments include agricultural robots, precision farming, drone analytics, livestock monitoring, and weather tracking. Precision farming is a central use case, allowing site-specific management and real-time decision-making. Computer vision and predictive analytics support monitoring of crop growth, pest activity, and environmental conditions. AI-enabled robotics reduce chemical usage by targeting weeds and improving spraying accuracy.

Regionally, North America demonstrates strong growth due to investment in automation, Internet of Things integration, and research activity. However, adoption is expanding globally as digital agriculture becomes a strategic priority across developing and developed economies.

Competitive and Strategic Outlook

The competitive landscape includes technology developers, agritech firms, and diversified agricultural solution providers. Market participants focus on product innovation, analytics capabilities, and platform integration. Strategic partnerships with research institutions and agricultural organizations support commercialization and scaling.

Companies are investing in robotics, sensor networks, and cloud-based decision platforms. The market is expected to see continued collaboration across hardware, software, and analytics ecosystems to deliver integrated farm management solutions.

Key Takeaways

AI is reshaping agricultural production through data-driven decision-making and automation. Despite adoption barriers, ongoing innovation, government support, and productivity pressures will sustain market expansion. Intelligent farming technologies are becoming a structural component of modern agriculture.

Key Benefits of this Report

  • Insightful Analysis: Gain detailed market insights across regions, customer segments, policies, socio-economic factors, consumer preferences, and industry verticals.
  • Competitive Landscape: Understand strategic moves by key players to identify optimal market entry approaches.
  • Market Drivers and Future Trends: Assess major growth forces and emerging developments shaping the market.
  • Actionable Recommendations: Support strategic decisions to unlock new revenue streams.
  • Caters to a Wide Audience: Suitable for startups, research institutions, consultants, SMEs, and large enterprises.

What businesses use our reports for

Industry and market insights, opportunity assessment, product demand forecasting, market entry strategy, geographical expansion, capital investment decisions, regulatory analysis, new product development, and competitive intelligence.

Report Coverage

  • Historical data from 2021 to 2025 and forecast data from 2026 to 2031
  • Growth opportunities, challenges, supply chain outlook, regulatory framework, and trend analysis
  • Competitive positioning, strategies, and market share evaluation
  • Revenue growth and forecast assessment across segments and regions
  • Company profiling including strategies, products, financials, and key developments

TABLE OF CONTENTS

1. INTRODUCTION

  • 1.1. Market Overview
  • 1.2. Market Definition
  • 1.3. Scope of the Study
  • 1.4. Market Segmentation
  • 1.5. Currency
  • 1.6. Assumptions
  • 1.7. Base and Forecast Years Timeline
  • 1.8. Key Benefits to the Stakeholder

2. RESEARCH METHODOLOGY

  • 2.1. Research Design
  • 2.2. Research Processes

3. EXECUTIVE SUMMARY

  • 3.1. Key Findings
  • 3.2. CXO Perspective

4. MARKET DYNAMICS

  • 4.1. Market Drivers
  • 4.2. Market Restraints
  • 4.3. Porter's Five Forces Analysis
    • 4.3.1. Bargaining Power of Suppliers
    • 4.3.2. Bargaining Power of Buyers
    • 4.3.3. Threat of New Entrants
    • 4.3.4. Threat of Substitutes
    • 4.3.5. Competitive Rivalry in the Industry
  • 4.4. Industry Value Chain Analysis
  • 4.5. Analyst View

5. AI FOR AGRICULTURE MARKET BY TECHNOLOGY

  • 5.1. Introduction
  • 5.2. Machine Learning
  • 5.3. Computer Vision
  • 5.4. Predictive Analytics

6. AI FOR AGRICULTURE MARKET BY APPLICATION

  • 6.1. Introduction
  • 6.2. Agricultural Robots
  • 6.3. Precision Farming
  • 6.4. Drone Analytics
  • 6.5. Livestock Monitoring
  • 6.6. Weather Tracking
  • 6.7. Others

7. AI FOR AGRICULTURE MARKET BY GEOGRAPHY

  • 7.1. Introduction
  • 7.2. North America
    • 7.2.1. By Technology
    • 7.2.2. By Application
    • 7.2.3. By Country
      • 7.2.3.1. USA
      • 7.2.3.2. Canada
      • 7.2.3.3. Mexico
  • 7.3. South America
    • 7.3.1. By Technology
    • 7.3.2. By Application
    • 7.3.3. By Country
      • 7.3.3.1. Brazil
      • 7.3.3.2. Argentina
      • 7.3.3.3. Others
  • 7.4. Europe
    • 7.4.1. By Technology
    • 7.4.2. By Application
    • 7.4.3. By Country
      • 7.4.3.1. Germany
      • 7.4.3.2. France
      • 7.4.3.3. United Kingdom
      • 7.4.3.4. Spain
      • 7.4.3.5. Others
  • 7.5. Middle East and Africa
    • 7.5.1. By Technology
    • 7.5.2. By Application
    • 7.5.3. By Country
      • 7.5.3.1. Saudi Arabia
      • 7.5.3.2. Israel
      • 7.5.3.3. UAE
      • 7.5.3.4. Others
  • 7.6. Asia Pacific
    • 7.6.1. By Technology
    • 7.6.2. By Application
    • 7.6.3. By Country
      • 7.6.3.1. China
      • 7.6.3.2. Japan
      • 7.6.3.3. India
      • 7.6.3.4. South Korea
      • 7.6.3.5. Indonesia
      • 7.6.3.6. Vietnam
      • 7.6.3.7. Thailand
      • 7.6.3.8. Others

8. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 8.1. Major Players and Strategy Analysis
  • 8.2. Market Share Analysis
  • 8.3. Mergers, Acquisitions, Agreements, and Collaborations
  • 8.4. Competitive Dashboard

9. COMPANY PROFILES

  • 9.1. Gamaya SA
  • 9.2. IBM Corporation
  • 9.3. Trimble Inc.
  • 9.4. Bayer AG
  • 9.5. Prospera Technologies Ltd.
  • 9.6. PrecisionHawk Inc.
  • 9.7. Cainthus Corp.
  • 9.8. AGCO Corporation
  • 9.9. Deere & Company
  • 9.10. Farmers Edge Inc.