세계의 농업용 인공지능(AI) 시장 조사 보고서 : 산업 분석, 규모, 점유율, 성장, 동향, 예측(2024-2032년)

The global demand for Artificial Intelligence in Agriculture Market is presumed to reach the market size of nearly USD 16174.55 Million by 2032 from USD 2026.46 Million in 2023 with a CAGR of 25.96% under the study period 2024-2032.

Artificial Intelligence (AI) in agriculture involves applying machine learning, robotics, and data analytics to improve farming practices and productivity. AI technologies analyze data from sensors, drones, and satellite imagery to monitor soil conditions, crop health, and weather patterns. This empowers farmers to make data-driven decisions concerning planting, irrigation, and pest management. AI in agriculture enhances efficiency, reduces resource usage, and increases yields, addressing challenges such as food security and environmental sustainability. It revolutionizes traditional farming methods, promoting precision agriculture and sustainable practices.

MARKET DYNAMICS

Artificial intelligence in the agriculture market is experiencing rapid growth due to several influential factors. One primary driver is the increasing pressure to enhance agricultural productivity and efficiency to sustainably meet the growing demand for food. AI technologies, such as predictive analytics & machine learning algorithms, offer farmers valuable insights and decision-making capabilities to optimize crop management practices, improve yields, and reduce resource usage. Additionally, the proliferation of precision agriculture techniques, including sensor-based monitoring systems and drones equipped with AI, is driving the adoption of AI in agriculture. These technologies enable farmers to collect & analyze data on soil conditions, weather patterns, crop health, and pest infestations, allowing for targeted interventions and optimized resource allocation. Furthermore, the rising adoption of AI-powered agricultural machinery and robotics, such as autonomous tractors and robotic harvesters, is revolutionizing farming operations and labor efficiency. Government initiatives and subsidies promoting digitalization and modernization of agriculture also drive the adoption of AI technologies in the agricultural sector. Moreover, the increasing availability of AI solutions tailored to the specific needs of farmers, coupled with the declining costs of sensors and data analytics tools, is making AI more accessible to small and medium-sized farms. As farmers continue to seek innovative solutions to improve productivity and sustainability, the demand for artificial intelligence in agriculture is expected to grow significantly in the coming years. However, high implementation costs and a lack of technical expertise may hinder the growth of artificial intelligence in the agriculture market in the next few years.

The research report covers Porter's Five Forces Model, Market Attractiveness Analysis, and Value Chain analysis. These tools help to get a clear picture of the industry's structure and evaluate the competition attractiveness at a global level. Additionally, these tools also give an inclusive assessment of each segment in the global market of Artificial Intelligence in Agriculture. The growth and trends of Artificial Intelligence in Agriculture industry provide a holistic approach to this study.

MARKET SEGMENTATION

This section of the Artificial Intelligence in Agriculture market report provides detailed data on the segments at country and regional level, thereby assisting the strategist in identifying the target demographics for the respective product or services with the upcoming opportunities.

By Component

• Hardware
• Software
• Service

By Technology

• Machine Learning & Deep Learning
• Predictive Analytics
• Computer Vision

By Application

• Precision Farming
• Drone Analytics
• Agriculture Robots
• Livestock Monitoring
• Others

REGIONAL ANALYSIS

This section covers the regional outlook, which accentuates current and future demand for the Artificial Intelligence in Agriculture market across North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa. Further, the report focuses on demand, estimation, and forecast for individual application segments across all the prominent regions.

The research report also covers the comprehensive profiles of the key players in the market and an in-depth view of the competitive landscape worldwide. The major players in the Artificial Intelligence in Agriculture market include IBM Corporation, Microsoft, Deere & Company, AgEagle Aerial Systems Inc., The Climate Corporation, Granular Inc., Descartes Labs Inc., Prospera Technologies, Taranis, AWhere Inc., GAMAYA, Ec2ce, PrecisionHawk, VineView, Tule Technologies Inc. This section consists of a holistic view of the competitive landscape that includes various strategic developments such as key mergers & acquisitions, future capacities, partnerships, financial overviews, collaborations, new product developments, new product launches, and other developments.

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TABLE OF CONTENTS

1. PREFACE

• 1.1. Report Description
  • 1.1.1 Objective
  • 1.1.2 Target Audience
  • 1.1.3 Unique Selling Proposition (USP) & offerings
• 1.2. Research Scope
• 1.3. Research Methodology
  • 1.3.1 Market Research Process
  • 1.3.2 Market Research Methodology

2. EXECUTIVE SUMMARY

• 2.1. Highlights of Market
• 2.2. Global Market Snapshot

3. ARTIFICIAL INTELLIGENCE IN AGRICULTURE - INDUSTRY ANALYSIS

• 3.1. Introduction - Market Dynamics
• 3.2. Market Drivers
• 3.3. Market Restraints
• 3.4. Opportunities
• 3.5. Industry Trends
• 3.6. Porter's Five Force Analysis
• 3.7. Market Attractiveness Analysis
  • 3.7.1 Market Attractiveness Analysis By Component
  • 3.7.2 Market Attractiveness Analysis By Technology
  • 3.7.3 Market Attractiveness Analysis By Application
  • 3.7.4 Market Attractiveness Analysis By Region

4. VALUE CHAIN ANALYSIS

• 4.1. Value Chain Analysis
• 4.2. Raw Material Analysis
  • 4.2.1 List of Raw Materials
  • 4.2.2 Raw Material Manufactures List
  • 4.2.3 Price Trend of Key Raw Materials
• 4.3. List of Potential Buyers
• 4.4. Marketing Channel
  • 4.4.1 Direct Marketing
  • 4.4.2 Indirect Marketing
  • 4.4.3 Marketing Channel Development Trend

5. GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET ANALYSIS BY COMPONENT

• 5.1. Overview By Component
• 5.2. Historical and Forecast Data Analysis By Component
• 5.3. Hardware Historic and Forecast Sales By Regions
• 5.4. Software Historic and Forecast Sales By Regions
• 5.5. Service Historic and Forecast Sales By Regions

6. GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET ANALYSIS BY TECHNOLOGY

• 6.1. Overview By Technology
• 6.2. Historical and Forecast Data Analysis By Technology
• 6.3. Machine Learning & Deep Learning Historic and Forecast Sales By Regions
• 6.4. Predictive Analytics Historic and Forecast Sales By Regions
• 6.5. Computer Vision Historic and Forecast Sales By Regions

7. GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET ANALYSIS BY APPLICATION

• 7.1. Overview By Application
• 7.2. Historical and Forecast Data Analysis By Application
• 7.3. Precision Farming Historic and Forecast Sales By Regions
• 7.4. Drone Analytics Historic and Forecast Sales By Regions
• 7.5. Agriculture Robots Historic and Forecast Sales By Regions
• 7.6. Livestock Monitoring Historic and Forecast Sales By Regions
• 7.7. Others Historic and Forecast Sales By Regions

8. GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET ANALYSIS BY GEOGRAPHY

• 8.1. Regional Outlook
• 8.2. Introduction
• 8.3. North America Sales Analysis
  • 8.3.1 Overview, Historic and Forecast Data Sales Analysis
  • 8.3.2 North America By Segment Sales Analysis
  • 8.3.3 North America By Country Sales Analysis
  • 8.3.4 United States Sales Analysis
  • 8.3.5 Canada Sales Analysis
  • 8.3.6 Mexico Sales Analysis
• 8.4. Europe Sales Analysis
  • 8.4.1 Overview, Historic and Forecast Data Sales Analysis
  • 8.4.2 Europe By Segment Sales Analysis
  • 8.4.3 Europe By Country Sales Analysis
  • 8.4.4 United Kingdom Sales Analysis
  • 8.4.5 France Sales Analysis
  • 8.4.6 Germany Sales Analysis
  • 8.4.7 Italy Sales Analysis
  • 8.4.8 Russia Sales Analysis
  • 8.4.9 Rest Of Europe Sales Analysis
• 8.5. Asia Pacific Sales Analysis
  • 8.5.1 Overview, Historic and Forecast Data Sales Analysis
  • 8.5.2 Asia Pacific By Segment Sales Analysis
  • 8.5.3 Asia Pacific By Country Sales Analysis
  • 8.5.4 China Sales Analysis
  • 8.5.5 India Sales Analysis
  • 8.5.6 Japan Sales Analysis
  • 8.5.7 South Korea Sales Analysis
  • 8.5.8 Australia Sales Analysis
  • 8.5.9 South East Asia Sales Analysis
  • 8.5.10 Rest Of Asia Pacific Sales Analysis
• 8.6. Latin America Sales Analysis
  • 8.6.1 Overview, Historic and Forecast Data Sales Analysis
  • 8.6.2 Latin America By Segment Sales Analysis
  • 8.6.3 Latin America By Country Sales Analysis
  • 8.6.4 Brazil Sales Analysis
  • 8.6.5 Argentina Sales Analysis
  • 8.6.6 Peru Sales Analysis
  • 8.6.7 Chile Sales Analysis
  • 8.6.8 Rest of Latin America Sales Analysis
• 8.7. Middle East & Africa Sales Analysis
  • 8.7.1 Overview, Historic and Forecast Data Sales Analysis
  • 8.7.2 Middle East & Africa By Segment Sales Analysis
  • 8.7.3 Middle East & Africa By Country Sales Analysis
  • 8.7.4 Saudi Arabia Sales Analysis
  • 8.7.5 UAE Sales Analysis
  • 8.7.6 Israel Sales Analysis
  • 8.7.7 South Africa Sales Analysis
  • 8.7.8 Rest Of Middle East And Africa Sales Analysis

9. COMPETITIVE LANDSCAPE OF THE ARTIFICIAL INTELLIGENCE IN AGRICULTURE COMPANIES

• 9.1. Artificial Intelligence in Agriculture Market Competition
• 9.2. Partnership/Collaboration/Agreement
• 9.3. Merger And Acquisitions
• 9.4. New Product Launch
• 9.5. Other Developments

10. COMPANY PROFILES OF ARTIFICIAL INTELLIGENCE IN AGRICULTURE INDUSTRY

• 10.1. Top Companies Market Share Analysis
• 10.2. Market Concentration Rate
• 10.3. IBM Corporation
  • 10.3.1 Company Overview
  • 10.3.2 Company Revenue
  • 10.3.3 Products
  • 10.3.4 Recent Developments
• 10.4. Microsoft
  • 10.4.1 Company Overview
  • 10.4.2 Company Revenue
  • 10.4.3 Products
  • 10.4.4 Recent Developments
• 10.5. Deere & Company
  • 10.5.1 Company Overview
  • 10.5.2 Company Revenue
  • 10.5.3 Products
  • 10.5.4 Recent Developments
• 10.6. AgEagle Aerial Systems Inc.
  • 10.6.1 Company Overview
  • 10.6.2 Company Revenue
  • 10.6.3 Products
  • 10.6.4 Recent Developments
• 10.7. The Climate Corporation
  • 10.7.1 Company Overview
  • 10.7.2 Company Revenue
  • 10.7.3 Products
  • 10.7.4 Recent Developments
• 10.8. Granular Inc.
  • 10.8.1 Company Overview
  • 10.8.2 Company Revenue
  • 10.8.3 Products
  • 10.8.4 Recent Developments
• 10.9. Descartes Labs Inc.
  • 10.9.1 Company Overview
  • 10.9.2 Company Revenue
  • 10.9.3 Products
  • 10.9.4 Recent Developments
• 10.10. Prospera Technologies
  • 10.10.1 Company Overview
  • 10.10.2 Company Revenue
  • 10.10.3 Products
  • 10.10.4 Recent Developments
• 10.11. Taranis
  • 10.11.1 Company Overview
  • 10.11.2 Company Revenue
  • 10.11.3 Products
  • 10.11.4 Recent Developments
• 10.12. AWhere Inc.
  • 10.12.1 Company Overview
  • 10.12.2 Company Revenue
  • 10.12.3 Products
  • 10.12.4 Recent Developments
• 10.13. GAMAYA
  • 10.13.1 Company Overview
  • 10.13.2 Company Revenue
  • 10.13.3 Products
  • 10.13.4 Recent Developments
• 10.14. Ec2ce
  • 10.14.1 Company Overview
  • 10.14.2 Company Revenue
  • 10.14.3 Products
  • 10.14.4 Recent Developments
• 10.15. PrecisionHawk
  • 10.15.1 Company Overview
  • 10.15.2 Company Revenue
  • 10.15.3 Products
  • 10.15.4 Recent Developments
• 10.16. VineView
  • 10.16.1 Company Overview
  • 10.16.2 Company Revenue
  • 10.16.3 Products
  • 10.16.4 Recent Developments
• 10.17. Tule Technologies Inc
  • 10.17.1 Company Overview
  • 10.17.2 Company Revenue
  • 10.17.3 Products
  • 10.17.4 Recent Developments

Note - In company profiling, financial details and recent developments are subject to availability or might not be covered in the case of private companies