查看購物車
  • Simplified Chinese
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1494858

到 2030 年農業人工智慧市場預測：按作物類型、成分、部署模式、技術、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，2024 年全球農業人工智慧市場規模為 19.5 億美元，預計預測期內年複合成長率為 25.2%，到 2030 年將達到 65.3 億美元。

農業中的人工智慧是指應用機器學習、電腦視覺、機器人技術和資料分析來增強農業運作。人工智慧主導的技術透過分析各種資訊來源的資料（包括土壤感測器、天氣預報和衛星圖像）來實現精密農業。這些技術有助於最佳化作物產量、減少資源使用並最大限度地減少對環境的影響。人工智慧簡化了害蟲檢測、作物監測和自動收割等任務，使農業經營更有效率、更永續性、更盈利。

據 NASSCOM 稱，到 2025 年，資料和人工智慧技術將為印度農業部門付加約 900 億美元的價值。總體而言，到 2025 年，人工智慧預計將為印度 GDP 增加約 5,000 億美元。

糧食生產需求增加

糧食生產的需求增加將推動農業人工智慧的發展，因為需要高效的資源利用、最大化產量和永續的實踐。精密農業、預測分析和自動化機械等人工智慧技術將最佳化資源利用、提高作物產量並減少廢棄物。隨著世界人口的成長，農民將採用人工智慧來永續滿足糧食供應需求。先進的人工智慧應用將透過促進即時監測、害蟲管理和資料主導的決策來推動市場成長，使農業更具彈性和應對挑戰的能力。

缺乏技術專長

農業人工智慧（AI）技術專業知識的缺乏是由於該行業對傳統耕作方法的依賴以及對先進技術的接觸有限。技術知識不足正在阻礙人工智慧的全部潛力得到充分利用，阻礙創新、資料主導的決策和農業整體生產力的提高。因此，人工智慧技術的採用速度將會放緩，限制其市場擴張和對該產業的變革性影響。

加大對農業技術新興企業的投資

增加對農業技術新興企業的投資將推動先進的人工智慧驅動解決方案的創新和開發。這些投資將使新興企業能夠透過機器學習、電腦視覺和資料分析等人工智慧技術加強精密農業、最佳化資源利用並提高作物產量。增加的資金籌措將加速研究和開發，以創建更強大和可擴展的人工智慧應用程式，從而改變農業實踐、提高生產力並應對氣候變遷和糧食安全等挑戰。

初期投資成本高

農業人工智慧需要先進的技術、基礎設施和熟練的人力資源，導致初始投資成本高。開發和實施機器學習演算法、機器人和物聯網設備等人工智慧系統需要大量資金。因此，市場成長受到廣泛採用放緩、進入障礙以及農業部門技術進步和生產力成長整體步伐放緩的阻礙。

COVID-19 的影響

COVID-19 大流行凸顯了食品供應鏈對自動化和彈性的需求，並加速了人工智慧在農業中的採用。勞動力短缺和物流中斷引發了人們對人工智慧主導的精密農業、遠端監控和自動收割解決方案的興趣。然而，經濟不確定性和供應鏈中斷也帶來了挑戰，影響了農業人工智慧技術的投資和實施時間表。

機器人與自動化產業預計將在預測期內成為最大的產業

機器人和自動化領域預計將出現良好的成長。農業機器人和自動化利用人工智慧來提高效率和生產力。自動曳引機、無人機和機器人收割機使用人工智慧來執行種植、澆水和收割等精準任務。這些技術可以即時監測和管理作物，降低人事費用並提高產量。人工智慧主導的自動化可確保資源的最佳利用，最大限度地減少浪費，並有助於資料主導的決策，以實現更好的作物管理和永續性。

預計現場準備部分在預測期間內年複合成長率最高

預計在預測期內，田間準備產業將以最高的年複合成長率成長。人工智慧主導的農業中的田間準備涉及使用土壤感測器、無人機和機器學習演算法等技術來分析土壤健康、濕度水平和養分含量。這些資料指南農民最佳化犁地、種植計劃和土壤處理，從而提高作物產量、降低投入成本和永續的農業實踐。人工智慧支援精確的田間測繪和決策，提高農業的整體效率和生產力。

比最大的地區

由於糧食需求增加、政府措施和技術進步，預計亞太地區在預測期內將佔據最大的市場佔有率。中國、印度和日本等國家在將人工智慧應用於精密農業、作物監測和自動化機械方面處於領先地位。快速的都市化、技術進步和不斷變化的飲食偏好正在重塑市場動態。該地區龐大的農業基地，加上對農業科技新興企業投資的增加，正在推動人工智慧解決方案的創新和實施。

年複合成長率最高的地區：

在該地區精密農業技術的推動下，預計歐洲在預測期內將出現最高的年複合成長率。歐洲既有小型家庭農場，也有大型商業農場，人們越來越關注永續性和有機生產方法。歐洲的支持性法規環境和政府措施極大地促進了數位農業。這一趨勢表明，人工智慧在歐洲農業中的整合前景廣闊，並將徹底改變該行業的營運格局。

提供免費客製化：

訂閱此報告的客戶可以存取以下免費自訂選項之一：

  • 公司簡介
    • 其他市場參與者的綜合分析（最多 3 家公司）
    • 主要企業SWOT分析（最多3家企業）
  • 區域分割
    • 根據客戶興趣對主要國家的市場估計、預測和年複合成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 研究資訊來源
    • 主要研究資訊來源
    • 二次研究資訊來源
    • 先決條件

第3章市場趨勢分析

  • 促進因素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19 的影響

第4章波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭公司之間的敵對關係

第5章全球農業人工智慧市場：按作物類型

  • 穀類/穀物
  • 油籽和豆類
  • 水果和蔬菜
  • 其他作物類型

第6章全球農業市場人工智慧：按組成部分

  • 硬體
    • 感應器
    • 無人機
    • 機器人
  • 軟體
    • 人工智慧平台
    • 人工智慧解決方案
  • 服務
    • 專業服務
    • 管理服務

第7章全球農業人工智慧市場：依部署模式

  • 雲端基礎
  • 本地

第8章全球農業人工智慧市場：依技術分類

  • 機器學習
  • 電腦視覺
  • 預測分析
  • 自然語言處理（NLP）
  • 機器人和自動化
  • 其他技術

第9章全球農業人工智慧市場：依應用分類

  • 精密農業
  • 牲畜監測
  • 土壤管理
  • 現場準備
  • 其他用途

第10章全球農業市場人工智慧：依最終用戶分類

  • 農民
  • 農業產業
  • 研究機構
  • 政府機關
  • 其他最終用戶

第11章全球農業人工智慧市場：按地區

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東/非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲

第12章 主要進展

  • 合約、夥伴關係、協作和合資企業
  • 收購和合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第13章 公司概況

  • 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.