农业中的人工智慧市场规模 - 按组成部分、应用、技术和预测，2024 年 - 2032 年

农业中的人工智慧市场规模预计在 2024 年至 2032 年间将以超过 24% 的复合年增长率增长。精准农业、预测分析和自动化机械等人工智慧技术有助于优化资源利用和农作物产量。机器学习和资料分析的重大进步也使得更好的决策和解决问题成为可能。

不断增加的投资和支持性政府政策正在吸引创新并鼓励人工智慧在农业中的应用。例如，2024 年 4 月，农业科技公司 Cropin Technology 推出了“Aksara”，这是一种专为气候智慧型农业设计的生成式人工智慧系统。此外，为了解决环境问题，对永续农业实践的需求不断增长，这推动了人工智慧解决方案的整合，以有效监测和管理农作物、土壤和天气状况。

整个产业分为组件、技术、应用和区域。

从组成部分来看，由于对资料分析、农场管理软体和预测建模服务的需求不断增长，服务领域的农业市场人工智慧预计将在 2024 年至 2032 年间显着增长。增强的支援和维护以及培训和咨询正在推动这些服务的采用。对即时洞察和精准农业解决方案以优化生产力和永续性的强烈需求正在鼓励农民利用人工智慧驱动的服务。

在应用方面，由于对土壤健康和作物状况的精确资料的需求，预计到 2032 年，农业产业中作物和土壤监测领域的人工智慧将以复合年增长率大幅增长。人工智慧可以实现即时监控、预测分析和高效的资源管理，从而提高作物产量和永续性。人们对永续农业实践的认识不断增强，加上物联网感测器和无人机的整合以进行详细的现场分析，也推动了该领域的成长。

从地区来看，在技术的快速采用、对农业技术新创公司的投资增加以及政府推出支持智慧农业的倡议的推动下，亚太地区农业人工智慧市场将在 2024 年至 2032 年间出现显着增长。该地区的大型农业部门受益于人工智慧提高的生产力和效率。人们对永续实践的认识不断增强，以及应对粮食安全挑战的需求不断增加，也加速了人工智慧在亚太地区农业领域的部署。

目录

第 1 章：方法与范围

第 2 章：执行摘要

第 3 章：产业洞察

• 产业生态系统分析
• 供应商格局
  • 元件供应商
  • 技术提供者
  • 解决方案提供者
  • 系统整合商
  • 农业装备供应商
  • 终端用户
• 利润率分析
• 技术与创新格局
• 专利分析
• 重要新闻和倡议
• 监管环境
• 衝击力
  • 成长动力
    • 美国农作物产量损失增加
    • 越来越多采用精准农业
    • 专注于作物营养的农业技术解决方案激增
    • 增加对人工智慧新创公司和农业科技公司的投资
  • 产业陷阱与挑战
    • 实施人工智慧技术的初始成本较高
    • 缺乏基础设施和连结性
• 成长潜力分析
• 波特的分析
• PESTEL分析

第 4 章：竞争格局

• 介绍
• 公司市占率分析
• 竞争定位矩阵
• 战略展望矩阵

第 5 章：市场估计与预测：按组成部分，2021 - 2032 年

• 主要趋势
• 解决方案
• 服务

第 6 章：市场估计与预测：依技术分类，2021 - 2032

• 主要趋势
• 机器学习
• 电脑视觉
• 预测分析

第 7 章：市场估计与预测：依应用分类，2021 - 2032

• 主要趋势
• 作物和土壤监测
• 牲畜健康监测
• 智慧喷涂
• 精耕
• 农业机器人
• 资料
• 其他的

第 8 章：市场估计与预测：按地区，2021 - 2032

• 主要趋势
• 北美洲
  • 我们
  • 加拿大
• 欧洲
  • 英国
  • 德国
  • 法国
  • 义大利
  • 荷兰
  • 西班牙
  • 欧洲其他地区
• 亚太地区
  • 中国
  • 印度
  • 日本
  • 新加坡
  • 澳新银行
  • 亚太地区其他地区
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
  • 拉丁美洲其他地区
• MEA
  • 伊朗
  • 摩洛哥
  • 叶门
  • 埃及
  • 南非
  • MEA 的其余部分

第 9 章：公司简介

• aWhere Inc.
• Climate LLC (The Climate Corporation)
• Corteva
• Descartes Labs, Inc
• ec2ce
• Ever.Ag, (Cainthus Corp)
• Gamya
• Granular Inc.
• IBM Corporation
• Microsoft Corporation
• PrecisionHawk Inc
• Taranis
• Valmont Industries (Prospera Technologies)
• Tule
• Vineview

AI in Agriculture Market size is projected to grow at over 24% CAGR between 2024 and 2032. The increasing global demand for food due to the growing population is necessitating enhanced productivity and efficiency. AI technologies like precision farming, predictive analytics, and automated machinery help optimize resource use and crop yields. Significant advancements in machine learning and data analytics are also enabling better decision-making and problem-solving.

The increasing investments and supportive government policies are attracting innovations and encouraging the adoption of AI in agriculture. For instance, in April 2024, agritech company Cropin Technology unveiled 'Aksara,' a generative AI system designed for climate-smart agriculture. Moreover, the rising need for sustainable farming practices to address environmental concerns is fueling the integration of AI solutions to monitor and manage crops, soil, and weather conditions effectively.

The overall industry is segregated into component, technology, application, and region.

Based on component, the AI in agriculture market from the service segment is anticipated to rise at notable rate between 2024 and 2032, owing to the rising demand for data analytics, farm management software, and predictive modeling services. Enhanced support and maintenance, coupled with training and consulting are driving the adoption of these services. The strong need for real-time insights and precision agriculture solutions to optimize productivity and sustainability is encouraging farmers to leverage AI-driven services.

In terms of application, AI in agriculture industry from the crop and soil monitoring segment is anticipated to grow at substantial CAGR through 2032, driven by the need for precise data on soil health and crop conditions. AI enables real-time monitoring, predictive analytics, and efficient resource management for enhancing crop yields and sustainability. The increasing awareness of sustainable farming practices along with the integration of IoT sensors and drones for detailed field analysis are also driving the segment growth ahead.

Regionally, the Asia Pacific AI in agriculture market will depict significant growth between 2024 and 2032, driven by rapid technological adoption, increasing investments in agri-tech startups, and the launch of government initiatives to support smart farming. The large agricultural sector of the region benefits from AI-enhanced productivity and efficiency. The growing awareness of sustainable practices and the surging need to address food security challenges is also accelerating the deployment of AI in agriculture in APAC.

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 360 degree synopsis, 2021 - 2032

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
• 3.2 Supplier landscape
  • 3.2.1 Component provider
  • 3.2.2 Technology provider
  • 3.2.3 Solution provider
  • 3.2.4 System integrators
  • 3.2.5 Agriculture equipment providers
  • 3.2.6 End users
• 3.3 Profit margin analysis
• 3.4 Technology & innovation landscape
• 3.5 Patent analysis
• 3.6 Key news & initiatives
• 3.7 Regulatory landscape
• 3.8 Impact forces
  • 3.8.1 Growth drivers
    • 3.8.1.1 Increasing crop production losses in the U.S.
    • 3.8.1.2 Increasing adoption of precision agriculture
    • 3.8.1.3 Proliferation of agritech solutions focused on crop nutrition
    • 3.8.1.4 Increasing investments in AI startups and agricultural technology companies
  • 3.8.2 Industry pitfalls & challenges
    • 3.8.2.1 High initial cost of implementing AI technologies
    • 3.8.2.2 Lack of infrastructure and connectivity
• 3.9 Growth potential analysis
• 3.10 Porter's analysis
• 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

• 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 - 2032 ($Bn)

• 5.1 Key trends
• 5.2 Solution
• 5.3 Service

Chapter 6 Market Estimates & Forecast, By Technology, 2021 - 2032 ($Bn)

• 6.1 Key trends
• 6.2 Machine learning
• 6.3 Computer vision
• 6.4 Predictive analysis

Chapter 7 Market Estimates & Forecast, By Application, 2021 - 2032 ($Bn)

• 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 Region, 2021 - 2032 ($Bn)

• 8.1 Key trends
• 8.2 North America
  • 8.2.1 U.S.
  • 8.2.2 Canada
• 8.3 Europe
  • 8.3.1 UK
  • 8.3.2 Germany
  • 8.3.3 France
  • 8.3.4 Italy
  • 8.3.5 Netherlands
  • 8.3.6 Spain
  • 8.3.7 Rest of Europe
• 8.4 Asia Pacific
  • 8.4.1 China
  • 8.4.2 India
  • 8.4.3 Japan
  • 8.4.4 Singapore
  • 8.4.5 ANZ
  • 8.4.6 Rest of Asia Pacific
• 8.5 Latin America
  • 8.5.1 Brazil
  • 8.5.2 Mexico
  • 8.5.3 Argentina
  • 8.5.4 Rest of Latin America
• 8.6 MEA
  • 8.6.1 Iran
  • 8.6.2 Morocco
  • 8.6.3 Yemen
  • 8.6.4 Egypt
  • 8.6.5 South Africa
  • 8.6.6 Rest of MEA

Chapter 9 Company Profiles

• 9.1 aWhere Inc.
• 9.2 Climate LLC (The Climate Corporation)
• 9.3 Corteva
• 9.4 Descartes Labs, Inc
• 9.5 ec2ce
• 9.6 Ever.Ag, (Cainthus Corp)
• 9.7 Gamya
• 9.8 Granular Inc.
• 9.9 IBM Corporation
• 9.10 Microsoft Corporation
• 9.11 PrecisionHawk Inc
• 9.12 Taranis
• 9.13 Valmont Industries (Prospera Technologies)
• 9.14 Tule
• 9.15 Vineview