查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
2021760

2034年农业人工智慧市场预测：按组件、技术、应用、最终用户和地区分類的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2026 年，全球农业人工智慧市场规模将达到 58 亿美元，并在预测期内以 23.0% 的复合年增长率增长，到 2034 年将达到 300 亿美元。

人工智慧在农业领域的应用，透过机器学习、数据分析和智慧演算法，正在改变农场管理。这使得农民能够预测天气、监测土壤和作物状况、检测病虫害并优化资源利用。无人机、感测器和自主设备等技术为精密农业提供支持，最大限度地减少劳动力投入，并促进永续的耕作方式。透过整合人工智慧，农业可以提高生产力、提升作物品质、减少环境影响并支持明智的决策，有助于加强全球粮食安全。

对粮食安全和永续农业实践的需求日益增长

人工智慧技术能够实现精密农业，优化水、肥料和农药的使用，在最大限度提高作物产量的同时，减少对环境的影响。土壤健康即时监测和预测分析使农民能够做出积极主动的决策，预防作物歉收，提高食品供应链的可靠性。政府推动智慧农业的倡议以及数据驱动型耕作方式的日益普及，进一步加速了人工智慧的整合应用。随着耕地面积的减少和气候模式的日益难以预测，人工智慧为永续集约化农业提供了扩充性的解决方案，使其成为现代农业不可或缺的工具和市场驱动力。

前期投资高，且资料互通性。

在农业领域实施人工智慧解决方案需要大量的初期投资，包括无人机、物联网感测器和自主农业机械等硬件，以及软体订阅和云端基础设施。对于缺乏补贴和其他支持的开发中地区的小规模和微型农户而言，这些成本尤其沉重。此外，农业资料通常来自卫星、气象站和农业机械等多种来源，且资料格式和协定不相容。缺乏标准化的数据互通性阻碍了无缝集成，降低了人工智慧模型的有效性。培训当地农民如何使用数位工具也需要耗费大量时间和资源。儘管人工智慧具有明显的长期效益，但这些资金和技术障碍减缓了其普及速度，抑制了市场成长。

人工智慧驱动的机器人农业和自主设备的发展

自动拖拉机、机器人收割机和人工智慧驱动的除草机的快速发展为农业人工智慧市场带来了巨大的机会。这些系统可以解决人手不足，降低营运成本，并以比人类更高的精度完成重复性工作。新的应用包括机器人水果采摘、自动疏果和利用电脑视觉进行选择性喷洒。此外，5G通讯在农村地区的普及将实现即时数据传输和远端设备控制。随着农业相关企业寻求减少对季节性工人的依赖并提高营运的稳定性，对全自动农业解决方案的需求将会增加。投资于稳健、低功耗人工智慧机器人技术的製造商有望获得显着的市场份额。

资料隐私外洩和演算法偏见的脆弱性。

资料外洩可能导致专有农业技术洩露，并使大型农业企业得以市场运作。此外，基于不一致资料集训练的人工智慧模型可能会产生偏差的建议，这些建议在特定的土壤类型、作物品种或气候条件下可能无法正常运作，从而导致次优结果和经济损失。过度依赖未经实地检验的黑箱演算法也可能导致在罕见天气事件期间做出错误决策。如果没有强有力的网路安全措施和透明且经过偏差检验的模型，这些漏洞可能会削弱农民的信任，并阻碍人工智慧的普及应用，尤其是在小规模农户群体中。

新冠疫情的影响：

新冠疫情初期扰乱了农业供应链，限制了农场获得技术支援服务的管道，并延缓了人工智慧技术的应用。然而，封锁期间的劳动力短缺增加了人们对自动化收割和机器人解决方案的兴趣，从而推动了对人工智慧设备的需求。多个国家的政府经济刺激措施包括为数位农业计画提供资金，支持了市场復苏。此外，出行限制阻碍了现场勘察，促使利用云端人工智慧平台进行远端农场管理的普及。儘管硬体供应链有所延误，但软体和分析领域却稳定成长。在后疫情时代，人们对粮食安全的担忧日益加剧，公共和私营部门对具有韧性和技术主导的农业系统的投资增加，为农业人工智慧市场提供了长期的利好因素。

在预测期内，软体平台产业预计将占据最大的市场份额。

预计在预测期内，软体平台领域将占据最大的市场份额。该领域涵盖人工智慧模型和演算法、数据管理和分析工具、整合应用程式介面（API）以及视觉化仪錶盘，这些都是任何智慧农业运营的核心。所有农业应用对数据处理、预测建模和即时监控的迫切需求推动了这一领域的领先地位。此外，基于云端的机器学习和边缘人工智慧的持续进步也增加了对软体的需求。

预计在预测期内，机器人和自动化领域将呈现最高的复合年增长率。

在预测期内，机器人和自动化技术领域预计将呈现最高的成长率。自主除草机器人、收割机器人和无人机喷洒系统能够消除重复性的人工劳动，并提高作业精度。这在面临严重农业劳动力短缺的地区尤其重要。低功耗人工智慧晶片、电脑视觉演算法和轻型致动器的发展正在提高机器人的可靠性和经济性。对于人事费用和永续性压力最为严峻的大型农业企业和温室经营者而言，机器人技术也极具吸引力，因为它能够实现全天候农业作业，并透过精准喷洒减少化学品的使用。

市占率最大的地区：

在预测期内，北美预计将占据最大的市场份额。这主要得益于该地区拥有许多大型农业相关企业、约翰迪尔和IBM等技术供应商，以及精密农业工具的早期应用。该地区高度一体化的农场以及企业对农业研发的大量投入，都为人工智慧在大规模种植和畜牧业中的应用提供了支持。此外，由无人机服务供应商、卫星影像公司和农场管理软体供应商组成的成熟生态系统，也促进了美国和加拿大的高采用率。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于中国、印度和东南亚国家人口的快速增长、耕地面积的减少以及政府现代化项目的推进。印度和越南等国的数位化农业倡议和农业技术Start-Ups生态系统的建立，正在推动对价格合理的AI解决方案的需求。各国政府正大力投资作物产量预测模型和病虫害预警系统。随着小规模农户寻求提高生产力，经济高效的行动端AI工具正使亚太地区成为农业AI市场成长最快的地区。

免费客製化服务：

所有购买此报告的客户均可享受以下免费自订选项之一：

  • 企业概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域划分
    • 应客户要求，我们提供主要国家和地区的市场估算和预测，以及复合年增长率（註：需进行可行性检查）。
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章执行摘要

  • 市场概览及主要亮点
  • 驱动因素、挑战与机会
  • 竞争格局概述
  • 战略洞察与建议

第二章：研究框架

  • 研究目标和范围
  • 相关人员分析
  • 研究假设和限制
  • 调查方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 技术与创新展望
  • 新兴市场/高成长市场
  • 监管和政策环境
  • 新冠疫情的影响及復苏前景

第四章：竞争环境与策略评估

  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 替代品的威胁
    • 新进入者的威胁
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章：全球农业人工智慧市场：按组件划分

  • 软体平台
    • 人工智慧模型和演算法
    • 数据管理与分析
    • 整合和 API
    • 可视化和监控仪錶板
  • 服务
    • 咨询与策略
    • 整合与部署
    • 培训支援
    • 託管服务

第六章：全球农业人工智慧市场：依技术划分

  • 机器学习（ML）
  • 深度学习
  • 电脑视觉
  • 自然语言处理（NLP）
  • 预测分析
  • 机器人与自动化
  • 其他技术

第七章：全球农业人工智慧市场：按应用领域划分

  • 精密农业
    • 土壤和作物健康监测
    • 收益率预测
    • 浮动利率应用（VRA）
  • 牲畜监测与管理
  • 无人机和卫星影像分析
  • 自动化灌溉和水资源管理
  • 病虫害检测
  • 供应炼和物流优化
  • 天气预报与风险管理
  • 其他用途

第八章：全球农业人工智慧市场：按最终用户划分

  • 农民和农业合作社
  • 农业研究机构
  • 政府和监管机构
  • 农产品和大型农场
  • 温室/室内农业营运商
  • 其他最终用户

第九章：全球农业人工智慧市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十章 战略市场资讯

  • 工业价值网络和供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十一章 产业趋势与策略倡议

  • 併购
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十二章：公司简介

  • 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.