According to the research report, the global generative AI in agriculture market size is expected to touch USD 1,287.84 million by 2032, from USD 138.06 million in 2022, growing with a significant CAGR of 25.02% from 2023 to 2032.
The generative AI in agriculture report offers a comprehensive study of the current state expected at the major drivers, market strategies, and key vendors’ growth. The report presents energetic visions to conclude and study the market size, market hopes, and competitive surroundings. The research also focuses on the important achievements of the market, research & development, and regional growth of the leading competitors operating in the market. The current trends of the global generative AI in agriculture in conjunction with the geographical landscape of this vertical have also been included in this report.
The report offers intricate dynamics about different aspects of the global generative AI in agriculture market, which aids companies operating in the market in making strategic development decisions. The study also elaborates on significant changes that are highly anticipated to configure growth of the global generative AI in agriculture during the forecast period. It also includes a key indicator assessment that highlights growth prospects of this market and estimates statistics related to growth of the market in terms of value (US$ Mn) and volume (tons).
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This study covers a detailed segmentation of the global generative AI in agriculture market, along with key information and a competition outlook. The report mentions company profiles of players that are currently dominating the global generative AI in agriculture market, wherein various developments, expansions, and winning strategies practiced and implemented by leading players have been presented in detail.
Key Players
- Google LLC
- Microsoft Corporation
- AGCO Corporation
- Deere & Company
- A.A.A Taranis Visual Ltd.
- AgEagle Aerial Systems Inc.
- Bayer AG
- Raven Industries Inc.
- Ag Leader Technology
- Trimble Inc.
- IBM Corporation
- Gamaya SA
- Granular Inc.
Market Segmentation
By Technology
- Machine Learning
- Computer Vision
- Predictive Analytics
By Application
- Precision Farming
- Agriculture Robots
- Livestock Monitoring
- Drone Analytics
- Labor Management
- Others
By Geography
- North America
- Europe
- Asia-Pacific
- Latin America
- Middle East and Africa
Research Methodology
The research methodology adopted by analysts for compiling the global generative AI in agriculture report is based on detailed primary as well as secondary research. With the help of in-depth insights of the market-affiliated information that is obtained and legitimated by market-admissible resources, analysts have offered riveting observations and authentic forecasts for the global market.
During the primary research phase, analysts interviewed market stakeholders, investors, brand managers, vice presidents, and sales and marketing managers. Based on data obtained through interviews of genuine resources, analysts have emphasized the changing scenario of the global market.
For secondary research, analysts scrutinized numerous annual report publications, white papers, market association publications, and company websites to obtain the necessary understanding of the global generative AI in agriculture market.
TABLE OF CONTENT
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology (Premium Insights)
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Generative AI in Agriculture Market
5.1. COVID-19 Landscape: Generative AI in Agriculture Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Generative AI in Agriculture Market, By Technology
8.1. Generative AI in Agriculture Market, by Technology, 2023-2032
8.1.1. Machine Learning
8.1.1.1. Market Revenue and Forecast (2020-2032)
8.1.2. Computer Vision
8.1.2.1. Market Revenue and Forecast (2020-2032)
8.1.3. Predictive Analytics
8.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 9. Global Generative AI in Agriculture Market, By Application
9.1. Generative AI in Agriculture Market, by Application, 2023-2032
9.1.1. Precision Farming
9.1.1.1. Market Revenue and Forecast (2020-2032)
9.1.2. Agriculture Robots
9.1.2.1. Market Revenue and Forecast (2020-2032)
9.1.3. Livestock Monitoring
9.1.3.1. Market Revenue and Forecast (2020-2032)
9.1.4. Drone Analytics
9.1.4.1. Market Revenue and Forecast (2020-2032)
9.1.5. Labor Management
9.1.5.1. Market Revenue and Forecast (2020-2032)
9.1.6. Others
9.1.6.1. Market Revenue and Forecast (2020-2032)
Chapter 10. Global Generative AI in Agriculture Market, Regional Estimates and Trend Forecast
10.1. North America
10.1.1. Market Revenue and Forecast, by Technology (2020-2032)
10.1.2. Market Revenue and Forecast, by Application (2020-2032)
10.1.3. U.S.
10.1.3.1. Market Revenue and Forecast, by Technology (2020-2032)
10.1.3.2. Market Revenue and Forecast, by Application (2020-2032)
10.1.4. Rest of North America
10.1.4.1. Market Revenue and Forecast, by Technology (2020-2032)
10.1.4.2. Market Revenue and Forecast, by Application (2020-2032)
10.2. Europe
10.2.1. Market Revenue and Forecast, by Technology (2020-2032)
10.2.2. Market Revenue and Forecast, by Application (2020-2032)
10.2.3. UK
10.2.3.1. Market Revenue and Forecast, by Technology (2020-2032)
10.2.3.2. Market Revenue and Forecast, by Application (2020-2032)
10.2.4. Germany
10.2.4.1. Market Revenue and Forecast, by Technology (2020-2032)
10.2.4.2. Market Revenue and Forecast, by Application (2020-2032)
10.2.5. France
10.2.5.1. Market Revenue and Forecast, by Technology (2020-2032)
10.2.5.2. Market Revenue and Forecast, by Application (2020-2032)
10.2.6. Rest of Europe
10.2.6.1. Market Revenue and Forecast, by Technology (2020-2032)
10.2.6.2. Market Revenue and Forecast, by Application (2020-2032)
10.3. APAC
10.3.1. Market Revenue and Forecast, by Technology (2020-2032)
10.3.2. Market Revenue and Forecast, by Application (2020-2032)
10.3.3. India
10.3.3.1. Market Revenue and Forecast, by Technology (2020-2032)
10.3.3.2. Market Revenue and Forecast, by Application (2020-2032)
10.3.4. China
10.3.4.1. Market Revenue and Forecast, by Technology (2020-2032)
10.3.4.2. Market Revenue and Forecast, by Application (2020-2032)
10.3.5. Japan
10.3.5.1. Market Revenue and Forecast, by Technology (2020-2032)
10.3.5.2. Market Revenue and Forecast, by Application (2020-2032)
10.3.6. Rest of APAC
10.3.6.1. Market Revenue and Forecast, by Technology (2020-2032)
10.3.6.2. Market Revenue and Forecast, by Application (2020-2032)
10.4. MEA
10.4.1. Market Revenue and Forecast, by Technology (2020-2032)
10.4.2. Market Revenue and Forecast, by Application (2020-2032)
10.4.3. GCC
10.4.3.1. Market Revenue and Forecast, by Technology (2020-2032)
10.4.3.2. Market Revenue and Forecast, by Application (2020-2032)
10.4.4. North Africa
10.4.4.1. Market Revenue and Forecast, by Technology (2020-2032)
10.4.4.2. Market Revenue and Forecast, by Application (2020-2032)
10.4.5. South Africa
10.4.5.1. Market Revenue and Forecast, by Technology (2020-2032)
10.4.5.2. Market Revenue and Forecast, by Application (2020-2032)
10.4.6. Rest of MEA
10.4.6.1. Market Revenue and Forecast, by Technology (2020-2032)
10.4.6.2. Market Revenue and Forecast, by Application (2020-2032)
10.5. Latin America
10.5.1. Market Revenue and Forecast, by Technology (2020-2032)
10.5.2. Market Revenue and Forecast, by Application (2020-2032)
10.5.3. Brazil
10.5.3.1. Market Revenue and Forecast, by Technology (2020-2032)
10.5.3.2. Market Revenue and Forecast, by Application (2020-2032)
10.5.4. Rest of LATAM
10.5.4.1. Market Revenue and Forecast, by Technology (2020-2032)
10.5.4.2. Market Revenue and Forecast, by Application (2020-2032)
Chapter 11. Company Profiles
11.1. Google LLC
11.1.1. Company Overview
11.1.2. Product Offerings
11.1.3. Financial Performance
11.1.4. Recent Initiatives
11.2. Microsoft Corporation
11.2.1. Company Overview
11.2.2. Product Offerings
11.2.3. Financial Performance
11.2.4. Recent Initiatives
11.3. AGCO Corporation
11.3.1. Company Overview
11.3.2. Product Offerings
11.3.3. Financial Performance
11.3.4. Recent Initiatives
11.4. Deere & Company
11.4.1. Company Overview
11.4.2. Product Offerings
11.4.3. Financial Performance
11.4.4. Recent Initiatives
11.5. A.A.A Taranis Visual L
11.5.1. Company Overview
11.5.2. Product Offerings
11.5.3. Financial Performance
11.5.4. Recent Initiatives
11.6. AgEagle Aerial Systems Inc.
11.6.1. Company Overview
11.6.2. Product Offerings
11.6.3. Financial Performance
11.6.4. Recent Initiatives
11.7. Bayer AG
11.7.1. Company Overview
11.7.2. Product Offerings
11.7.3. Financial Performance
11.7.4. Recent Initiatives
11.8. Raven Industries Inc.
11.8.1. Company Overview
11.8.2. Product Offerings
11.8.3. Financial Performance
11.8.4. Recent Initiatives
11.9. Ag Leader Technology
11.9.1. Company Overview
11.9.2. Product Offerings
11.9.3. Financial Performance
11.9.4. Recent Initiatives
11.10. Trimble Inc.
11.10.1. Company Overview
11.10.2. Product Offerings
11.10.3. Financial Performance
11.10.4. Recent Initiatives
Chapter 12. Research Methodology
12.1. Primary Research
12.2. Secondary Research
12.3. Assumptions
Chapter 13. Appendix
13.1. About Us
13.2. Glossary of Terms
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