by The global artificial intelligence in agriculture market size was estimated to be around US$ 1.37 billion in 2022. It is projected to reach US$ 11.13 billion by 2032, indicating a CAGR of 23.3% from 2023 to 2032.
Key Takeaways:
- North America contributed more than 39% of revenue share in 2022.
- By component, the software segment is expected to dominate the market during the forecast period.
- By technology, the predictive analytics segment captured more than 47% of revenue share in 2022.
- By application, precision farming is expected to capture the largest market share over the forecast period.
Farming with artificial intelligence (AI) approaches leads to significantly higher productivity and crop yield. As a result, agricultural firms are increasingly adopting AI technologies to leverage predictive analytics for various solutions. AI-based tools and methods play a crucial role in pest management, promoting healthier crop production, monitoring soil conditions, and optimizing agricultural processes throughout the entire supply chain. Additionally, the analysis of farm data using artificial intelligence is proving instrumental in enhancing harvest quality and precision.
The growing demand for AI in the agriculture sector is primarily fueled by the world’s rapidly expanding population. With arable land becoming scarce, there is a pressing need for a green revolution powered by AI, the Internet of Things (IoT), and big data to ensure greater food production and food security. AI-enabled applications cater to several aspects of agriculture, including predictive and recommendation analytics, plant disease detection, pest infestation detection, and soil monitoring.
The market research report on the Artificial intelligence in agriculture market provides a comprehensive analysis of various key aspects. It includes the definition, classification, and application of Artificial intelligence in agriculture products. The report examines the development trends, competitive landscape, and industrial chain structure within the industry. Furthermore, it presents an overview of the industry, analyzes national policies and planning, and offers insights into the latest market dynamics and opportunities at a global level.
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Artificial Intelligence in Agriculture Market Report Scope
| Report Coverage | Details |
| Market Size in 2023 | USD 1.69 Billion |
| Market Size by 2032 | USD 11.13 Billion |
| Growth Rate from 2023 to 2032 | CAGR of 23.3% |
| Largest Market | North America |
| Base Year | 2022 |
| Forecast Period | 2023 to 2032 |
| Segments Covered | By Component, By Technology, and By Application |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Read More: Generative AI in Legal Market Size to Garner USD 781.55 Million by 2032
The report presents the volume and value-based market size for the base year 2022 and forecasts the market’s growth between 2023 and 2032. It estimates market numbers based on product form and application, providing size and forecast for each application segment in both global and regional markets.
Focusing on the global Artificial intelligence in agriculture market, the report highlights its status, future forecasts, growth opportunities, key market players, and key market regions such as the United States, Europe, and China. The study aims to present the development of the Artificial intelligence in agriculture market by considering factors like Year-on-Year (Y-o-Y) growth, in addition to Compound Annual Growth Rate (CAGR). This approach enables a better understanding of market certainty and the identification of lucrative opportunities.
Regarding production, the report investigates the capacity, production, value, ex-factory price, growth rate, and market share of major manufacturers, regions, and product types. On the consumption side, the report focuses on the regional consumption of Artificial intelligence in agriculture products across different countries and applications.
Buyers of the report gain access to verified market figures, including global market size in terms of revenue and volume. The report provides reliable estimations and calculations for global revenue and volume by product type from 2023 to 2032. It also includes accurate figures for production capacity and production by region during the same period.
The research includes product parameters, production processes, cost structures, and data classified by region, technology, and application. Furthermore, it conducts SWOT analysis and investment feasibility studies for new projects.
This in-depth research report offers valuable insights into the Artificial intelligence in agriculture market. It employs an objective and fair approach to analyze industry trends, supporting customer competition analysis, development planning, and investment decision-making. The project received support and assistance from technicians and marketing personnel across various links in the industry chain.
The competitive landscape section of the report provides detailed information on Artificial intelligence in agriculture market competitors. It includes company overviews, financials, revenue generation, market potential, research and development investments, new market initiatives, global presence, production sites, production capacities, strengths and weaknesses, product launches, product range, and application dominance. However, the data points provided only focus on the companies’ activities related to the Artificial intelligence in agriculture market.
Prominent players in the market are expected to face tough competition from new entrants. Key players are targeting acquisitions of startup companies to maintain their dominance. The report
Reasons to Purchase this Report:
- Comprehensive market segmentation analysis incorporating qualitative and quantitative research, considering the impact of economic and policy factors.
- In-depth regional and country-level analysis, examining the demand and supply dynamics that influence market growth.
- Market size in USD million and volume in million units provided for each segment and sub-segment.
- Detailed competitive landscape, including market share of major players, recent projects, and strategies implemented over the past five years.
- Comprehensive company profiles encompassing product offerings, key financial information, recent developments, SWOT analysis, and employed strategies by major market players.
Key Players
- Microsoft
- IBM Corporation
- Granular, Inc.
- AgEagle Aerial Systems Inc.
- The Climate Corporation
- Deere & Company
- Descartes Labs, Inc.
- Prospera Technologies
- GAMAYA
- aWhere Inc.
- Taranis
- ec2ce
- VineView
- PrecisionHawk
- Tule Technologies Inc.
Artificial Intelligence in Agriculture Market Segmentations
By Component
- Hardware
- Software
- Services
By Technology
- Machine Learning & Deep Learning
- Predictive Analytics
- Computer Vision
By Application
- Precision Farming
- Drone Analytics
- Agriculture Robots
- Livestock Monitoring
- Others
By Geography
- North America
- Europe
- Asia-Pacific
- Latin America
- Middle East and Africa
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 Artificial Intelligence in Agriculture Market
5.1. COVID-19 Landscape: Artificial Intelligence 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 Artificial Intelligence in Agriculture Market, By Component
8.1. Artificial Intelligence in Agriculture Market, by Component, 2023-2032
8.1.1 Hardware
8.1.1.1. Market Revenue and Forecast (2020-2032)
8.1.2. Software
8.1.2.1. Market Revenue and Forecast (2020-2032)
8.1.3. Services
8.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 9. Global Artificial Intelligence in Agriculture Market, By Technology
9.1. Artificial Intelligence in Agriculture Market, by Technology, 2023-2032
9.1.1. Machine Learning & Deep Learning
9.1.1.1. Market Revenue and Forecast (2020-2032)
9.1.2. Predictive Analytics
9.1.2.1. Market Revenue and Forecast (2020-2032)
9.1.3. Computer Vision
9.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 10. Global Artificial Intelligence in Agriculture Market, By Application
10.1. Artificial Intelligence in Agriculture Market, by Application, 2023-2032
10.1.1. Precision Farming
10.1.1.1. Market Revenue and Forecast (2020-2032)
10.1.2. Drone Analytics
10.1.2.1. Market Revenue and Forecast (2020-2032)
10.1.3. Agriculture Robots
10.1.3.1. Market Revenue and Forecast (2020-2032)
10.1.4. Livestock Monitoring
10.1.4.1. Market Revenue and Forecast (2020-2032)
10.1.5. Others
10.1.5.1. Market Revenue and Forecast (2020-2032)
Chapter 11. Global Artificial Intelligence in Agriculture Market, Regional Estimates and Trend Forecast
11.1. North America
11.1.1. Market Revenue and Forecast, by Component (2020-2032)
11.1.2. Market Revenue and Forecast, by Technology (2020-2032)
11.1.3. Market Revenue and Forecast, by Application (2020-2032)
11.1.4. U.S.
11.1.4.1. Market Revenue and Forecast, by Component (2020-2032)
11.1.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.1.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.1.5. Rest of North America
11.1.5.1. Market Revenue and Forecast, by Component (2020-2032)
11.1.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.1.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.2. Europe
11.2.1. Market Revenue and Forecast, by Component (2020-2032)
11.2.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.4. UK
11.2.4.1. Market Revenue and Forecast, by Component (2020-2032)
11.2.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.5. Germany
11.2.5.1. Market Revenue and Forecast, by Component (2020-2032)
11.2.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.6. France
11.2.6.1. Market Revenue and Forecast, by Component (2020-2032)
11.2.6.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.6.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.7. Rest of Europe
11.2.7.1. Market Revenue and Forecast, by Component (2020-2032)
11.2.7.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.7.3. Market Revenue and Forecast, by Application (2020-2032)
11.3. APAC
11.3.1. Market Revenue and Forecast, by Component (2020-2032)
11.3.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.4. India
11.3.4.1. Market Revenue and Forecast, by Component (2020-2032)
11.3.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.5. China
11.3.5.1. Market Revenue and Forecast, by Component (2020-2032)
11.3.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.6. Japan
11.3.6.1. Market Revenue and Forecast, by Component (2020-2032)
11.3.6.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.6.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.7. Rest of APAC
11.3.7.1. Market Revenue and Forecast, by Component (2020-2032)
11.3.7.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.7.3. Market Revenue and Forecast, by Application (2020-2032)
11.4. MEA
11.4.1. Market Revenue and Forecast, by Component (2020-2032)
11.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.4. GCC
11.4.4.1. Market Revenue and Forecast, by Component (2020-2032)
11.4.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.5. North Africa
11.4.5.1. Market Revenue and Forecast, by Component (2020-2032)
11.4.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.6. South Africa
11.4.6.1. Market Revenue and Forecast, by Component (2020-2032)
11.4.6.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.6.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.7. Rest of MEA
11.4.7.1. Market Revenue and Forecast, by Component (2020-2032)
11.4.7.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.7.3. Market Revenue and Forecast, by Application (2020-2032)
11.5. Latin America
11.5.1. Market Revenue and Forecast, by Component (2020-2032)
11.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.5.4. Brazil
11.5.4.1. Market Revenue and Forecast, by Component (2020-2032)
11.5.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.5.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.5.5. Rest of LATAM
11.5.5.1. Market Revenue and Forecast, by Component (2020-2032)
11.5.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.5.5.3. Market Revenue and Forecast, by Application (2020-2032)
Chapter 12. Company Profiles
12.1. Microsoft
12.1.1. Company Overview
12.1.2. Product Offerings
12.1.3. Financial Performance
12.1.4. Recent Initiatives
12.2. IBM Corporation
12.2.1. Company Overview
12.2.2. Product Offerings
12.2.3. Financial Performance
12.2.4. Recent Initiatives
12.3. Granular, Inc.
12.3.1. Company Overview
12.3.2. Product Offerings
12.3.3. Financial Performance
12.3.4. Recent Initiatives
12.4. AgEagle Aerial Systems Inc.
12.4.1. Company Overview
12.4.2. Product Offerings
12.4.3. Financial Performance
12.4.4. Recent Initiatives
12.5. The Climate Corporation
12.5.1. Company Overview
12.5.2. Product Offerings
12.5.3. Financial Performance
12.5.4. Recent Initiatives
12.6. Deere & Company
12.6.1. Company Overview
12.6.2. Product Offerings
12.6.3. Financial Performance
12.6.4. Recent Initiatives
12.7. Descartes Labs, Inc.
12.7.1. Company Overview
12.7.2. Product Offerings
12.7.3. Financial Performance
12.7.4. Recent Initiatives
12.8. Prospera Technologies
12.8.1. Company Overview
12.8.2. Product Offerings
12.8.3. Financial Performance
12.8.4. Recent Initiatives
12.9. GAMAYA
12.9.1. Company Overview
12.9.2. Product Offerings
12.9.3. Financial Performance
12.9.4. Recent Initiatives
12.10. aWhere Inc.
12.10.1. Company Overview
12.10.2. Product Offerings
12.10.3. Financial Performance
12.10.4. Recent Initiatives
Chapter 13. Research Methodology
13.1. Primary Research
13.2. Secondary Research
13.3. Assumptions
Chapter 14. Appendix
14.1. About Us
14.2. Glossary of Terms
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