PUBLISHER: Global Market Insights Inc. | PRODUCT CODE: 1516086
PUBLISHER: Global Market Insights Inc. | PRODUCT CODE: 1516086
AI in Agriculture Market Size - By Component, Application, Technology & Forecast, 2024 - 2032
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
- 3.8.1 Growth drivers
- 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
