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市场调查报告书
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1494858

到 2030 年农业人工智慧市场预测：按作物类型、成分、部署模式、技术、应用、最终用户和地区进行的全球分析

Artificial Intelligence in Agriculture Market Forecasts to 2030 - Global Analysis By Crop Type, Component, Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，2024 年全球农业人工智慧市场规模为 19.5 亿美元，预计预测期内复合年增长率为 25.2%，到 2030 年将达到 65.3 亿美元。

农业中的人工智慧是指应用机器学习、电脑视觉、机器人技术和资料分析来增强农业运作。人工智慧主导的技术透过分析各种资讯来源的资料（包括土壤感测器、天气预报和卫星图像）来实现精密农业。这些技术有助于优化作物产量、减少资源使用并最大限度地减少对环境的影响。人工智慧简化了害虫检测、作物监测和自动收割等任务，使农业经营更有效率、更永续性、更盈利。

据 NASSCOM 称，到 2025 年，资料和人工智慧技术将为印度农业部门付加约 900 亿美元的价值。总体而言，到 2025 年，人工智慧预计将为印度 GDP 增加约 5,000 亿美元。

粮食生产需求增加

粮食生产的需求增加将推动农业人工智慧的发展，因为需要高效的资源利用、最大化产量和永续的实践。精密农业、预测分析和自动化机械等人工智慧技术将优化资源利用、提高作物产量并减少废弃物。随着世界人口的增长，农民将采用人工智慧来永续满足粮食供应需求。先进的人工智慧应用将透过促进即时监测、害虫管理和资料主导的决策来推动市场成长，使农业更具弹性和应对挑战的能力。

缺乏技术专长

农业人工智慧（AI）技术专业知识的缺乏是由于该行业对传统耕作方法的依赖以及对先进技术的接触有限。技术知识不足正在阻碍人工智慧的全部潜力得到充分利用，阻碍创新、资料主导的决策和农业整体生产力的提高。因此，人工智慧技术的采用速度将会放缓，限制其市场扩张和对该产业的变革性影响。

加大对农业技术新兴企业的投资

增加对农业技术新兴企业的投资将推动先进的人工智慧驱动解决方案的创新和开发。这些投资将使新兴企业能够透过机器学习、电脑视觉和资料分析等人工智慧技术加强精密农业、优化资源利用并提高作物产量。增加的资金筹措将加速研究和开发，以创建更强大和可扩展的人工智慧应用程序，从而改变农业实践、提高生产力并应对气候变迁和粮食安全等挑战。

初期投资成本高

农业人工智慧需要先进的技术、基础设施和熟练的人力资源，导致初始投资成本高。开发和实施机器学习演算法、机器人和物联网设备等人工智慧系统需要大量资金。因此，市场成长受到广泛采用放缓、进入障碍以及农业部门技术进步和生产力成长整体步伐放缓的阻碍。

COVID-19 的影响

COVID-19 大流行凸显了食品供应链对自动化和弹性的需求，并加速了人工智慧在农业中的采用。劳动力短缺和物流中断引发了人们对人工智慧主导的精密农业、远端监控和自动收割解决方案的兴趣。然而，经济不确定性和供应链中断也带来了挑战，影响了农业人工智慧技术的投资和实施时间表。

机器人与自动化产业预计将在预测期内成为最大的产业

机器人和自动化领域预计将出现良好的成长。农业机器人和自动化利用人工智慧来提高效率和生产力。自动拖拉机、无人机和机器人收割机使用人工智慧来执行种植、浇水和收割等精准任务。这些技术可以即时监测和管理作物，降低人事费用并提高产量。人工智慧主导的自动化可确保资源的最佳利用，最大限度地减少浪费，并有助于资料主导的决策，以实现更好的作物管理和永续性。

预计现场准备部分在预测期间内复合年增长率最高

预计在预测期内，田间准备产业将以最高的复合年增长率成长。人工智慧主导的农业中的田间准备涉及使用土壤感测器、无人机和机器学习演算法等技术来分析土壤健康、湿度水平和养分含量。这些资料指南农民优化犁地、种植计划和土壤处理，从而提高作物产量、降低投入成本和永续的农业实践。人工智慧支援精确的田间测绘和决策，提高农业的整体效率和生产力。

比最大的地区

由于粮食需求增加、政府措施和技术进步，预计亚太地区在预测期内将占据最大的市场占有率。中国、印度和日本等国家在将人工智慧应用于精密农业、作物监测和自动化机械方面处于领先地位。快速的都市化、技术进步和不断变化的饮食偏好正在重塑市场动态。该地区庞大的农业基地，加上对农业科技新兴企业投资的增加，正在推动人工智慧解决方案的创新和实施。

复合年增长率最高的地区：

在该地区精密农业技术的推动下，预计欧洲在预测期内将出现最高的复合年增长率。欧洲既有小型家庭农场，也有大型商业农场，人们越来越关注永续性和有机生产方法。欧洲的支持性法规环境和政府措施极大地促进了数位农业。这一趋势表明，人工智慧在欧洲农业中的整合前景广阔，并将彻底改变该行业的营运格局。

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  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究资讯来源
    • 主要研究资讯来源
    • 二次研究资讯来源
    • 先决条件

第三章市场趋势分析

  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19 的影响

第4章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第五章全球农业人工智慧市场：按作物类型

  • 谷类/谷物
  • 油籽和豆类
  • 水果和蔬菜
  • 其他作物类型

第六章全球农业市场人工智慧：按组成部分

  • 硬体
    • 感应器
    • 无人机
    • 机器人
  • 软体
    • 人工智慧平台
    • 人工智慧解决方案
  • 服务
    • 专业服务
    • 管理服务

第七章全球农业人工智慧市场：依部署模式

  • 云端基础
  • 本地

第八章全球农业人工智慧市场：依技术分类

  • 机器学习
  • 电脑视觉
  • 预测分析
  • 自然语言处理（NLP）
  • 机器人和自动化
  • 其他技术

第九章全球农业人工智慧市场：依应用分类

  • 精密农业
  • 牲畜监测
  • 土壤管理
  • 现场准备
  • 其他用途

第10章全球农业市场人工智慧：依最终用户分类

  • 农民
  • 农业产业
  • 研究机构
  • 政府机关
  • 其他最终用户

第十一章全球农业人工智慧市场：按地区

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东/非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲

第十二章 主要进展

  • 合约、伙伴关係、协作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十三章 公司概况

  • IBM Corporation
  • Microsoft Corporation
  • Deere & Company
  • Bayer AG
  • Trimble Inc.
  • AG Leader Technology
  • Cropin Technology Solutions Pvt. Ltd.
  • Agribotix LLC
  • Prospera Technologies
  • Descartes Labs
  • Taranis
  • Corteva
  • aWhere Inc.
  • Ceres Imaging
  • Gamaya
Product Code: SMRC26378

According to Stratistics MRC, the Global Artificial Intelligence in Agriculture Market is accounted for $1.95 billion in 2024 and is expected to reach $6.53 billion by 2030 growing at a CAGR of 25.2% during the forecast period. Artificial Intelligence in agriculture refers to the application of machine learning, computer vision, robotics, and data analytics to enhance farming practices. AI-driven technologies enable precision farming by analyzing data from various sources such as soil sensors, weather forecasts, and satellite imagery. These technologies assist in optimizing crop yields, reducing resource usage, and minimizing environmental impact. Tasks such as pest detection, crop monitoring, and automated harvesting are streamlined through AI, leading to improved efficiency, sustainability, and profitability in agricultural operations.

According to NASSCOM, by 2025, approximately USD 90 billion of value will be added to the agriculture sector through data and AI technologies in India. With all the sectors combined, artificial intelligence is projected to add approximately USD 500 billion to India's GDP by 2025.

Market Dynamics:

Driver:

Increasing demand for food production

Increasing food production demand drives AI growth in agriculture by necessitating efficient resource use, yield maximization, and sustainable practices. AI technologies, such as precision farming, predictive analytics, and automated machinery, optimize resource use, improve crop yields, and reduce waste. As the global population rises, farmers adopt AI to meet food supply demands sustainably. Advanced AI applications facilitate real-time monitoring, pest and disease management, and data-driven decision-making, making agriculture more resilient and responsive to challenges, thereby propelling market growth.

Restraint:

Lack of technical expertise

The lack of technical expertise in Artificial Intelligence (AI) in agriculture stems from the sector's traditional reliance on conventional farming methods and limited exposure to advanced technologies. Insufficient technical know-how leads to underutilization of AI's potential, hindering innovation, data-driven decision-making and overall productivity improvements in agriculture. Consequently, the adoption rate of AI technologies slows, limiting the market's expansion and its transformative impact on the sector.

Opportunity:

Rising investments in agritech start-ups

Rising investments in agritech start-ups fosters innovation and development of advanced AI-driven solutions. These investments enable start-ups to enhance precision farming, optimize resource utilization, and improve crop yield through AI technologies like machine learning, computer vision, and data analytics. Increased funding accelerates research and development, leading to more robust and scalable AI applications, thereby transforming agricultural practices, boosting productivity, and addressing challenges such as climate change and food security.

Threat:

High initial investment costs

Artificial Intelligence in agriculture involves high initial investment costs due to the need for advanced technologies, infrastructure, and skilled personnel. Developing and implementing AI systems, such as machine learning algorithms, robotics, and IoT devices, requires substantial financial resources. Consequently, market growth is hampered as widespread implementation is slowed, creating a barrier to entry and reducing the overall pace of technological advancement and productivity improvements in the agricultural sector.

Covid-19 Impact

The covid-19 pandemic accelerated the adoption of AI in agriculture by highlighting the need for automation and resilience in food supply chains. Labor shortages and disrupted logistics spurred interest in AI-driven solutions for precision farming, remote monitoring, and automated harvesting. However, economic uncertainties and disrupted supply chains also posed challenges, affecting investment and implementation timelines for AI technologies in the agricultural sector.

The robotics & automation segment is expected to be the largest during the forecast period

The robotics & automation segment is estimated to have a lucrative growth. Robotics and automation in agriculture leverage AI to enhance efficiency and productivity. Autonomous tractors, drones, and robotic harvesters use AI for precision tasks like planting, watering, and harvesting. These technologies enable real-time monitoring and management of crops, reducing labor costs and increasing yields. AI-driven automation ensures optimal use of resources, minimizes waste, and helps in making data-driven decisions for better crop management and sustainability.

The field preparation segment is expected to have the highest CAGR during the forecast period

The field preparation segment is anticipated to witness the highest CAGR growth during the forecast period. Field preparation in AI-driven agriculture involves using technologies like soil sensors, drones, and machine learning algorithms to analyze soil health, moisture levels, and nutrient content. This data guides farmers in optimizing tillage, planting schedules, and soil treatment, leading to improved crop yields, reduced input costs, and sustainable farming practices. AI aids in precise field mapping and decision-making, enhancing overall efficiency and productivity in agriculture.

Region with largest share:

Asia Pacific is projected to hold the largest market share during the forecast period due to increasing food demand, government initiatives, and advancements in technology. Countries like China, India, and Japan are leading in adopting AI for precision farming, crop monitoring, and automated machinery. Rapid urbanization, technological advancements, and shifting dietary preferences are reshaping the market dynamics. The region's large agricultural base, coupled with rising investments in AgriTech start-ups, fosters innovation and implementation of AI solutions.

Region with highest CAGR:

Europe is projected to have the highest CAGR over the forecast period, driven by the region's precision farming techniques. Europe is marked by a mix of small-scale family farms and large commercial operations, with an increasing focus on sustainability and organic production methods. Europe's supportive regulatory environment and government initiatives are highly promoting digital agriculture. This trend indicates a promising future for AI integration in European agriculture, poised to revolutionize the sector's operational landscape.

Key players in the market

Some of the key players profiled in the Artificial Intelligence in Agriculture Market include IBM Corporation, Microsoft Corporation, Deere & Company, Bayer AG, Trimble Inc., AG Leader Technology, Cropin Technology Solutions Pvt. Ltd., Agribotix LLC, Prospera Technologies, Descartes Labs, Taranis, Corteva, aWhere Inc., Ceres Imaging and Gamaya.

Key Developments:

In April 2024, Cropin launched Aksara, a generative AI system for climate smart agriculture. Aksara will cover nine crops such as paddy, wheat, maize, sorghum, barley, cotton, sugarcane, soybean, and millets for 5 countries in the Indian subcontinent. This generative AI system can suggest farmers which inputs to use for crops like rice or maize under specific agro-climatic conditions or provide climate smart agri-advisories, the company said in a statement.

In June 2023, Deere & Company has unveiled its first fully autonomous tractor, which is already operational on select farms and available for purchase. This tractor is a product of 20 years of AI development and is designed to complete tasks on time, every time, and at a high level of quality.

Crop Types Covered:

  • Cereals & Grains
  • Oilseeds & Pulses
  • Fruits & Vegetables
  • Other Crop Types

Components Covered:

  • Hardware
  • Software
  • Services

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises

Technologies Covered:

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

Applications Covered:

  • Precision Farming
  • Livestock Monitoring
  • Soil Management
  • Field Preparation
  • Other Applications

End Users Covered:

  • Farmers
  • Agribusinesses
  • Research Organizations
  • Government Bodies
  • 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 2022, 2023, 2024, 2026, and 2030
  • 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

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Artificial Intelligence in Agriculture Market, By Crop Type

  • 5.1 Introduction
  • 5.2 Cereals & Grains
  • 5.3 Oilseeds & Pulses
  • 5.4 Fruits & Vegetables
  • 5.5 Other Crop Types

6 Global Artificial Intelligence in Agriculture Market, By Component

  • 6.1 Introduction
  • 6.2 Hardware
    • 6.2.1 Sensors
    • 6.2.2 Drones
    • 6.2.3 Robots
  • 6.3 Software
    • 6.3.1 Artificial Intelligence Platforms
    • 6.3.2 Artificial Intelligence Solutions
  • 6.4 Services
    • 6.4.1 Professional Services
    • 6.4.2 Managed Services

7 Global Artificial Intelligence in Agriculture Market, By Deployment Mode

  • 7.1 Introduction
  • 7.2 Cloud-Based
  • 7.3 On-Premises

8 Global Artificial Intelligence in Agriculture Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning
  • 8.3 Computer Vision
  • 8.4 Predictive Analytics
  • 8.5 Natural Language Processing (NLP)
  • 8.6 Robotics & Automation
  • 8.7 Other Technologies

9 Global Artificial Intelligence in Agriculture Market, By Application

  • 9.1 Introduction
  • 9.2 Precision Farming
  • 9.3 Livestock Monitoring
  • 9.4 Soil Management
  • 9.5 Field Preparation
  • 9.6 Other Applications

10 Global Artificial Intelligence in Agriculture Market, By End User

  • 10.1 Introduction
  • 10.2 Farmers
  • 10.3 Agribusinesses
  • 10.4 Research Organizations
  • 10.5 Government Bodies
  • 10.6 Other End Users

11 Global Artificial Intelligence in Agriculture Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 IBM Corporation
  • 13.2 Microsoft Corporation
  • 13.3 Deere & Company
  • 13.4 Bayer AG
  • 13.5 Trimble Inc.
  • 13.6 AG Leader Technology
  • 13.7 Cropin Technology Solutions Pvt. Ltd.
  • 13.8 Agribotix LLC
  • 13.9 Prospera Technologies
  • 13.10 Descartes Labs
  • 13.11 Taranis
  • 13.12 Corteva
  • 13.13 aWhere Inc.
  • 13.14 Ceres Imaging
  • 13.15 Gamaya

List of Tables

  • Table 1 Global Artificial Intelligence in Agriculture Market Outlook, By Region (2022-2030) ($MN)
  • Table 2 Global Artificial Intelligence in Agriculture Market Outlook, By Crop Type (2022-2030) ($MN)
  • Table 3 Global Artificial Intelligence in Agriculture Market Outlook, By Cereals & Grains (2022-2030) ($MN)
  • Table 4 Global Artificial Intelligence in Agriculture Market Outlook, By Oilseeds & Pulses (2022-2030) ($MN)
  • Table 5 Global Artificial Intelligence in Agriculture Market Outlook, By Fruits & Vegetables (2022-2030) ($MN)
  • Table 6 Global Artificial Intelligence in Agriculture Market Outlook, By Other Crop Types (2022-2030) ($MN)
  • Table 7 Global Artificial Intelligence in Agriculture Market Outlook, By Component (2022-2030) ($MN)
  • Table 8 Global Artificial Intelligence in Agriculture Market Outlook, By Hardware (2022-2030) ($MN)
  • Table 9 Global Artificial Intelligence in Agriculture Market Outlook, By Sensors (2022-2030) ($MN)
  • Table 10 Global Artificial Intelligence in Agriculture Market Outlook, By Drones (2022-2030) ($MN)
  • Table 11 Global Artificial Intelligence in Agriculture Market Outlook, By Robots (2022-2030) ($MN)
  • Table 12 Global Artificial Intelligence in Agriculture Market Outlook, By Software (2022-2030) ($MN)
  • Table 13 Global Artificial Intelligence in Agriculture Market Outlook, By Artificial Intelligence Platforms (2022-2030) ($MN)
  • Table 14 Global Artificial Intelligence in Agriculture Market Outlook, By Artificial Intelligence Solutions (2022-2030) ($MN)
  • Table 15 Global Artificial Intelligence in Agriculture Market Outlook, By Services (2022-2030) ($MN)
  • Table 16 Global Artificial Intelligence in Agriculture Market Outlook, By Professional Services (2022-2030) ($MN)
  • Table 17 Global Artificial Intelligence in Agriculture Market Outlook, By Managed Services (2022-2030) ($MN)
  • Table 18 Global Artificial Intelligence in Agriculture Market Outlook, By Deployment Mode (2022-2030) ($MN)
  • Table 19 Global Artificial Intelligence in Agriculture Market Outlook, By Cloud-Based (2022-2030) ($MN)
  • Table 20 Global Artificial Intelligence in Agriculture Market Outlook, By On-Premises (2022-2030) ($MN)
  • Table 21 Global Artificial Intelligence in Agriculture Market Outlook, By Technology (2022-2030) ($MN)
  • Table 22 Global Artificial Intelligence in Agriculture Market Outlook, By Machine Learning (2022-2030) ($MN)
  • Table 23 Global Artificial Intelligence in Agriculture Market Outlook, By Computer Vision (2022-2030) ($MN)
  • Table 24 Global Artificial Intelligence in Agriculture Market Outlook, By Predictive Analytics (2022-2030) ($MN)
  • Table 25 Global Artificial Intelligence in Agriculture Market Outlook, By Natural Language Processing (NLP) (2022-2030) ($MN)
  • Table 26 Global Artificial Intelligence in Agriculture Market Outlook, By Robotics & Automation (2022-2030) ($MN)
  • Table 27 Global Artificial Intelligence in Agriculture Market Outlook, By Other Technologies (2022-2030) ($MN)
  • Table 28 Global Artificial Intelligence in Agriculture Market Outlook, By Application (2022-2030) ($MN)
  • Table 29 Global Artificial Intelligence in Agriculture Market Outlook, By Precision Farming (2022-2030) ($MN)
  • Table 30 Global Artificial Intelligence in Agriculture Market Outlook, By Livestock Monitoring (2022-2030) ($MN)
  • Table 31 Global Artificial Intelligence in Agriculture Market Outlook, By Soil Management (2022-2030) ($MN)
  • Table 32 Global Artificial Intelligence in Agriculture Market Outlook, By Field Preparation (2022-2030) ($MN)
  • Table 33 Global Artificial Intelligence in Agriculture Market Outlook, By Other Applications (2022-2030) ($MN)
  • Table 34 Global Artificial Intelligence in Agriculture Market Outlook, By End User (2022-2030) ($MN)
  • Table 35 Global Artificial Intelligence in Agriculture Market Outlook, By Farmers (2022-2030) ($MN)
  • Table 36 Global Artificial Intelligence in Agriculture Market Outlook, By Agribusinesses (2022-2030) ($MN)
  • Table 37 Global Artificial Intelligence in Agriculture Market Outlook, By Research Organizations (2022-2030) ($MN)
  • Table 38 Global Artificial Intelligence in Agriculture Market Outlook, By Government Bodies (2022-2030) ($MN)
  • Table 39 Global Artificial Intelligence in Agriculture Market Outlook, By Other End Users (2022-2030) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.