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市場調查報告書
商品編碼
2021760

2034年農業人工智慧市場預測：按組件、技術、應用、最終用戶和地區分類的全球分析

AI in Agriculture Market Forecasts to 2034 - Global Analysis By Component (Software Platforms and Services), Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球農業人工智慧市場規模將達到 58 億美元，並在預測期內以 23.0% 的複合年成長率成長，到 2034 年將達到 300 億美元。

人工智慧在農業領域的應用，透過機器學習、數據分析和智慧演算法，正在改變農場管理。這使得農民能夠預測天氣、監測土壤和作物狀況、檢測病蟲害並最佳化資源利用。無人機、感測器和自主設備等技術為精密農業提供支持，最大限度地減少勞動力投入，並促進永續的耕作方式。透過整合人工智慧，農業可以提高生產力、提升作物品質、減少環境影響並支持明智的決策，有助於加強全球糧食安全。

對糧食安全和永續農業實踐的需求日益成長

人工智慧技術能夠實現精密農業，最佳化水、肥料和農藥的使用，在最大限度提高作物產量的同時，減少對環境的影響。土壤健康即時監測和預測分析使農民能夠做出積極主動的決策，預防作物歉收，提高食品供應鏈的可靠性。政府推動智慧農業的舉措以及數據驅動型耕作方式的日益普及，進一步加速了人工智慧的整合應用。隨著耕地面積的減少和氣候模式的日益難以預測，人工智慧為永續集約化農業提供了擴充性的解決方案，使其成為現代農業不可或缺的工具和市場驅動力。

前期投資高，且資料互通性。

在農業領域實施人工智慧解決方案需要大量的初期投資，包括無人機、物聯網感測器和自主農業機械等硬體，以及軟體訂閱和雲端基礎設施。對於缺乏補貼和其他支持的開發中地區的小規模和微型農戶而言，這些成本尤其沉重。此外，農業資料通常來自衛星、氣象站和農業機械等多種來源，且資料格式和協定不相容。缺乏標準化的數據互通性阻礙了無縫整合，降低了人工智慧模型的有效性。培訓當地農民如何使用數位工具也需要耗費大量時間和資源。儘管人工智慧具有明顯的長期效益，但這些資金和技術障礙減緩了其普及速度，抑制了市場成長。

人工智慧驅動的機器人農業和自主設備的發展

自動曳引機、機器人收割機和人工智慧驅動的除草機的快速發展為農業人工智慧市場帶來了巨大的機會。這些系統可以解決人手不足，降低營運成本，並以比人類更高的精度完成重複性工作。新的應用包括機器人水果採摘、自動疏果和利用電腦視覺進行選擇性噴灑。此外，5G通訊在農村地區的普及將實現即時數據傳輸和遠端設備控制。隨著農業相關企業尋求減少對季節性工人的依賴並提高營運的穩定性，對全自動農業解決方案的需求將會增加。投資於穩健、低功耗人工智慧機器人技術的製造商有望獲得顯著的市場佔有率。

資料隱私外洩和演算法偏見的脆弱性。

資料外洩可能導致專有農業技術洩露，並使大型農業企業得以市場運作。此外，基於不一致資料集訓練的人工智慧模型可能會產生偏差的建議，這些建議在特定的土壤類型、作物品種或氣候條件下可能無法正常運作，從而導致次優結果和經濟損失。過度依賴未經實地檢驗的黑箱演算法也可能導致在罕見天氣事件期間做出錯誤決策。如果沒有強力的網路安全措施和透明且經過偏差檢驗的模型，這些漏洞可能會削弱農民的信任，並阻礙人工智慧的普及應用，尤其是在小規模農戶群體中。

新冠疫情的影響：

新冠疫情初期擾亂了農業供應鏈，限制了農場獲得技術支援服務的管道，並延緩了人工智慧技術的應用。然而，封鎖期間的勞動力短缺增加了人們對自動化收割和機器人解決方案的興趣，從而推動了對人工智慧設備的需求。多個國家的政府經濟刺激措施包括為數位農業計畫提供資金，支持了市場復甦。此外，出行限制阻礙了現場勘察，促使利用雲端人工智慧平台進行遠端農場管理的普及。儘管硬體供應鏈有所延誤，但軟體和分析領域卻穩定成長。在後疫情時代，人們對糧食安全的擔憂日益加劇，公共和私營部門對具有韌性和技術主導的農業系統的投資增加，為農業人工智慧市場提供了長期的利好因素。

在預測期內，軟體平台產業預計將佔據最大的市場佔有率。

預計在預測期內，軟體平台領域將佔據最大的市場佔有率。該領域涵蓋人工智慧模型和演算法、數據管理和分析工具、整合應用程式介面（API）以及視覺化儀表板，這些都是任何智慧農業運營的核心。所有農業應用對數據處理、預測建模和即時監控的迫切需求推動了這一領域的領先地位。此外，基於雲端的機器學習和邊緣人工智慧的持續進步也增加了對軟體的需求。

預計在預測期內，機器人和自動化領域將呈現最高的複合年成長率。

在預測期內，機器人和自動化技術領域預計將呈現最高的成長率。自主除草機器人、收割機器人和無人機噴灑系統能夠消除重複性的人工勞動，並提高作業精度。這在面臨嚴重農業勞動力短缺的地區尤其重要。低功耗人工智慧晶片、電腦視覺演算法和輕型致動器的發展正在提高機器人的可靠性和經濟性。對於人事費用和永續性壓力最為嚴峻的大型農業企業和溫室經營者而言，機器人技術也極具吸引力，因為它能夠實現全天候農業作業，並透過精準噴灑減少化學品的使用。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率。這主要得益於該地區擁有許多大型農業相關企業、約翰迪爾和IBM等技術供應商，以及精密農業工具的早期應用。該地區高度一體化的農場以及企業對農業研發的大量投入，都為人工智慧在大規模種植和畜牧業中的應用提供了支持。此外，由無人機服務供應商、衛星影像公司和農場管理軟體供應商組成的成熟生態系統，也促進了美國和加拿大的高採用率。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、印度和東南亞國家人口的快速成長、耕地面積的減少以及政府現代化項目的推進。印度和越南等國的數位化農業舉措和農業技術Start-Ups生態系統的建立，正在推動對價格合理的AI解決方案的需求。各國政府正大力投資作物產量預測模型和病蟲害預警系統。隨著小規模農戶尋求提高生產力，經濟高效的行動端AI工具正使亞太地區成為農業AI市場成長最快的地區。

免費客製化服務：

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  • 企業概況
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  • 區域分類
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
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    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球農業人工智慧市場：按組件分類

  • 軟體平台
    • 人工智慧模型和演算法
    • 數據管理與分析
    • 整合和 API
    • 可視化和監控儀錶板
  • 服務
    • 諮詢與策略
    • 整合與部署
    • 培訓支援
    • 託管服務

第6章：全球農業人工智慧市場：依技術分類

  • 機器學習（ML）
  • 深度學習
  • 電腦視覺
  • 自然語言處理（NLP）
  • 預測分析
  • 機器人與自動化
  • 其他技術

第7章：全球農業人工智慧市場：按應用領域分類

  • 精密農業
    • 土壤和作物健康監測
    • 收益率預測
    • 浮動利率應用（VRA）
  • 牲畜監測與管理
  • 無人機和衛星影像分析
  • 自動化灌溉和水資源管理
  • 病蟲害檢測
  • 供應鍊和物流最佳化
  • 天氣預報與風險管理
  • 其他用途

第8章：全球農業人工智慧市場：按最終用戶分類

  • 農民和農業合作社
  • 農業研究機構
  • 政府和監管機構
  • 農產品和大型農場
  • 溫室/室內農業營運商
  • 其他最終用戶

第9章：全球農業人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第10章 戰略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第11章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第12章：公司簡介

  • John Deere
  • Bayer Crop Science（Climate LLC）
  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • AWhere Inc.
  • Taranis
  • Prospera Technologies
  • Granular
  • The Climate Corporation
  • Descartes Labs
  • AgEagle Aerial Systems
  • Resson
  • VineView
  • ec2ce
Product Code: SMRC35025

According to Stratistics MRC, the Global AI in Agriculture Market is accounted for $5.8 billion in 2026 and is expected to reach $30.0 billion by 2034 growing at a CAGR of 23.0% during the forecast period. AI in agriculture applies machine learning, data analytics, and smart algorithms to transform farming operations. It helps farmers forecast weather, monitor soil and crop conditions, detect pests or diseases, and optimize resource usage. Technologies such as drones, sensors, and autonomous equipment support precision agriculture, minimize labor, and encourage sustainable practices. By integrating AI, agriculture can improve productivity, enhance crop quality, reduce environmental effects, and enable informed decisions to strengthen food security globally.

Market Dynamics:

Driver:

Rising need for food security and sustainable farming practices

AI technologies enable precision farming techniques that optimize water, fertilizer, and pesticide usage, reducing environmental impact while maximizing crop yields. Real-time soil health monitoring and predictive analytics help farmers make proactive decisions, preventing crop failures and improving food supply chain reliability. Government initiatives promoting smart agriculture and the increasing adoption of data-driven farming methods further accelerate AI integration. As arable land diminishes and weather patterns become erratic, AI provides scalable solutions for sustainable intensification, making it an indispensable tool for modern agriculture and a major market driver.

Restraint:

High initial investment and data interoperability challenges

Implementing AI solutions in agriculture requires substantial upfront capital for hardware such as drones, IoT sensors, and autonomous machinery, along with software subscriptions and cloud infrastructure. Small and marginal farmers, particularly in developing regions, find these costs prohibitive without subsidy support. Additionally, agricultural data often comes from disparate sources-satellites, weather stations, farm equipment-using incompatible formats and protocols. Lack of standardized data interoperability limits seamless integration and reduces the effectiveness of AI models. Training local farmers to use digital tools also demands time and resources. These financial and technical barriers slow down widespread adoption, restraining market growth despite clear long-term benefits.

Opportunity:

Expansion of AI-powered robotic farming and autonomous equipment

The rapid development of autonomous tractors, robotic harvesters, and AI-driven weeding machines presents a significant opportunity for the AI in agriculture market. These systems address labor shortages, reduce operational costs, and perform repetitive tasks with higher precision than human workers. Emerging applications include robotic fruit picking, automated thinning, and selective spraying using computer vision. Furthermore, the integration of 5G connectivity in rural areas enables real-time data transmission and remote equipment control. As agribusinesses seek to reduce dependency on seasonal labor and improve operational consistency, demand for fully autonomous farming solutions will grow. Manufacturers investing in ruggedized, low-power AI robotics stand to capture substantial market share.

Threat:

Vulnerability to data privacy breaches and algorithmic bias

A data breach could expose proprietary farming techniques or enable market manipulation by large agribusinesses. Additionally, AI models trained on non-diverse datasets may produce biased recommendations that fail for certain soil types, crop varieties, or climatic conditions, leading to suboptimal outcomes or financial losses. Over-reliance on black-box algorithms without local validation can also result in poor decision-making during rare weather events. Without robust cybersecurity frameworks and transparent, bias-tested models, these vulnerabilities threaten farmer trust and limit AI adoption, especially among smallholders.

Covid-19 Impact:

The COVID-19 pandemic initially disrupted agricultural supply chains and reduced access to on-farm technical support services, slowing new AI deployments. Labor shortages during lockdowns, however, accelerated interest in automated harvesting and robotic solutions, driving demand for AI-powered equipment. Government stimulus packages in several countries included funding for digital agriculture projects, supporting market recovery. Additionally, remote farm management using cloud-based AI dashboards gained traction as movement restrictions limited physical inspections. While hardware supply chains faced delays, software and analytics segments grew steadily. As food security concerns intensified post-pandemic, both public and private sectors increased investments in resilient, technology-driven farming systems, giving the AI in agriculture market a long-term growth tailwind.

The software platforms segment is expected to be the largest during the forecast period

The software platforms segment is expected to account for the largest market share during the forecast period. This segment includes AI models & algorithms, data management & analytics tools, integration APIs, and visualization dashboards that form the core of any smart farming operation. The essential need for data processing, predictive modeling, and real-time monitoring across all agricultural applications drives this dominance. Additionally, ongoing advancements in cloud-based machine learning and edge AI increase software demand.

The robotics & automation segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the robotics & automation technology segment is predicted to witness the highest growth rate. Autonomous weeding robots, robotic harvesters, and drone-based spraying systems eliminate repetitive manual labor and improve operational precision, particularly valuable in regions facing severe farm labor shortages. The development of low-power AI chips, computer vision algorithms, and lightweight actuators enhances robot reliability and affordability. Robotics also enables 24/7 farm operations and reduces chemical usage through targeted application, appealing to large-scale agribusinesses and greenhouse operators where labor costs and sustainability pressures are most critical.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by the presence of major agribusiness firms, technology providers such as John Deere and IBM, and early adoption of precision farming tools. The region's high farm consolidation and substantial corporate investment in agricultural R&D support AI integration into large-scale crop and livestock operations. Additionally, a mature ecosystem of drone service providers, satellite imaging companies, and farm management software vendors contributes to high adoption rates across the United States and Canada.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapidly growing population, shrinking arable land, and increasing government modernization programs in China, India, and Southeast Asian nations. The establishment of digital agriculture initiatives and AgriTech startup ecosystems in countries like India and Vietnam drives demand for affordable AI solutions. Governments are investing heavily in crop yield prediction models and pest alert systems. As smallholder farms seek productivity improvements, cost-effective mobile-based AI tools position APAC as the fastest-growing AI in agriculture market.

Key players in the market

Some of the key players in AI in Agriculture Market include John Deere, Bayer Crop Science (Climate LLC), IBM Corporation, Microsoft Corporation, Google LLC, AWhere Inc., Taranis, Prospera Technologies, Granular, The Climate Corporation, Descartes Labs, AgEagle Aerial Systems, Resson, VineView, and ec2ce.

Key Developments:

In March 2026, John Deere announced the acquisition of a computer vision startup to enhance its See & Spray(TM) technology, enabling real-time weed detection and targeted herbicide application across large row crops. The integration reduces chemical usage by up to 77% while improving crop safety.

In February 2026, Microsoft launched new Azure Data Manager for Agriculture features, including enhanced satellite imagery analytics and soil moisture prediction models, allowing agribusinesses to build custom digital twins of farm operations with seamless IoT sensor integration.

Components Covered:

  • Software Platforms
  • Services

Technologies Covered:

  • Machine Learning (ML)
  • Deep Learning
  • Computer Vision
  • Natural Language Processing (NLP)
  • Predictive Analytics
  • Robotics & Automation
  • Other Technologies

Applications Covered:

  • Precision Farming
  • Livestock Monitoring & Management
  • Drone & Satellite Imagery Analytics
  • Automated Irrigation & Water Management
  • Pest & Disease Detection
  • Supply Chain & Logistics Optimization
  • Weather Forecasting & Risk Management
  • Other Applications

End Users Covered:

  • Farmers & Agricultural Cooperatives
  • Agricultural Research Organizations
  • Government & Regulatory Bodies
  • Agribusiness & Large Farms
  • Greenhouse & Indoor Farming Operators
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Agriculture Market, By Component

  • 5.1 Software Platforms
    • 5.1.1 AI Models & Algorithms
    • 5.1.2 Data Management & Analytics
    • 5.1.3 Integration & APIs
    • 5.1.4 Visualization & Monitoring Dashboards
  • 5.2 Services
    • 5.2.1 Consulting & Strategy
    • 5.2.2 Integration & Deployment
    • 5.2.3 Training & Support
    • 5.2.4 Managed Services

6 Global AI in Agriculture Market, By Technology

  • 6.1 Machine Learning (ML)
  • 6.2 Deep Learning
  • 6.3 Computer Vision
  • 6.4 Natural Language Processing (NLP)
  • 6.5 Predictive Analytics
  • 6.6 Robotics & Automation
  • 6.7 Other Technologies

7 Global AI in Agriculture Market, By Application

  • 7.1 Precision Farming
    • 7.1.1 Soil & Crop Health Monitoring
    • 7.1.2 Yield Prediction
    • 7.1.3 Variable Rate Application (VRA)
  • 7.2 Livestock Monitoring & Management
  • 7.3 Drone & Satellite Imagery Analytics
  • 7.4 Automated Irrigation & Water Management
  • 7.5 Pest & Disease Detection
  • 7.6 Supply Chain & Logistics Optimization
  • 7.7 Weather Forecasting & Risk Management
  • 7.8 Other Applications

8 Global AI in Agriculture Market, By End User

  • 8.1 Farmers & Agricultural Cooperatives
  • 8.2 Agricultural Research Organizations
  • 8.3 Government & Regulatory Bodies
  • 8.4 Agribusiness & Large Farms
  • 8.5 Greenhouse & Indoor Farming Operators
  • 8.6 Other End Users

9 Global AI in Agriculture Market, By Geography

  • 9.1 North America
    • 9.1.1 United States
    • 9.1.2 Canada
    • 9.1.3 Mexico
  • 9.2 Europe
    • 9.2.1 United Kingdom
    • 9.2.2 Germany
    • 9.2.3 France
    • 9.2.4 Italy
    • 9.2.5 Spain
    • 9.2.6 Netherlands
    • 9.2.7 Belgium
    • 9.2.8 Sweden
    • 9.2.9 Switzerland
    • 9.2.10 Poland
    • 9.2.11 Rest of Europe
  • 9.3 Asia Pacific
    • 9.3.1 China
    • 9.3.2 Japan
    • 9.3.3 India
    • 9.3.4 South Korea
    • 9.3.5 Australia
    • 9.3.6 Indonesia
    • 9.3.7 Thailand
    • 9.3.8 Malaysia
    • 9.3.9 Singapore
    • 9.3.10 Vietnam
    • 9.3.11 Rest of Asia Pacific
  • 9.4 South America
    • 9.4.1 Brazil
    • 9.4.2 Argentina
    • 9.4.3 Colombia
    • 9.4.4 Chile
    • 9.4.5 Peru
    • 9.4.6 Rest of South America
  • 9.5 Rest of the World (RoW)
    • 9.5.1 Middle East
      • 9.5.1.1 Saudi Arabia
      • 9.5.1.2 United Arab Emirates
      • 9.5.1.3 Qatar
      • 9.5.1.4 Israel
      • 9.5.1.5 Rest of Middle East
    • 9.5.2 Africa
      • 9.5.2.1 South Africa
      • 9.5.2.2 Egypt
      • 9.5.2.3 Morocco
      • 9.5.2.4 Rest of Africa

10 Strategic Market Intelligence

  • 10.1 Industry Value Network and Supply Chain Assessment
  • 10.2 White-Space and Opportunity Mapping
  • 10.3 Product Evolution and Market Life Cycle Analysis
  • 10.4 Channel, Distributor, and Go-to-Market Assessment

11 Industry Developments and Strategic Initiatives

  • 11.1 Mergers and Acquisitions
  • 11.2 Partnerships, Alliances, and Joint Ventures
  • 11.3 New Product Launches and Certifications
  • 11.4 Capacity Expansion and Investments
  • 11.5 Other Strategic Initiatives

12 Company Profiles

  • 12.1 John Deere
  • 12.2 Bayer Crop Science (Climate LLC)
  • 12.3 IBM Corporation
  • 12.4 Microsoft Corporation
  • 12.5 Google LLC
  • 12.6 AWhere Inc.
  • 12.7 Taranis
  • 12.8 Prospera Technologies
  • 12.9 Granular
  • 12.10 The Climate Corporation
  • 12.11 Descartes Labs
  • 12.12 AgEagle Aerial Systems
  • 12.13 Resson
  • 12.14 VineView
  • 12.15 ec2ce

List of Tables

  • Table 1 Global AI in Agriculture Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Agriculture Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Agriculture Market Outlook, By Software Platforms (2023-2034) ($MN)
  • Table 4 Global AI in Agriculture Market Outlook, By AI Models & Algorithms (2023-2034) ($MN)
  • Table 5 Global AI in Agriculture Market Outlook, By Data Management & Analytics (2023-2034) ($MN)
  • Table 6 Global AI in Agriculture Market Outlook, By Integration & APIs (2023-2034) ($MN)
  • Table 7 Global AI in Agriculture Market Outlook, By Visualization & Monitoring Dashboards (2023-2034) ($MN)
  • Table 8 Global AI in Agriculture Market Outlook, By Services (2023-2034) ($MN)
  • Table 9 Global AI in Agriculture Market Outlook, By Consulting & Strategy (2023-2034) ($MN)
  • Table 10 Global AI in Agriculture Market Outlook, By Integration & Deployment (2023-2034) ($MN)
  • Table 11 Global AI in Agriculture Market Outlook, By Training & Support (2023-2034) ($MN)
  • Table 12 Global AI in Agriculture Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 13 Global AI in Agriculture Market Outlook, By Technology (2023-2034) ($MN)
  • Table 14 Global AI in Agriculture Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 15 Global AI in Agriculture Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 16 Global AI in Agriculture Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 17 Global AI in Agriculture Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 18 Global AI in Agriculture Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 19 Global AI in Agriculture Market Outlook, By Robotics & Automation (2023-2034) ($MN)
  • Table 20 Global AI in Agriculture Market Outlook, By Other Technologies (2023-2034) ($MN)
  • Table 21 Global AI in Agriculture Market Outlook, By Application (2023-2034) ($MN)
  • Table 22 Global AI in Agriculture Market Outlook, By Precision Farming (2023-2034) ($MN)
  • Table 23 Global AI in Agriculture Market Outlook, By Soil & Crop Health Monitoring (2023-2034) ($MN)
  • Table 24 Global AI in Agriculture Market Outlook, By Yield Prediction (2023-2034) ($MN)
  • Table 25 Global AI in Agriculture Market Outlook, By Variable Rate Application (VRA) (2023-2034) ($MN)
  • Table 26 Global AI in Agriculture Market Outlook, By Livestock Monitoring & Management (2023-2034) ($MN)
  • Table 27 Global AI in Agriculture Market Outlook, By Drone & Satellite Imagery Analytics (2023-2034) ($MN)
  • Table 28 Global AI in Agriculture Market Outlook, By Automated Irrigation & Water Management (2023-2034) ($MN)
  • Table 29 Global AI in Agriculture Market Outlook, By Pest & Disease Detection (2023-2034) ($MN)
  • Table 30 Global AI in Agriculture Market Outlook, By Supply Chain & Logistics Optimization (2023-2034) ($MN)
  • Table 31 Global AI in Agriculture Market Outlook, By Weather Forecasting & Risk Management (2023-2034) ($MN)
  • Table 32 Global AI in Agriculture Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 33 Global AI in Agriculture Market Outlook, By End User (2023-2034) ($MN)
  • Table 34 Global AI in Agriculture Market Outlook, By Farmers & Agricultural Cooperatives (2023-2034) ($MN)
  • Table 35 Global AI in Agriculture Market Outlook, By Agricultural Research Organizations (2023-2034) ($MN)
  • Table 36 Global AI in Agriculture Market Outlook, By Government & Regulatory Bodies (2023-2034) ($MN)
  • Table 37 Global AI in Agriculture Market Outlook, By Agribusiness & Large Farms (2023-2034) ($MN)
  • Table 38 Global AI in Agriculture Market Outlook, By Greenhouse & Indoor Farming Operators (2023-2034) ($MN)
  • Table 39 Global AI in Agriculture Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.