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市場調查報告書
商品編碼
1995798

農業人工智慧市場：策略性洞察與預測（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個工作天內

價格
簡介目錄

全球農業人工智慧市場預計將從 2026 年的 32 億美元成長到 2031 年的 93 億美元，複合年成長率為 23.8%。

人工智慧正成為推動現代農業轉型發展的核心要素。這個市場策略性地處於三大因素的交會點：糧食安全壓力、氣候變遷、農業管理的數位化。海量的即時農業數據，涵蓋土壤、天氣、作物生長和牲畜生產力等方面，正在改變整個價值鏈的決策方式。人工智慧工具將這些資料流轉化為可用於預測和營運的洞察，從而有助於提高產量品質、投入效率和資源管理水準。各國政府和研究機構也支持數位化農業項目，以提高生產力和韌性。因此，智慧系統在農業管理的應用正從實驗階段走向商業農業整體的系統化實施。

市場促進因素

全球對農產品需求的持續成長仍然是推動農業成長的主要動力。人口成長和消費模式的改變給生產力和效率帶來了越來越大的壓力。人工智慧技術可以幫助最佳化種植決策、監測作物健康並改善資源配置。精密農業工具能夠實現基於數據的肥料、灌溉和病蟲害防治管理，從而提高產量穩定性並減少浪費。

政府對農業研發和數位基礎設施的投入也促進了技術的普及應用。創新計畫和實地試驗的資助支持了先進耕作方法的開發，並加速了技術轉移到農民的基礎上。無人機、感測器和數據分析平台的日益普及進一步提高了作業可視性和決策的準確性。

畜牧監測也是一股強大的驅動力。以人工智慧為基礎的系統能夠即時追蹤畜牧的健康指標、採食行為和環境狀況。這些功能可以提高生產力、降低疾病風險，並實現更有效率的畜群管理。

市場限制因素

農民缺乏相關意識和技術知識仍然是一個主要障礙。在許多地區，人工智慧的實際價值尚未被充分認知，導致其應用推廣落後。實施人工智慧通常需要培訓、基礎設施升級和系統整合支援。

區域間數位化準備程度的差異也限制了成長。在通訊基礎設施有限或技術普及率低的地區，部署難度更高。供應商需要增加對教育、客製化和服務支援的投入，以擴大市場覆蓋範圍。

對技術和細分市場的洞察

市場按技術、應用和地理分類。機器學習、電腦視覺和預測分析構成核心技術領域。這些工具能夠實現疾病的自動檢測、產量預測和資源最佳化。

應用領域包括農業機器人、精密農業、無人機分析、牲畜監測和氣象追蹤。精密農業是其主要應用案例，可實現逐塊田地管理和即時決策。電腦視覺和預測分析有助於監測作物生長、病蟲害活動和環境狀況。人工智慧機器人透過精準除草和提高噴灑精度來減少農藥用量。

從區域來看，北美地區在自動化、物聯網（IoT）融合以及研發投入的推動下，正經歷強勁的成長。然而，隨著數位農業成為開發中國家的戰略重點，其應用正在全球範圍內不斷擴展。

競爭格局與策略展望

競爭格局包括技術開發公司、農業技術公司和多元化農業解決方案供應商。市場參與企業專注於產品創新、分析能力和平台整合。與研究機構和農業組織的策略合作有助於商業化和業務拓展。

各公司正在投資機器人技術、感測器網路和基於雲端的決策平台。預計市場將繼續在硬體、軟體和分析生態系統之間開展合作，以提供整合的農場管理解決方案。

重點

人工智慧正透過數據驅動的決策和自動化改變農業生產。儘管普及應用面臨許多障礙，但持續的創新、政府的支持以及提高生產力的壓力將推動市場持續擴張。智慧農業技術正成為現代農業不可或缺的一部分。

本報告的主要益處

  • 深入分析：獲得跨地區、客戶群、政策、社會經濟因素、消費者偏好和產業領域的詳細市場洞察。
  • 競爭格局：了解主要企業的策略趨勢，並確定最佳的市場進入方式。
  • 市場促進因素與未來趨勢：我們評估影響市場的關鍵成長要素和新興趨勢。
  • 實用建議：我們支援制定策略決策以開發新的收入來源。
  • 適合各類讀者：非常適合Start-Ups、研究機構、顧問公司、中小企業和大型企業。

我們的報告的使用範例

產業和市場洞察、機會評估、產品需求預測、打入市場策略、區域擴張、資本投資決策、監管分析、新產品開發和競爭情報。

報告範圍

  • 2021年至2025年的歷史數據和2026年至2031年的預測數據
  • 成長機會、挑戰、供應鏈前景、法律規範與趨勢分析
  • 競爭定位、策略和市場佔有率評估
  • 細分市場和區域銷售成長及預測評估
  • 公司簡介，包括策略、產品、財務狀況和主要發展動態。

目錄

第1章：引言

  • 市場概覽
  • 市場的定義
  • 調查範圍
  • 市場區隔
  • 貨幣
  • 先決條件
  • 基準年及預測年調查期
  • 相關人員的主要收益

第2章：調查方法

  • 調查設計
  • 研究過程

第3章執行摘要

  • 主要發現
  • 首席體驗長的觀點

第4章 市場動態

  • 市場促進因素
  • 市場限制因素
  • 波特五力分析
  • 產業價值鏈分析
  • 分析師意見

第5章：農業人工智慧市場：依技術分類

  • 機器學習
  • 電腦視覺
  • 預測分析

第6章：人工智慧農業市場：按應用領域分類

  • 農業機器人
  • 精密農業
  • 無人機分析
  • 牲畜監測
  • 天氣追蹤
  • 其他

第7章：農業人工智慧市場：按地區分類

  • 北美洲
    • 透過技術
    • 透過使用
    • 國家
      • 美國
      • 加拿大
      • 墨西哥
  • 南美洲
    • 透過技術
    • 透過使用
    • 國家
      • 巴西
      • 阿根廷
      • 其他
  • 歐洲
    • 透過技術
    • 透過使用
    • 國家
      • 德國
      • 法國
      • 英國
      • 西班牙
      • 其他
  • 中東和非洲
    • 透過技術
    • 透過使用
    • 國家
      • 沙烏地阿拉伯
      • 以色列
      • UAE
      • 其他
  • 亞太地區
    • 透過技術
    • 透過使用
    • 國家
      • 中國
      • 日本
      • 印度
      • 韓國
      • 印尼
      • 越南
      • 泰國
      • 其他

第8章：競爭環境與分析

  • 主要企業及策略分析
  • 市佔率分析
  • 合併、收購、協議和合作關係
  • 競爭環境儀錶板

第9章：公司簡介

  • Gamaya SA
  • IBM Corporation
  • Trimble Inc.
  • Bayer AG
  • Prospera Technologies Ltd.
  • PrecisionHawk Inc.
  • Cainthus Corp.
  • AGCO Corporation
  • Deere & Company
  • Farmers Edge Inc.
簡介目錄
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.