농업용 AI 시장 규모 조사 : 용도별, 도입별, 지역별 예측(2022-2032년)

Global AI in Agriculture Market is valued approximately at USD 1753.2 million in 2023 and is anticipated to grow with a healthy growth rate of more than 26.21% over the forecast period 2024-2032. AI in agriculture encompasses the adoption of machine learning algorithms, computer vision, robotics, and other AI techniques to improve agricultural production, resource management, crop health monitoring, task automation, and data-driven decision-making. AI in agriculture seeks to improve efficiency, production, and sustainability in farming while lowering costs and adverse environmental effects. Furthermore, rising integration of drone analytics are gaining attention towards Global AI in Agriculture Market. Drones equipped with cameras and sensors can collect high-resolution imagery and data over large agricultural areas. AI algorithms can then analyze this data to provide farmers with valuable insights about crop health, nutrient levels, water stress, pest infestations, and other factors.

The Global AI in Agriculture Market is driven by rising demand for precision farming techniques and increase use of unmanned aerial vehicles (UAVs) within agricultural farms across the world. Precision farming techniques streamline farm operations and improve overall efficiency. By automating repetitive tasks such as field monitoring, irrigation scheduling, and pest detection, AI technologies reduce the need for manual labor and enable farmers to focus their efforts on more strategic activities. In addition, UAVs enable precision agriculture by providing farmers with detailed, real-time information about their fields. AI algorithms can analyze the data collected by drones to create detailed maps and models of the farm, identifying areas of variability, and optimizing input application. However, high deployment cost of AI in Agriculture and lack of standardization in data collection is going to impede the overall demand for the market during the forecast period 2022-2032.

The key regions considered for the Global AI in Agriculture market study includes Asia Pacific, North America, Europe, Latin America, and Rest of the World. In 2023, North America was the largest regional market in terms of revenue owing to factors such as increasing adoption of AI technologies to enhance agricultural productivity across the region. AI-powered systems help farmers optimize the use of inputs such as water, fertilizers, and pesticides by providing real-time monitoring and analysis of crop conditions. Furthermore, the market in Asia Pacific, on the other hand, is expected to grow at the fastest rate over the forecast period.

Major market player included in this report are:

• Microsoft Corporation
• International Business Machines Corporation
• Granular Inc
• aWhere Inc
• Prospera Technologies Ltd
• Gamaya SA
• Easytosee Agtech, SL
• PrecisionHawk Inc
• Cainthus Corp
• Tule Technologies Inc

The detailed segments and sub-segment of the market are explained below:

By Application

• Weather Tracking
• Precision Farming
• Drone Analytics

By Deployment

• Cloud
• On-premise
• Hybrid

By Region:

• North America
• U.S.
• Canada
• Europe
• UK
• Germany
• France
• Spain
• Italy
• ROE
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• RoAPAC
• Latin America
• Brazil
• Mexico
• Middle East & Africa
• Saudi Arabia
• South Africa
• RoMEA

Years considered for the study are as follows:

• Historical year - 2022
• Base year - 2023
• Forecast period - 2024 to 2032

Key Takeaways:

• Market Estimates & Forecast for 10 years from 2022 to 2032.
• Annualized revenues and regional level analysis for each market segment.
• Detailed analysis of geographical landscape with Country level analysis of major regions.
• Competitive landscape with information on major players in the market.
• Analysis of key business strategies and recommendations on future market approach.
• Analysis of competitive structure of the market.
• Demand side and supply side analysis of the market

Table of Contents

Chapter 1.Global AI in Agriculture Market Definition and Research Assumptions

• 1.1.Research Objective
• 1.2.Market Definition
• 1.3.Research Assumptions
  • 1.3.1.Inclusion & Exclusion
  • 1.3.2.Limitations
  • 1.3.3.Supply Side Analysis
    • 1.3.3.1.Availability
    • 1.3.3.2.Infrastructure
    • 1.3.3.3.Regulatory Environment
    • 1.3.3.4.Market Competition
    • 1.3.3.5.Economic Viability (Consumer's Perspective)
  • 1.3.4.Demand Side Analysis
    • 1.3.4.1.Regulatory frameworks
    • 1.3.4.2.Technological Advancements
    • 1.3.4.3.Environmental Considerations
    • 1.3.4.4.Consumer Awareness & Acceptance
• 1.4.Estimation Methodology
• 1.5.Years Considered for the Study
• 1.6.Currency Conversion Rates

Chapter 2.Executive Summary

• 2.1.Global AI in Agriculture Market Size & Forecast (2022- 2032)
• 2.2.Regional Summary
• 2.3.Segmental Summary
  • 2.3.1.By Application
  • 2.3.2.By Deployment
• 2.4.Key Trends
• 2.5.Recession Impact
• 2.6.Analyst Recommendation & Conclusion

Chapter 3.Global AI in Agriculture Market Dynamics

• 3.1.Market Drivers
• 3.2.Market Challenges
• 3.3.Market Opportunities

Chapter 4.Global AI in Agriculture Market: Industry Analysis

• 4.1.Porter's 5 Force Model
  • 4.1.1.Bargaining Power of Suppliers
  • 4.1.2.Bargaining Power of Buyers
  • 4.1.3.Threat of New Entrants
  • 4.1.4.Threat of Substitutes
  • 4.1.5.Competitive Rivalry
  • 4.1.6.Futuristic Approach to Porter's 5 Force Model
  • 4.1.7.Porter's 5 Force Impact Analysis
• 4.2.PESTEL Analysis
  • 4.2.1.Political
  • 4.2.2.Economic
  • 4.2.3.Social
  • 4.2.4.Technological
  • 4.2.5.Environmental
  • 4.2.6.Legal
• 4.3.Top investment opportunity
• 4.4.Top winning strategies
• 4.5.Disruptive Trends
• 4.6.Industry Expert Perspective
• 4.7.Analyst Recommendation & Conclusion

Chapter 5.Global AI in Agriculture Market Size & Forecasts by Application 2022-2032

• 5.1.Weather Tracking
• 5.2.Precision Farming
• 5.3.Drone Analytics

Chapter 6.Global AI in Agriculture Market Size & Forecasts by Deployment 2022-2032

• 6.1.Cloud
• 6.2.On-premise
• 6.3.Hybrid

Chapter 7.Global AI in Agriculture Market Size & Forecasts by Region 2022-2032

• 7.1.North America AI in Agriculture Market
  • 7.1.1.U.S. AI in Agriculture Market
    • 7.1.1.1.Application breakdown size & forecasts, 2022-2032
    • 7.1.1.2.Deployment breakdown size & forecasts, 2022-2032
  • 7.1.2.Canada AI in Agriculture Market
• 7.2.Europe AI in Agriculture Market
  • 7.2.1.U.K. AI in Agriculture Market
  • 7.2.2.Germany AI in Agriculture Market
  • 7.2.3.France AI in Agriculture Market
  • 7.2.4.Spain AI in Agriculture Market
  • 7.2.5.Italy AI in Agriculture Market
  • 7.2.6.Rest of Europe AI in Agriculture Market
• 7.3.Asia-Pacific AI in Agriculture Market
  • 7.3.1.China AI in Agriculture Market
  • 7.3.2.India AI in Agriculture Market
  • 7.3.3.Japan AI in Agriculture Market
  • 7.3.4.Australia AI in Agriculture Market
  • 7.3.5.South Korea AI in Agriculture Market
  • 7.3.6.Rest of Asia Pacific AI in Agriculture Market
• 7.4.Latin America AI in Agriculture Market
  • 7.4.1.Brazil AI in Agriculture Market
  • 7.4.2.Mexico AI in Agriculture Market
  • 7.4.3.Rest of Latin America AI in Agriculture Market
• 7.5.Middle East & Africa AI in Agriculture Market
  • 7.5.1. Saudi Arabia AI in Agriculture Market
  • 7.5.2. South Africa AI in Agriculture Market
  • 7.5.3.Rest of Middle East & Africa AI in Agriculture Market

Chapter 8.Competitive Intelligence

• 8.1.Key Company SWOT Analysis
• 8.2.Top Market Strategies
• 8.3.Company Profiles
  • 8.3.1.Microsoft Corporation
    • 8.3.1.1.Key Information
    • 8.3.1.2.Overview
    • 8.3.1.3.Financial (Subject to Data Availability)
    • 8.3.1.4.Product Summary
    • 8.3.1.5.Market Strategies
  • 8.3.2.International Business Machines Corporation
  • 8.3.3.Granular Inc
  • 8.3.4.aWhere Inc
  • 8.3.5.Prospera Technologies Ltd
  • 8.3.6.Gamaya SA
  • 8.3.7.Easytosee Agtech, SL
  • 8.3.8.PrecisionHawk Inc
  • 8.3.9.Cainthus Corp
  • 8.3.10.Tule Technologies Inc

Chapter 9.Research Process

• 9.1.Research Process
  • 9.1.1.Data Mining
  • 9.1.2.Analysis
  • 9.1.3.Market Estimation
  • 9.1.4.Validation
  • 9.1.5.Publishing
• 9.2.Research Attributes