農業人工智慧市場機會、成長動力、產業趨勢分析及 2025 - 2034 年預測

2024年，全球農業人工智慧市場規模達47億美元，預計2034年將以26.3%的複合年成長率成長，達到466億美元。這得歸功於人工智慧技術日益普及，旨在提高農業生產力、最佳化資源利用率並解決農業勞動力短缺問題。機器學習演算法、預測分析和自動化等人工智慧應用正被用於改善作物監測、病害檢測、灌溉管理和產量預測。

人工智慧技術使農民能夠從海量資料集中獲取即時洞察，從而最佳化資源利用，最大限度地減少作物損失，並提高整體產量品質。這種精準驅動的方法不僅提高了營運效率，還促進了永續農業方法的發展，例如定向灌溉、預測性病蟲害管理和土壤健康監測。透過將人工智慧融入日常農場營運，生產者可以預測挑戰、減少浪費並快速應對環境變化——所有這些對於在資源受限的世界中滿足日益成長的全球糧食需求至關重要。

2024年，解決方案領域佔據市場主導地位，產值達33億美元，預計2034年將達到310億美元。基於人工智慧的解決方案涵蓋廣泛的應用，包括作物監測、病害檢測、精準種植、智慧灌溉和產量預測。這些軟體平台分析來自感測器、無人機和衛星影像的資料，為農民提供切實可行的建議。人工智慧解決方案的可擴展性和靈活性使其適用於各種作物、地理和耕作方式，與單一服務相比，其經濟性和有效性更高。大多數人工智慧農業解決方案基於雲端技術，操作簡便，方便在任何規模的農場輕鬆實施。

機器學習 (ML) 在 2024 年佔據了 50% 的市場佔有率，預計將實現大幅成長。 ML 演算法擅長處理農業領域的大量結構化和非結構化資料，從而實現精準預測。 ML 廣泛應用於產量預測、疾病檢測和蟲害預測。隨著新資料的積累，這些模型會不斷改進，使 ML 成為一種多功能技術，為許多人工智慧驅動的農業解決方案奠定了基礎。從智慧灌溉和精準農業到市場預測和自動化機械，大多數人工智慧系統都依賴 ML 演算法，從而能夠基於即時和歷史資料流進行即時決策。

2024年，北美農業人工智慧市場佔36%的佔有率。美國在技術創新方面處於全球領先地位，尤其是在人工智慧和精準農業領域。大型科技公司已投資人工智慧和機器學習，以開發農業生產力解決方案。美國也擁有強大的研發生態系統，大學和政府計畫推動農業技術的進步。這些因素，加上高額的投資和強大的研發能力，使美國在農業人工智慧應用領域處於領先地位，並鞏固了其在全球市場的領導地位。

農業人工智慧市場的主要參與者包括：Gamaya、Corteva、John Deere、Taranis、aWhere、Trimble、IBM、Microsoft 和 Bayer Crop Science (Climate LLC)。這些公司正在積極開發和部署人工智慧驅動的解決方案，以加強農業實踐並應對農業面臨的挑戰。為了加強在農業人工智慧市場的地位，各公司正專注於多項策略性舉措。其中包括投資研發，以創建針對農民特定需求的創新人工智慧解決方案。正在與農業組織、研究機構和政府機構建立合作與夥伴關係，以開發和實施人工智慧驅動的解決方案，以應對糧食安全、永續性和氣候變遷等更廣泛的挑戰。透過進入新市場和在關鍵地區建立業務來擴大其全球影響力是另一種佔領更大市場佔有率的策略。

目錄

第1章：方法論與範圍

第2章：執行摘要

第3章：行業洞察

• 產業生態系統分析
  • 供應商格局
    • 技術提供者
    • 農業技術設備製造商
    • 農作物投入品公司
    • 物聯網感測器和影像解決方案提供商
    • 農業科技新創企業和資料整合商
  • 利潤率分析
• 川普政府關稅的影響
  • 對貿易的影響
    • 貿易量中斷
    • 報復措施
  • 對產業的影響
    • 主要材料價格波動
    • 供應鏈重組
    • 價格傳導至終端市場
  • 策略產業反應
    • 供應鏈重組
    • 定價和產品策略
• 技術與創新格局
• 重要新聞和舉措
• 成本細分分析
• 定價分析
  • 產品
  • 地區
• 專利分析
• 監管格局
• 衝擊力
  • 成長動力
    • 精準農業需求不斷成長
    • 勞動力短缺和自動化需求
    • 政府措施和資金支持
    • 氣候變遷和降低風險的必要性
  • 產業陷阱與挑戰
    • 實施人工智慧技術的初始成本高
    • 缺乏基礎設施和連結性
• 成長潛力分析
• 波特的分析
• PESTEL分析

第4章：競爭格局

• 介紹
• 公司市佔率分析
• 競爭定位矩陣
• 戰略展望矩陣

第5章：市場估計與預測：按組件，2021 - 2034 年

• 主要趨勢
• 解決方案
• 服務

第6章：市場估計與預測：依技術分類，2021 - 2034 年

• 主要趨勢
• 機器學習
• 電腦視覺
• 預測分析

第7章：市場估計與預測：按應用，2021 - 2034 年

• 主要趨勢
• 作物和土壤監測
• 牲畜健康監測
• 智慧噴塗
• 精準農業
• 農業機器人
• 資料
• 其他

第8章：市場估計與預測：依部署模式，2021 - 2034 年

• 主要趨勢
• 基於雲端
• 本地

第9章：市場預估與預測：依農場規模，2021 - 2034

• 主要趨勢
• 小農場
• 中型農場
• 大型農場

第10章：市場估計與預測：按地區，2021 - 2034 年

• 主要趨勢
• 北美洲
  • 美國
  • 加拿大
• 歐洲
  • 英國
  • 德國
  • 法國
  • 義大利
  • 西班牙
  • 俄羅斯
  • 北歐人
• 亞太地區
  • 中國
  • 印度
  • 日本
  • 韓國
  • 澳洲
  • 東南亞
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
• MEA
  • 阿拉伯聯合大公國
  • 沙烏地阿拉伯
  • 南非

第 11 章：公司簡介

• 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)