The Artificial Intelligence In Agriculture Market was valued at USD 1.8 billion in 2023 and is expected to reach USD 12.8 billion by 2032, growing at a CAGR of 24.34% from 2024-2032.
The 2023 data highlights that AI technology adoption in precision farming is most prominent in North America and Europe, driven by advanced agri-tech infrastructure and supportive government policies. The use of AI-enabled drones and robotics in crop monitoring has surged, especially in countries like the U.S., China, and Brazil, enhancing field-level data collection. Investment in AI-driven smart irrigation systems has grown across arid regions, aiming to optimize water use. Furthermore, AI-based forecasting models have significantly improved crop yields, particularly in the cereal and fruit farming sectors. The report also unveils trends in AI integration across agribusiness value chains, increasing automation, and the role of machine learning in enhancing sustainability and reducing input costs.
The U.S. Artificial Intelligence in Agriculture Market was valued at USD 0.5 billion in 2023 and is expected to reach USD 3.4 billion by 2032, growing at a CAGR of 24.03% from 2024-2032. due to the rising demand for precision farming and increasing investments in agri-tech innovations. Strong government support and early adoption of AI tools like drones and smart irrigation systems are accelerating deployment. The market is expected to maintain robust growth, reaching substantial valuation by 2032 with a high CAGR.
Driver
AI is boosting productivity and resource efficiency in farming to meet growing global food demand.
The growing global population is putting pressure on food systems, pushing farmers to adopt advanced solutions to increase yield and efficiency. AI helps streamline crop management, irrigation, pest control, and resource utilization. Precision farming, supported by AI algorithms and data analytics, enables farmers to make real-time decisions and optimize output. Additionally, the rise in sensor-based technologies and drone surveillance has boosted AI integration across large-scale farms. Governments and private entities are investing significantly in digital agriculture initiatives, further driving adoption. These factors combined are creating a solid foundation for the sustained growth of AI in agriculture, especially in regions with advanced technological infrastructures.
Restraint
Lack of internet and tech access in rural areas limits AI adoption in agriculture.
While AI technologies offer numerous benefits, limited access to stable internet, power, and digital infrastructure in rural and remote areas hampers widespread adoption. Many developing countries still face gaps in connectivity and affordability, which restricts the deployment of cloud-based AI tools and real-time monitoring systems. Furthermore, the high initial cost of AI-enabled devices and a lack of technical expertise among farmers create barriers to entry. Without targeted efforts to bridge these infrastructure and education gaps, the market growth potential of AI in agriculture remains uneven, particularly in small and medium-scale farming operations in emerging economies.
Opportunity
Combining AI with IoT and satellite data opens new possibilities for real-time, data-driven farming.
The convergence of AI with IoT devices and satellite imaging presents vast opportunities to transform agricultural practices. Smart sensors combined with AI can monitor soil health, crop conditions, and weather patterns in real time. Satellite-based imagery analysis using machine learning allows for early detection of pest infestations and crop stress. These capabilities help optimize input use and boost sustainability. With falling hardware costs and expanding rural connectivity, the integration of such technologies is becoming more feasible. This creates an ideal landscape for companies and governments to implement AI-driven platforms and expand precision agriculture solutions at scale.
Challenge
Unclear data ownership and lack of standard protocols hinder trust and seamless AI integration.
One of the significant challenges in the AI in agriculture market is the lack of clear data ownership policies and standards. AI systems depend heavily on large datasets gathered from farms, drones, and IoT devices, raising concerns about who controls and benefits from this information. Many farmers are hesitant to share their operational data due to mistrust or unclear data usage terms. Moreover, the absence of universal standards for data collection, sharing, and interpretation hinders interoperability between systems from different vendors. This fragmentation not only reduces efficiency but also slows down innovation and cross-platform deployment, limiting the full potential of AI in agriculture.
By Component
In 2023, the software segment led artificial intelligence in the agriculture market and held a revenue share of 55%. The growing demand for precision farming and resource allocation is driving the growth of this segment. With the introduction of modern technologies in the agriculture sector, diverse data is being generated from different sources such as sensors, drones, and weather stations, etc. Using data obtained by sensors to understand crop health, soil conditions and weather patterns, software will also be key to interpret these data, allowing farmers to use the data to maximize crop yields and reduce waste.
The fastest CAGR during the forecast period is expected to be seen in the hardware segment. This segment is being propelled by the rising adoption of AI-driven sensors and drones. These hardware systems aid farmers in accurately tracking various dynamics pertinent to the agriculture industry. Soil monitoring using sensors involves placing devices that measure various factors, including soil moisture, temperature, pH levels, and nutrient content.
By Technology
In 2023, the machine learning & deep learning technology segment led the market and held the largest revenue share of more than 47%. Big Data technologies have allowed farmers to make precise decisions based on insights gained from the analysis of complex data sets. Machine learning and deep learning apply historical data, weather patterns, and soil conditions to predict crop yields, sense diseases, and measure when to plant. Additionally, the incorporation of these technologies within AI is projected to help provide groundbreaking and sophisticated solutions in agriculture, including predictive analytics platforms and AI-enabled farm management systems.
The computer vision segment is projected to offer the fastest CAGR during the forecast period. Computer vision technology uses these algorithms and other technology-driven tools and enables the analysis of visual data collected through cameras and sensors, detects problems, and allows quick action to prevent crop losses. It enables real-time data analytics, which helps in making timely decisions and better yield.
By Application
The precision farming segment held the largest share of the market in 2023. Just like C2C, precision farming is emerging as one of the most prominent uses of AI in the agriculture sector. It allows farmers to be more cost-efficient and control resources better. Precision agriculture, which is enabled by artificial intelligence, allows farmers to collect real-time data on soil conditions, temperature, and moisture levels using advanced technologies like GPS, drones, and satellite imaging so they can make data-driven decisions.
The agriculture robots segment is anticipated to register the fastest growth rate during the forecast period. Advances in robotics, computer vision, and machine learning also allow robots to carry out sophisticated crop monitoring and harvesting activities. As the demand for food production increases, the need for improved efficiency and productivity in farming processes and the development of robotics technologies will drive the growth of the segment.
North America dominated the market and accounted for the largest share of 36% in 2023. Some of the major growth contributors for this segment include strong technological innovation and hastened acceptance of AI solutions. This market is also gaining from increased penetration of technology, such as AI, machine learning, and data analytics, in almost every sector in the region.
The Asia-Pacific is expected to grow at the fastest CAGR from 2024 to 2032. This regional industry has expanded due to the growing population, its increasing agricultural productivity requirements, and major agro-tech enhancements supported by governments and other organizing bodies. With countries like China and India seeing population explosions, there is a growing demand for improved food production techniques in order to maintain food security.
The major key players along with their products are
IBM Corporation – Watson Decision Platform for Agriculture
Microsoft Corporation – Azure FarmBeats
Deere & Company – See & Spray
Trimble Inc. – Trimble Ag Software
AGCO Corporation – Fuse Smart Farming
BASF SE – xarvio Digital Farming Solutions
Corteva Agriscience – Granular Insights
Bayer AG (Climate LLC) – Climate FieldView
Raven Industries, Inc. – VSN (Visual Guidance System)
Prospera Technologies – Prospera Crop Monitoring
Taranis – Taranis Precision Scouting
Gamaya – Gamaya AI Crop Intelligence
PrecisionHawk – PrecisionAnalytics Agriculture
AgEagle Aerial Systems Inc. – MicaSense RedEdge Sensor
On Nov 14, 2024, IBM and Sustainable Energy for All launched AI-powered solutions to support sustainable urban development, including the Open Building Insights platform and an urban growth model. These tools aim to help policymakers address energy and infrastructure needs in developing regions.
On Aug 23, 2024, Bayer Crop Science is developing a new AWS-based data science platform with generative AI capabilities to create innovative agricultural solutions. The platform, built with Amazon SageMaker Studio and Bedrock, is in the discovery phase, with production models expected by 2025.
|
Report Attributes |
Details |
|
Market Size in 2023 |
US$ 1.8 Billion |
|
Market Size by 2032 |
US$ 12.8 Billion |
|
CAGR |
CAGR of 24.34 % From 2024 to 2032 |
|
Base Year |
2023 |
|
Forecast Period |
2024-2032 |
|
Historical Data |
2020-2022 |
|
Report Scope & Coverage |
Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
|
Key Segments |
• By Component (Hardware, Software, Services) |
|
Regional Analysis/Coverage |
North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]), Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia, Rest of Latin America) |
|
Company Profiles |
IBM Corporation, Microsoft Corporation, Deere & Company, Trimble Inc., AGCO Corporation, BASF SE, Corteva Agriscience, Bayer AG (Climate LLC), Raven Industries Inc., Descartes Labs, Prospera Technologies, Taranis, Gamaya, PrecisionHawk, AgEagle Aerial Systems Inc. |
Ans - The Artificial Intelligence In Agriculture Market was valued at USD 1.8 billion in 2023 and is expected to reach USD 12.8 billion by 2032
Ans- The CAGR of the Artificial Intelligence In Agriculture Market during the forecast period is 24.34% from 2024-2032.
Ans- Asia-Pacific is expected to register the fastest CAGR during the forecast period.
Ans- AI is boosting productivity and resource efficiency in farming to meet growing global food demand.
Ans- Unclear data ownership and lack of standard protocols hinder trust and seamless AI integration
Table of Content
1. Introduction
1.1 Market Definition
1.2 Scope (Inclusion and Exclusions)
1.3 Research Assumptions
2. Executive Summary
2.1 Market Overview
2.2 Regional Synopsis
2.3 Competitive Summary
3. Research Methodology
3.1 Top-Down Approach
3.2 Bottom-up Approach
3.3. Data Validation
3.4 Primary Interviews
4. Market Dynamics Impact Analysis
4.1 Market Driving Factors Analysis
4.1.1 Drivers
4.1.2 Restraints
4.1.3 Opportunities
4.1.4 Challenges
4.2 PESTLE Analysis
4.3 Porter’s Five Forces Model
5. Statistical Insights and Trends Reporting
5.1 AI Technology Adoption Rate in Precision Farming, by Region (2023)
5.2 Usage of AI-Enabled Drones and Robotics in Crop Monitoring, by Country (2023)
5.3 Investment in AI-Driven Smart Irrigation Systems, by Region (2023)
5.4 Yield Improvement from AI-Based Forecasting Models, by Crop Type (2023)
6. Competitive Landscape
6.1 List of Major Companies, By Region
6.2 Market Share Analysis, By Region
6.3 Product Benchmarking
6.3.1 Product specifications and features
6.3.2 Pricing
6.4 Strategic Initiatives
6.4.1 Marketing and promotional activities
6.4.2 Distribution and supply chain strategies
6.4.3 Expansion plans and new product launches
6.4.4 Strategic partnerships and collaborations
6.5 Technological Advancements
6.6 Market Positioning and Branding
7. Artificial Intelligence In Agriculture Market Segmentation, By Component
7.1 Chapter Overview
7.2 Software
7.2.1 Software Market Trends Analysis (2020-2032)
7.2.2 Software Market Size Estimates and Forecasts to 2032 (USD Billion)
7.3 Services
7.3.1 Services Market Trends Analysis (2020-2032)
7.3.2 Services Market Size Estimates and Forecasts to 2032 (USD Billion)
7.4 Hardware
7.4.1 Hardware Market Trends Analysis (2020-2032)
7.4.2 Hardware Market Size Estimates and Forecasts to 2032 (USD Billion)
8. Artificial Intelligence In Agriculture Market Segmentation, by Technology
8.1 Chapter Overview
8.2 Machine Learning & Deep Learning
8.2.1 Machine Learning & Deep Learning Market Trends Analysis (2020-2032)
8.2.2 Machine Learning & Deep Learning Market Size Estimates And Forecasts To 2032 (USD Billion)
8.3 Predictive Analytics
8.3.1 Predictive Analytics Market Trends Analysis (2020-2032)
8.3.2 Predictive Analytics Market Size Estimates and Forecasts to 2032 (USD Billion)
8.3 Computer Vision
8.3.1 Computer Vision Market Trends Analysis (2020-2032)
8.3.2 Computer Vision Market Size Estimates and Forecasts to 2032 (USD Billion)
9. Artificial Intelligence In Agriculture Market Segmentation, by Application
9.1 Chapter Overview
9.2 Precision Farming
9.2.1 Precision Farming Market Trends Analysis (2020-2032)
9.2.2 Precision Farming Market Size Estimates and Forecasts to 2032 (USD Billion)
9.3 Livestock Monitoring
9.3.1 Livestock Monitoring Market Trends Analysis (2020-2032)
9.3.2 Livestock Monitoring Market Size Estimates and Forecasts to 2032 (USD Billion)
9.4 Drone Analytics
9.4.1 Drone Analytics Market Trends Analysis (2020-2032)
9.4.2 Drone Analytics Market Size Estimates and Forecasts to 2032 (USD Billion)
9.5 Agriculture Robots
9.5.1 Agriculture Robots Market Trends Analysis (2020-2032)
9.5.2 Agriculture Robots Market Size Estimates and Forecasts to 2032 (USD Billion)
9.6 Labor Management
9.6.1 Labor Management Market Trends Analysis (2020-2032)
9.6.2 Labor Management Market Size Estimates and Forecasts to 2032 (USD Billion)
9.7 Others
9.7.1 Others Market Trends Analysis (2020-2032)
9.7.2 Others Market Size Estimates and Forecasts to 2032 (USD Billion)
10. Regional Analysis
10.1 Chapter Overview
10.2 North America
10.2.1 Trends Analysis
10.2.2 North America Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.2.3 North America Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.2.4 North America Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.2.5 North America Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Service (2020-2032) (USD Billion)
10.2.6 USA
10.2.6.1 USA Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.2.6.2 USA Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.2.6.3 USA Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.2.7 Canada
10.2.7.1 Canada Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.2.7.2 Canada Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.2.7.3 Canada Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.2.8 Mexico
10.2.8.1 Mexico Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.2.8.2 Mexico Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.2.8.3 Mexico Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3 Europe
10.3.1 Eastern Europe
10.3.1.1 Trends Analysis
10.3.1.2 Eastern Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.3.1.3 Eastern Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.1.4 Eastern Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.1.5 Eastern Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.1.6 Poland
10.3.1.6.1 Poland Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.1.6.2 Poland Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.1.6.3 Poland Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.1.7 Romania
10.3.1.7.1 Romania Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.1.7.2 Romania Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.1.7.3 Romania Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.1.8 Hungary
10.3.1.8.1 Hungary Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.1.8.2 Hungary Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.1.8.3 Hungary Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.1.9 Turkey
10.3.1.9.1 Turkey Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.1.9.2 Turkey Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.1.9.3 Turkey Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.1.10 Rest of Eastern Europe
10.3.1.10.1 Rest of Eastern Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.1.10.2 Rest of Eastern Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.1.10.3 Rest of Eastern Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2 Western Europe
10.3.2.1 Trends Analysis
10.3.2.2 Western Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.3.2.3 Western Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.4 Western Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.5 Western Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2.6 Germany
10.3.2.6.1 Germany Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.6.2 Germany Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.6.3 Germany Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2.7 France
10.3.2.7.1 France Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.7.2 France Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.7.3 France Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2.8 UK
10.3.2.8.1 UK Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.8.2 UK Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.8.3 UK Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2.9 Italy
10.3.2.9.1 Italy Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.9.2 Italy Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.9.3 Italy Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2.10 Spain
10.3.2.10.1 Spain Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.10.2 Spain Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.10.3 Spain Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2.11 Netherlands
10.3.2.11.1 Netherlands Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.11.2 Netherlands Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.11.3 Netherlands Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2.12 Switzerland
10.3.2.12.1 Switzerland Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.12.2 Switzerland Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.12.3 Switzerland Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2.13 Austria
10.3.2.13.1 Austria Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.13.2 Austria Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.13.3 Austria Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.3.2.14 Rest of Western Europe
10.3.2.14.1 Rest of Western Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.3.2.14.2 Rest of Western Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.3.2.14.3 Rest of Western Europe Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.4 Asia Pacific
10.4.1 Trends Analysis
10.4.2 Asia Pacific Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.4.3 Asia Pacific Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.4.4 Asia Pacific Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.4.5 Asia Pacific Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.4.6 China
10.4.6.1 China Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.4.6.2 China Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Display (2020-2032) (USD Billion)
10.4.6.3 China Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.4.7 India
10.4.7.1 India Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.4.7.2 India Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.4.7.3 India Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.4.8 Japan
10.4.8.1 Japan Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.4.8.2 Japan Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.4.8.3 Japan Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.4.9 South Korea
10.4.9.1 South Korea Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.4.9.2 South Korea Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.4.9.3 South Korea Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.4.10 Vietnam
10.4.10.1 Vietnam Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.4.10.2 Vietnam Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.4.10.3 Vietnam Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.4.11 Singapore
10.4.11.1 Singapore Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.4.11.2 Singapore Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.4.11.3 Singapore Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.4.12 Australia
10.4.12.1 Australia Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.4.12.2 Australia Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.4.12.3 Australia Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.4.13 Rest of Asia Pacific
10.4.13.1 Rest of Asia Pacific Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.4.13.2 Rest of Asia Pacific Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.4.13.3 Rest of Asia Pacific Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5 Middle East and Africa
10.5.1 Middle East
10.5.1.1 Trends Analysis
10.5.1.2 Middle East Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.5.1.3 Middle East Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.1.4 Middle East Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.1.5 Middle East Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5.1.6 UAE
10.5.1.6.1 UAE Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.1.6.2 UAE Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.1.6.3 UAE Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5.1.7 Egypt
10.5.1.7.1 Egypt Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.1.7.2 Egypt Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.1.7.3 Egypt Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5.1.8 Saudi Arabia
10.5.1.8.1 Saudi Arabia Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.1.8.2 Saudi Arabia Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.1.8.3 Saudi Arabia Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5.1.9 Qatar
10.5.1.9.1 Qatar Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.1.9.2 Qatar Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.1.9.3 Qatar Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5.1.10 Rest of Middle East
10.5.1.10.1 Rest of Middle East Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.1.10.2 Rest of Middle East Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.1.10.3 Rest of Middle East Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5.2 Africa
10.5.2.1 Trends Analysis
10.5.2.2 Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.5.2.3 Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.2.4 Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.2.5 Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5.2.6 South Africa
10.5.2.6.1 South Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.2.6.2 South Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.2.6.3 South Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5.2.7 Nigeria
10.5.2.7.1 Nigeria Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.2.7.2 Nigeria Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.2.7.3 Nigeria Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.5.2.8 Rest of Africa
10.5.2.8.1 Rest of Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.5.2.8.2 Rest of Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.5.2.8.3 Rest of Africa Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.6 Latin America
10.6.1 Trends Analysis
10.6.2 Latin America Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
10.6.3 Latin America Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.6.4 Latin America Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.6.5 Latin America Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.6.6 Brazil
10.6.6.1 Brazil Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.6.6.2 Brazil Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.6.6.3 Brazil Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.6.7 Argentina
10.6.7.1 Argentina Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.6.7.2 Argentina Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.6.7.3 Argentina Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.6.8 Colombia
10.6.8.1 Colombia Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.6.8.2 Colombia Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.6.8.3 Colombia Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
10.6.9 Rest of Latin America
10.6.9.1 Rest of Latin America Artificial Intelligence In Agriculture Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
10.6.9.2 Rest of Latin America Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Technology (2020-2032) (USD Billion)
10.6.9.3 Rest of Latin America Artificial Intelligence In Agriculture Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
11. Company Profiles
11.1 IBM Corporation
11.1.1 Company Overview
11.1.2 Financial
11.1.3 Products/ Services Offered
11.1.4 SWOT Analysis
11.2 Microsoft Corporation
11.2.1 Company Overview
11.2.2 Financial
11.2.3 Products/ Services Offered
11.2.4 SWOT Analysis
11.3 Deere & Company
11.3.1 Company Overview
11.3.2 Financial
11.3.3 Products/ Services Offered
11.3.4 SWOT Analysis
11.4 Trimble Inc.
11.4.1 Company Overview
11.4.2 Financial
11.4.3 Products/ Services Offered
11.4.4 SWOT Analysis
11.5 AGCO Corporation
11.5.1 Company Overview
11.5.2 Financial
11.5.3 Products/ Services Offered
11.5.4 SWOT Analysis
11.6 BASF SE
11.6.1 Company Overview
11.6.2 Financial
11.6.3 Products/ Services Offered
11.6.4 SWOT Analysis
11.7 Corteva Agriscience
11.7.1 Company Overview
11.7.2 Financial
11.7.3 Products/ Services Offered
11.7.4 SWOT Analysis
11.8 Bayer AG (Climate LLC)
11.8.1 Company Overview
11.8.2 Financial
11.8.3 Products/ Services Offered
11.8.4 SWOT Analysis
11.9 Raven Industries, Inc
11.9.1 Company Overview
11.9.2 Financial
11.9.3 Products/ Services Offered
11.9.4 SWOT Analysis
11.10 Descartes Labs
11.10.1 Company Overview
11.10.2 Financial
11.10.3 Products/ Services Offered
11.10.4 SWOT Analysis
12. Use Cases and Best Practices
13. Conclusion
An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
The 5 steps process:
Step 1: Secondary Research:
Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.
Step 2: Primary Research
When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data. This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.
We at SNS Insider have divided Primary Research into 2 parts.
Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.
This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
Step 5: Final QC/QA Process:
This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.
Key Segmentation:
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
Labor Management
Others
Request for Segment Customization as per your Business Requirement: Segment Customization Request
Regional Coverage:
North America
US
Canada
Mexico
Europe
Eastern Europe
Poland
Romania
Hungary
Turkey
Rest of Eastern Europe
Western Europe
Germany
France
UK
Italy
Spain
Netherlands
Switzerland
Austria
Rest of Western Europe
Asia Pacific
China
India
Japan
South Korea
Vietnam
Singapore
Australia
Rest of Asia Pacific
Middle East & Africa
Middle East
UAE
Egypt
Saudi Arabia
Qatar
Rest of Middle East
Africa
Nigeria
South Africa
Rest of Africa
Latin America
Brazil
Argentina
Colombia
Rest of Latin America
Request for Country Level Research Report: Country Level Customization Request
Available Customization
With the given market data, SNS Insider offers customization as per the company’s specific needs. The following customization options are available for the report:
Detailed Volume Analysis
Criss-Cross segment analysis (e.g. Product X Application)
Competitive Product Benchmarking
Geographic Analysis
Additional countries in any of the regions
Customized Data Representation
Detailed analysis and profiling of additional market players
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Industrial Services Market was valued at USD 35.47 billion in 2023 and is expected to reach USD 65.76 billion by 2032, growing at a CAGR of 7.15% by 2032.
Managed Domain Name System Market was valued at USD 0.96 billion in 2023 and will reach USD 4.30 billion by 2032, growing at a CAGR of 18.19% by 2032.
Semantic Knowledge Graphing Market was valued at USD 1.61 billion in 2023 and will reach USD 5.07 billion by 2032, growing at a CAGR of 13.64% by 2032.
The Payment Processing Solutions Market size was valued at USD 52.1 billion in 2023 and will grow to USD 139.7 billion by 2032 and grow at a CAGR of 11.6 % by 2032.
The Virtual Reality (VR) Content Creation Market size was valued at USD 4.80 billion in 2023 and is expected to reach USD 163.8 Billion by 2032, growing at a CAGR of 45.49% from 2024-2032.
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