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市場調查報告書
商品編碼
2141438

人工智慧在農業領域的應用：市場機會、成長促進因素、產業趨勢分析及2026-2035年預測

AI in Agriculture Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026 - 2035
出版日期: | 出版商: Global Market Insights Inc. | 英文 282 Pages | 商品交期: 2-3個工作天內

價格
簡介目錄

預計到 2025 年，全球農業人工智慧市場規模將達到 42 億美元，並以 18.8% 的複合年成長率成長，到 2035 年將達到 239 億美元。

市場範圍
開始年份 2025
預測期 2026-2035
起始金額 42億美元
預測金額 239億美元
複合年成長率 18.8%

隨著農業部門採用數位轉型和精密農業技術以提高生產力和資源效率，農業部門正在快速擴張。人工智慧（AI）透過先進的分析和智慧自動化，賦能數據驅動的決策，進而改變現代農業。人工智慧平台處理來自多個互聯資訊來源的即時訊息，使農民能夠改進作物監測、灌溉管理、產量預測、牲畜管理、病蟲害識別、疾病檢測和土壤健康管理。在糧食生產壓力日益增大，以及應對勞動力短缺、氣候變遷和永續性目標的迫切需求下，人工智慧農業解決方案在全球的普及速度正在加快。對數位化農場管理、精密農業、可控環境農業和智慧農場基礎設施​​的投資增加，進一步推動了市場成長。農業相關企業正擴大採用人工智慧技術來最佳化田間作業、提高資源利用率、實現關鍵農業活動的自動化，並支援更有效率的生產計畫。此外，人工智慧在促進永續農業實踐方面發揮著至關重要的作用，它能夠減少不必要的資源消耗、提高用水效率、增強土壤生產力、降低農業排放，並支持長期的環境韌性。

預計到2025年，硬體細分市場將佔據45.9%的市場佔有率，並在2035年之前以18.1%的複合年成長率成長。硬體仍然是最大的細分市場，因為它為人工智慧驅動的農業運作提供了必要的實體基礎設施。對互聯農業設備、智慧感測技術、自動化機械和精密農業系統的投資不斷增加，持續推動先進硬體解決方案的需求。隨著商業農業數位轉型的加速，智慧田間設備和連網型設備的支出持續超過軟體平台的投資。

預計到2025年，機器學習和深度學習領域將佔據32%的市場佔有率，並在2026年至2035年間保持19.1%的複合年成長率。該領域之所以能維持領先地位，是因為先進的學習演算法能夠對海量農業數據進行精準分析，進而有助於提升農業營運整體環節的決策品質。預測精度的不斷提高、營運計劃的最佳化以及智慧推薦的生成，正推動機器學習和深度學習技術在農業領域的進一步應用。

預計到2025年，美國農業人工智慧市佔率將達到86.5%，市場規模將達13億美元。這一市場擴張的驅動力來自美國先進的農業產業、精密農業技術的日益普及以及對人工智慧、雲端運算和數位農業解決方案投資的增加。商業農戶不斷採用人工智慧技術來提高營運效率、最佳化資源管理、提升作物產量並增強農業的長期永續性。技術供應商和農業機械製造商的持續創新也推動了人工智慧農業解決方案在美國的進一步普及。

目錄

第1章：調查方法

第2章執行摘要

第3章 行業洞察

  • 產業生態系分析
    • 供應商情況
    • 利潤率分析
    • 成本結構
    • 每個階段增加的價值
    • 影響價值鏈的因素
    • 中斷
  • 影響產業的因素
    • 成長促進因素
      • 擴大精密農業和智慧農業的應用
      • 人手不足日益嚴重，推動了農業自動化進程。
      • 農業數據可用性提高和物聯網連接性擴展
      • 人們越來越關注永續農業和資源最佳化利用。
      • 政府加大對農業科技和人工智慧創新領域的支持和投資。
    • 產業潛在風險與挑戰
      • 前期投資和實施成本高。
      • 遍遠地區缺乏數位基礎設施和通訊環境
    • 市場機遇
      • 利用世代人工智慧擴展農業諮詢平台
      • 擴大人工智慧驅動的自主農業機械的引入
      • 人工智慧與氣候適應型和再生型農業的融合正在取得進展。
      • 農業供應鏈對人工智慧驅動的可追溯性的需求日益成長
      • 新興農業經濟體的快速數位轉型
  • 成長潛力分析
  • 技術與創新展望
    • 最新科技趨勢
    • 新興技術
  • 價格分析
    • 2022-2025年歷史價格趨勢分析
    • 依球員類型分類的定價策略（高級球員、超值球員、成本加成球員）
  • 監理情勢
    • 北美洲
      • 美國：人工智慧管治、精密農業計劃和美國農業部數位農業計劃
      • 加拿大：人工智慧法規、智慧農業政策與永續農業框架
    • 歐洲
      • 德國：人工智慧法律合規性、數位農業戰略和精密農業政策
      • 英國：人工智慧法規、農業技術策略與自主農業機械框架
      • 法國：數位農業藍圖與人工智慧在永續農業中的創新
      • 義大利：智慧農業計畫、農業數位化與人工智慧實施方案
    • 亞太地區
      • 印度：數位農業計畫、農業人工智慧舉措和無人機監管
      • 中國：智慧農業發展計畫、人工智慧創新政策與農業現代化
      • 日本：社會5.0、智慧農業推進及農業機器人框架
      • 澳洲：國家人工智慧戰略、數位農業計畫和精密農業標準
    • 拉丁美洲
      • 巴西：國家物聯網計劃、數位農業戰略和精密農業計劃
      • 墨西哥：智慧農業政策、農業數位化與人工智慧創新舉措
      • 阿根廷：推廣精密農業、農業科技創新與數位農業框架
    • 中東和非洲（MEA）
      • 以色列：人工智慧創新、精密農業技術與智慧灌溉政策
      • 南非：數位化農業策略及人工智慧助力農業現代化
      • 肯亞：數位農業策略、人工智慧諮詢服務和智慧農業計劃
      • 阿拉伯聯合大公國：「2031年國家人工智慧戰略」、智慧農業計畫和糧食安全舉措
      • 沙烏地阿拉伯：「2030願景」、向智慧農業轉型以及人工智慧驅動的糧食安全計劃
  • 波特的分析
  • PESTLE分析
  • 專利趨勢
  • 用例
    • 商業化
    • 早期採用
    • 試點階段
    • 新興
  • 人工智慧和生成式人工智慧對市場的影響
    • 利用人工智慧改造現有經營模式
    • 按細分市場分類的生成式人工智慧用例和部署藍圖
    • 風險、限制和監管考量
  • 基礎設施和部署狀況，2022-2025 年
    • 按地區和買家群體分類的人工智慧解決方案採用率
    • 基礎設施投資的可擴展性限制和趨勢
  • 預測假設和情境分析
    • 基本案例：驅動複合年成長率的關鍵宏觀經濟與產業變量
    • 樂觀情境：宏觀經濟與產業的順風
    • 悲觀情景：宏觀經濟放緩或產業逆風

第4章 競爭情勢

  • 介紹
  • 主要市場公司的競爭分析
  • 競爭定位矩陣
  • 主要進展
    • 併購
    • 夥伴關係和聯盟
    • 新產品發布
    • 業務拓展計劃及資金籌措
  • 按公司規模進行基準測試
    • 排名分類標準與遴選標準
    • 層級定位矩陣：依收入、地區和創新能力分類

第5章 市場估算與預測：依產品類型分類，2022-2035年

  • 硬體
    • 感測器和物聯網設備
    • 無人機和無人駕駛飛行器（UAV）
    • 機器人與自主機器
    • 影像、掃描和視覺設備
  • 軟體
    • 人工智慧平台和分析工具
    • 農業管理資訊系統（FMIS）
    • 供應鍊和物流管理軟體
    • 決策支援和預測智慧軟體
  • 服務
    • 諮詢和系統整合服務
    • 託管式和訂閱式人工智慧服務
    • 培訓、支援和售後服務

第6章 市場估計與預測：依技術分類，2022-2035年

  • 機器學習和深度學習
  • 電腦視覺和影像識別
  • 預測分析和資料科學
  • 自然語言處理（NLP）
  • 生成式人工智慧和基礎模型

第7章 市場估計與預測：依應用領域分類，2022-2035年

  • 田間生產和作物管理
    • 精密農業和變數施肥
    • 作物監測、病蟲害檢測
    • 產量預測與最佳化
    • 灌溉和水資源管理
    • 土壤健康和肥力管理
  • 畜牧業及動物畜牧業
    • 健康監測和疾病檢測
    • 飼料最佳化和營養管理
    • 生殖週期管理
  • 收穫和收穫後管理
    • 最佳化收穫時間
    • 採後品質檢驗與分級
    • 檢測和減少食物損失和廢棄物
  • 倉儲和冷鏈管理
    • 智慧倉庫和儲存狀態監控
    • 冷鏈狀態和溫度控制
    • 庫存最佳化和保存期限預測
  • 供應鏈、物流、配送
    • 路線最佳化和車輛管理
    • 需求預測與市場訊息
    • 農業供應鏈的可追溯性和透明度
    • 最佳化最後一公里配送
  • 農場管理和決策支持
    • 農場管理資訊系統（FMIS）
    • 農業風險評估與作物保險
    • 市場價格資訊和交易支持

第8章 市場估算與預測：依部署模式分類，2022-2035年

  • 基於雲端的
  • 現場
  • 混合邊緣

第9章 市場估計與預測：依最終用途分類，2022-2035年

  • 大型農業企業及農產品
  • 中小農場和合作社
  • 食品加工包裝公司
  • 物流/配送業務
  • 零售商和餐飲公司
  • 政府和研究機構
  • 其他

第10章 市場估價與預測：依地區分類，2022-2035年

  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 俄羅斯
    • 荷蘭
    • 比利時
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 韓國
    • 新加坡
    • 泰國
    • 印尼
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • 中東和非洲
    • 南非
    • 沙烏地阿拉伯
    • UAE

第11章：公司簡介

  • 世界公司
    • Accenture
    • AGCO
    • Carbon Robotics
    • CNH Industrial
    • DJI Agriculture
    • John Deere
    • Kubota
    • Microsoft
    • The Climate
    • Trimble Agriculture
  • 本地公司
    • Ag Leader Technology
    • CLAAS
    • CropIn Technology
    • Naio Technologies
    • SDF（SAME Deutz-Fahr）
    • Semios
    • XAG
  • 新興企業
    • Ceres AI
    • CropX Technologies
    • Taranis
簡介目錄
Product Code: 5856

The Global AI in Agriculture Market was valued at USD 4.2 billion in 2025 and is estimated to grow at a CAGR of 18.8% to reach USD 23.9 billion by 2035.

Market Scope
Start Year2025
Forecast Year2026-2035
Start Value$4.2 Billion
Forecast Value$23.9 Billion
CAGR18.8%

The market is expanding rapidly as the agriculture industry embraces digital transformation and precision farming technologies to improve productivity and resource efficiency. Artificial intelligence is reshaping modern farming by enabling data-driven decision-making through advanced analytics and intelligent automation. AI-powered platforms process real-time information collected from multiple connected sources, allowing farmers to improve crop monitoring, irrigation management, yield forecasting, livestock supervision, pest identification, disease detection, and soil health management. Increasing pressure to enhance food production while addressing labor shortages, changing climate conditions, and sustainability goals is accelerating the adoption of AI-enabled agricultural solutions worldwide. Growing investments in digital farm management, precision agriculture, controlled-environment farming, and smart agricultural infrastructure are further strengthening market growth. Agricultural businesses are increasingly implementing AI technologies to optimize field operations, improve resource utilization, automate critical farming activities, and support more efficient production planning. AI is also playing a significant role in advancing sustainable farming practices by helping reduce unnecessary resource consumption, improve water efficiency, enhance soil productivity, lower agricultural emissions, and support long-term environmental resilience.

The hardware segment accounted for 45.9% share in 2025 and is expected to grow at a CAGR of 18.1% through 2035. Hardware remains the largest segment because it provides the essential physical infrastructure required to support AI-powered farming operations. Increasing investments in connected agricultural equipment, intelligent sensing technologies, automated machinery, and precision farming systems continue to drive demand for advanced hardware solutions. As commercial farming operations accelerate digital transformation, spending on intelligent field equipment and connected devices continues to exceed investments in software platforms.

The machine learning and deep learning segment held a 32% share in 2025 and is projected to register a CAGR of 19.1% between 2026 and 2035. This segment maintains its leading position because advanced learning algorithms enable accurate analysis of large volumes of agricultural data, supporting better decision-making across farming operations. The ability to continuously improve prediction accuracy, optimize operational planning, and generate intelligent recommendations is encouraging wider adoption of machine learning and deep learning technologies throughout the agriculture sector.

United States AI in Agriculture Market accounted for 86.5% share in 2025, generating USD 1.3 billion. Market expansion is supported by the country's advanced agricultural sector, increasing adoption of precision farming technologies, and growing investments in artificial intelligence, cloud computing, and digital agriculture solutions. Commercial farming operations continue to implement AI-powered technologies to improve operational efficiency, optimize resource management, enhance crop productivity, and strengthen long-term agricultural sustainability. Continued innovation by technology providers and agricultural equipment manufacturers is also contributing to broader commercialization of AI-enabled farming solutions across the country.

Major companies operating in the Global AI in Agriculture Market include Accenture, AGCO, CNH Industrial, DJI Agriculture, John Deere, Kubota, Microsoft, The Climate, Trimble Agriculture, and XAG. Companies operating in the AI in Agriculture Market are strengthening their competitive positions by investing in advanced artificial intelligence technologies, smart farming platforms, and precision agriculture solutions. Many organizations are expanding their product portfolios with intelligent analytics, automation capabilities, connected agricultural equipment, and cloud-based farm management systems that improve productivity and resource efficiency. Strategic partnerships, acquisitions, and collaborations with technology providers, equipment manufacturers, and agricultural organizations continue to accelerate innovation and expand market reach. Companies are also increasing research and development investments to improve data analytics, machine learning capabilities, autonomous farming technologies, and digital integration.

Table of Contents

Chapter 1 Methodology

  • 1.1 Research approach
  • 1.2 Quality Commitments
    • 1.2.1 GMI AI policy & data integrity commitment
      • 1.2.1.1 Source consistency protocol
  • 1.3 Research Trail & Confidence Scoring
    • 1.3.1 Research Trail Components
    • 1.3.2 Scoring Components
  • 1.4 Data Collection
    • 1.4.1 Partial list of primary sources
  • 1.5 Data mining sources
    • 1.5.1 Paid sources
      • 1.5.1.1 Sources, by region
  • 1.6 Base estimates and calculations
    • 1.6.1 Base year calculation
  • 1.7 Forecast Model
    • 1.7.1 Quantified market impact analysis
      • 1.7.1.1 Mathematical impact of growth parameters on forecast
  • 1.8 Research transparency addendum
    • 1.8.1 Source attribution framework
    • 1.8.2 Quality assurance metrics
    • 1.8.3 Our commitment to trust

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis, 2022 – 2035
  • 2.2 Key market trends
    • 2.2.1 Offering
    • 2.2.2 Technology
    • 2.2.3 Application
    • 2.2.4 Deployment Mode
    • 2.2.5 End Use
    • 2.2.6 Regional
  • 2.3 TAM Analysis, 2026-2035
  • 2.4 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
    • 3.1.2 Profit margin analysis
    • 3.1.3 Cost structure
    • 3.1.4 Value addition at each stage
    • 3.1.5 Factor affecting the value chain
    • 3.1.6 Disruptions
  • 3.2 Industry impact forces
    • 3.2.1 Growth drivers
      • 3.2.1.1 Rising adoption of precision agriculture and smart farming
      • 3.2.1.2 Increasing labor shortages driving farm automation
      • 3.2.1.3 Growing availability of agricultural data and IOT connectivity
      • 3.2.1.4 Increasing focus on sustainable agriculture and resource optimization
      • 3.2.1.5 Government support and rising investments in Agtech and ai innovation
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 High initial investment and implementation costs
      • 3.2.2.2 Limited digital infrastructure and rural connectivity
    • 3.2.3 Market opportunities
      • 3.2.3.1 Expansion of generative ai-powered farm advisory platforms
      • 3.2.3.2 Increasing adoption of ai-enabled autonomous agricultural machinery
      • 3.2.3.3 Growing integration of ai with climate-smart and regenerative agriculture
      • 3.2.3.4 Rising demand for ai-based agricultural supply chain traceability
      • 3.2.3.5 Rapid digital transformation across emerging agricultural economies
  • 3.3 Growth potential analysis
  • 3.4 Technology and Innovation landscape
    • 3.4.1 Current technological trends
    • 3.4.2 Emerging technologies
  • 3.5 Pricing Analysis (Driven by Primary Research)
    • 3.5.1 Historical Price Trend Analysis, 2022-2025
    • 3.5.2 Pricing Strategy by Player Type (Premium / Value / Cost-plus)
  • 3.6 Regulatory landscape
    • 3.6.1 North America
      • 3.6.1.1 U.S.: AI Governance, Precision Agriculture Initiatives & USDA Digital Agriculture Programs
      • 3.6.1.2 Canada: Artificial Intelligence Regulation, Smart Farming Policies & Sustainable Agriculture Frameworks
    • 3.6.2 Europe
      • 3.6.2.1 Germany: AI Act Compliance, Digital Farming Strategy & Precision Agriculture Policies
      • 3.6.2.2 UK: AI Regulation, Agri-Tech Strategy & Autonomous Agricultural Machinery Framework
      • 3.6.2.3 France: Digital Agriculture Roadmap & AI Innovation in Sustainable Farming
      • 3.6.2.4 Italy: Smart Agriculture Initiatives, Farm Digitalization & AI Adoption Programs
    • 3.6.3 Asia Pacific
      • 3.6.3.1 India: Digital Agriculture Mission, AI for Agriculture Initiatives & Drone Regulations
      • 3.6.3.2 China: Smart Agriculture Development Plan, AI Innovation Policies & Agricultural Modernization
      • 3.6.3.3 Japan: Society 5.0, Smart Agriculture Promotion & Agricultural Robotics Framework
      • 3.6.3.4 Australia: National AI Strategy, Digital Agriculture Programs & Precision Farming Standards
    • 3.6.4 Latin America
      • 3.6.4.1 Brazil: National IoT Plan, Digital Agriculture Strategy & Precision Farming Programs
      • 3.6.4.2 Mexico: Smart Agriculture Policies, Agricultural Digitalization & AI Innovation Initiatives
      • 3.6.4.3 Argentina: Precision Agriculture Promotion, AgTech Innovation & Digital Farming Frameworks
    • 3.6.5 Middle East & Africa (MEA)
      • 3.6.5.1 Israel: AI Innovation, Precision Agriculture Technologies & Smart Irrigation Policies
      • 3.6.5.2 South Africa: Digital Agriculture Strategy & AI-Enabled Agricultural Modernization
      • 3.6.5.3 Kenya: Digital Agriculture Strategy, AI Advisory Services & Smart Farming Initiatives
      • 3.6.5.4 UAE: National AI Strategy 2031, Smart Agriculture Programs & Food Security Initiatives
      • 3.6.5.5 Saudi Arabia: Vision 2030, Smart Agriculture Transformation & AI-Driven Food Security Program
  • 3.7 Porter’s analysis
  • 3.8 PESTEL analysis
  • 3.9 Patent Landscape (Driven by Primary Research)
  • 3.10 Use cases
    • 3.10.1 Commercialized
    • 3.10.2 Early adoption
    • 3.10.3 Pilot stage
    • 3.10.4 Emerging
  • 3.11 Impact of AI & Generative AI on the Market (Driven by Primary Research)
    • 3.11.1 AI-Driven Disruption of Existing Business Models
    • 3.11.2 GenAI Use Cases & Adoption Roadmap by Segment
    • 3.11.3 Risks, Limitations & Regulatory Considerations
  • 3.12 Infrastructure and deployment landscape, 2022-2025 (driven by primary research)
    • 3.12.1 AI solution deployment penetration by region and buyer segment
    • 3.12.2 Scalability constraints and infrastructure investment trends
  • 3.13 Forecast assumptions & scenario analysis (Driven by Primary Research)
    • 3.13.1 Base Case - Key Macro & Industry Variables Driving CAGR
    • 3.13.2 Optimistic Scenarios - Favourable macro and industry tailwinds
    • 3.13.3 Pessimistic Scenario - Macroeconomic slowdown or industry headwinds

Chapter 4 Competitive Landscape, 2025

  • 4.1 Introduction
  • 4.2 Company market share analysis, 2022-2026
    • 4.2.1 North America
    • 4.2.2 Europe
    • 4.2.3 Asia Pacific
    • 4.2.4 Latin America
    • 4.2.5 MEA
  • 4.3 Competitive analysis of major market players
  • 4.4 Competitive positioning matrix
  • 4.5 Key developments
    • 4.5.1 Mergers & acquisitions
    • 4.5.2 Partnerships & collaborations
    • 4.5.3 New Product Launches
    • 4.5.4 Expansion Plans and funding
  • 4.6 Company Tier Benchmarking
    • 4.6.1 Tier Classification Criteria & Qualifying Thresholds
    • 4.6.2 Tier Positioning Matrix by Revenue, Geography & Innovation

Chapter 5 Market Estimates and Forecast, By Offering, 2022 – 2035 ($ Million)

  • 5.1 Key trends
  • 5.2 Hardware
    • 5.2.1 Sensors and IoT Devices
    • 5.2.2 Drones and Unmanned Aerial Vehicles (UAVs)
    • 5.2.3 Robots and Autonomous Machinery
    • 5.2.4 Imaging, Scanning, and Vision Devices
  • 5.3 Software
    • 5.3.1 AI Platforms and Analytics Tools
    • 5.3.2 Farm Management Information Systems (FMIS)
    • 5.3.3 Supply Chain and Logistics Management Software
    • 5.3.4 Decision Support and Predictive Intelligence Software
  • 5.4 Services
    • 5.4.1 Consulting and System Integration Services
    • 5.4.2 Managed and Subscription-Based AI Services
    • 5.4.3 Training, Support, and After-Sales Services

Chapter 6 Market Estimates and Forecast, By Technology, 2022 – 2035 ($ Million)

  • 6.1 Key trends
  • 6.2 Machine Learning and Deep Learning
  • 6.3 Computer Vision and Image Recognition
  • 6.4 Predictive Analytics and Data Science
  • 6.5 Natural Language Processing (NLP)
  • 6.6 Generative AI and Foundation Models

Chapter 7 Market Estimates and Forecast, By Application, 2022 – 2035 ($ Million)

  • 7.1 Key trends
  • 7.2 Field Production and Crop Management
    • 7.2.1 Precision Farming and Variable Rate Application
    • 7.2.2 Crop Monitoring, Disease and Pest Detection
    • 7.2.3 Yield Prediction and Optimization
    • 7.2.4 Irrigation and Water Management
    • 7.2.5 Soil Health and Fertility Management
  • 7.3 Livestock and Animal Husbandry
    • 7.3.1 Health Monitoring and Disease Detection
    • 7.3.2 Feed Optimization and Nutrition Management
    • 7.3.3 Reproductive Cycle Management
  • 7.4 Harvest and Post-Harvest Management
    • 7.4.1 Harvest Timing Optimization
    • 7.4.2 Post-Harvest Quality Inspection and Grading
    • 7.4.3 Food Loss and Waste Detection and Reduction
  • 7.5 Storage and Cold Chain Management
    • 7.5.1 Intelligent Warehouse and Storage Monitoring
    • 7.5.2 Cold Chain Condition and Temperature Management
    • 7.5.3 Inventory Optimization and Shelf-Life Prediction
  • 7.6 Supply Chain, Logistics and Distribution
    • 7.6.1 Route Optimization and Fleet Management
    • 7.6.2 Demand Forecasting and Market Intelligence
    • 7.6.3 Agri-Supply Chain Traceability and Transparency
    • 7.6.4 Last-Mile Distribution Optimization
  • 7.7 Farm Management and Decision Support
    • 7.7.1 Farm Management Information Systems (FMIS)
    • 7.7.2 Agricultural Risk Assessment and Crop Insurance
    • 7.7.3 Market Price Intelligence and Trading Support

Chapter 8 Market Estimates and Forecast, By Deployment Mode, 2022 – 2035 ($ Million)

  • 8.1 Key trends
  • 8.2 Cloud-Based
  • 8.3 On-Premise
  • 8.4 Hybrid and Edge

Chapter 9 Market Estimates and Forecast, By End Use, 2022 – 2035 ($ Million)

  • 9.1 Key trends
  • 9.2 Large Agricultural Enterprises and Agribusinesses
  • 9.3 Small and Medium Farms and Cooperatives
  • 9.4 Food Processors and Packers
  • 9.5 Logistics and Distribution Operators
  • 9.6 Retailers and Foodservice Companies
  • 9.7 Government and Research Institutions
  • 9.8 Others

Chapter 10 Market Estimates & Forecast, By Region, 2022 - 2035 ($ Million)

  • 10.1 Key trends
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 France
    • 10.3.4 Italy
    • 10.3.5 Spain
    • 10.3.6 Russia
    • 10.3.7 Netherlands
    • 10.3.8 Belgium
  • 10.4 Asia Pacific
    • 10.4.1 China
    • 10.4.2 India
    • 10.4.3 Japan
    • 10.4.4 Australia
    • 10.4.5 South Korea
    • 10.4.6 Singapore
    • 10.4.7 Thailand
    • 10.4.8 Indonesia
  • 10.5 Latin America
    • 10.5.1 Brazil
    • 10.5.2 Mexico
    • 10.5.3 Argentina
  • 10.6 MEA
    • 10.6.1 South Africa
    • 10.6.2 Saudi Arabia
    • 10.6.3 UAE

Chapter 11 Company Profiles

  • 11.1 Global Players
    • 11.1.1 Accenture
    • 11.1.2 AGCO
    • 11.1.3 Carbon Robotics
    • 11.1.4 CNH Industrial
    • 11.1.5 DJI Agriculture
    • 11.1.6 John Deere
    • 11.1.7 Kubota
    • 11.1.8 Microsoft
    • 11.1.9 The Climate
    • 11.1.10 Trimble Agriculture
  • 11.2 Regional Players
    • 11.2.1 Ag Leader Technology
    • 11.2.2 CLAAS
    • 11.2.3 CropIn Technology
    • 11.2.4 Naio Technologies
    • 11.2.5 SDF (SAME Deutz-Fahr)
    • 11.2.6 Semios
    • 11.2.7 XAG
  • 11.3 Emerging Players
    • 11.3.1 Ceres AI
    • 11.3.2 CropX Technologies
    • 11.3.3 Taranis