According to HTF Market Intelligence, the Global AI in Agriculture market to witness a CAGR of 22.34% during the forecast period (2024-2030). The Latest Released AI in Agriculture Market Research assesses the future growth potential of the AI in Agriculture market and provides information and useful statistics on market structure and size.
This report aims to provide market intelligence and strategic insights to help decision-makers make sound investment decisions and identify potential gaps and growth opportunities. Additionally, the report identifies and analyses the changing dynamics and emerging trends along with the key drivers, challenges, opportunities and constraints in the AI in Agriculture market. The AI in Agriculture market size is estimated to reach by USD 5.17 Billion at a CAGR of 22.34% by 2030. The report includes historic market data from 2019 to 2023. The Current market value is pegged at USD 1.93 Billion.
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The Major Players Covered in this Report: IBM (United States), John Deere Company (United States), Intel (United States), Google (United States), Microsoft (United States), NVIDIA Corporation (United States), Sentient Technologies (United States), Numenta Inc. (United States), Agribotix (United States), The Climate Corporation (Subsidiary of Monsanto) (United States)
Definition:
The AI in Agriculture market refers to the use and application of Artificial Intelligence (AI) technologies and solutions in the agriculture sector. AI technologies are deployed to enhance various aspects of agricultural practices, including crop management, livestock monitoring, precision farming, and supply chain optimization. AI in Agriculture aims to improve efficiency, sustainability, and productivity in farming operations. AI is used for monitoring and managing crops by analyzing data from various sources such as drones, satellites, and sensors.
Market Trends:
- The adoption of precision agriculture techniques continued to grow, with AI enabling farmers to make data-driven decisions for precise planting, irrigation, and fertilization.
- The use of AI-powered remote sensing technologies, including drones and satellites, was increasing for crop monitoring, disease detection, and yield prediction.
- Robotics and AI-driven automation were gaining traction for tasks such as planting, weeding, and harvesting, reducing labor costs and improving efficiency.
Market Drivers:
- The need to feed a growing global population was a significant driver, prompting the adoption of AI to enhance crop yields and agricultural efficiency.
- Increasing emphasis on sustainable farming practices and environmental conservation drove the adoption of AI to reduce resource wastage and environmental impact.
- Advances in AI, machine learning, and IoT technologies provided new tools and solutions for agriculture.
Market Opportunities:
- Agricultural organizations and tech companies saw opportunities in monetizing data collected from farms, offering valuable insights to farmers and stakeholders.
- The global adoption of AI in Agriculture presented opportunities for technology providers to expand their offerings to different regions and emerging markets.
- There were opportunities for AI applications in livestock management, including health monitoring, breeding optimization, and feed management.
Market Challenges:
- Access to high-quality, reliable data and connectivity in rural areas remained a challenge, limiting the effectiveness of AI solutions.
- Some farmers faced challenges in terms of the initial cost of implementing AI technologies and the need for training.
- Concerns related to data privacy, ownership, and regulatory compliance added complexity to AI adoption in agriculture.
Market Restraints:
- Traditional farming practices and a lack of awareness or understanding of AI technology among some farmers posed a restraint to adoption.
- Small-scale farmers with limited resources may face challenges in adopting AI solutions due to financial constraints.
- Concerns about the security of sensitive farm data, including crop yield data and financial information, could hinder the adoption of AI in Agriculture.
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The titled segments and sub-sections of the market are illuminated below:
In-depth analysis of AI in Agriculture market segments by Types: Product, Hardware, Software, Service, Professional, Managed
Detailed analysis of AI in Agriculture market segments by Applications: Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots, Other
Major Key Players of the Market: IBM (United States), John Deere Company (United States), Intel (United States), Google (United States), Microsoft (United States), NVIDIA Corporation (United States), Sentient Technologies (United States), Numenta Inc. (United States), Agribotix (United States), The Climate Corporation (Subsidiary of Monsanto) (United States)
Geographically, the detailed analysis of consumption, revenue, market share, and growth rate of the following regions:
– The Middle East and Africa (South Africa, Saudi Arabia, UAE, Israel, Egypt, etc.)
– North America (United States, Mexico & Canada)
– South America (Brazil, Venezuela, Argentina, Ecuador, Peru, Colombia, etc.)
– Europe (Turkey, Spain, Turkey, Netherlands Denmark, Belgium, Switzerland, Germany, Russia UK, Italy, France, etc.)
– Asia-Pacific (Taiwan, Hong Kong, Singapore, Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia).
Objectives of the Report:
– -To carefully analyse and forecast the size of the AI in Agriculture market by value and volume.
– -To estimate the market shares of major segments of the AI in Agriculture market.
– -To showcase the development of the AI in Agriculture market in different parts of the world.
– -To analyse and study micro-markets in terms of their contributions to the AI in Agriculture market, their prospects, and individual growth trends.
– -To offer precise and useful details about factors affecting the growth of the AI in Agriculture market.
– -To provide a meticulous assessment of crucial business strategies used by leading companies operating in the AI in Agriculture market, which include research and development, collaborations, agreements, partnerships, acquisitions, mergers, new developments, and product launches.
Global AI in Agriculture Market Breakdown by Application (Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots, Other) by Type (Product, Hardware, Software, Service, Professional, Managed) by Technology (Machine Learning, Computer Vision, Predictive Analytics) and by Geography (North America, South America, Europe, Asia Pacific, MEA)
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Key takeaways from the AI in Agriculture market report:
– Detailed consideration of AI in Agriculture market-particular drivers, Trends, constraints, Restraints, Opportunities, and major micro markets.
– Comprehensive valuation of all prospects and threats in the
– In-depth study of industry strategies for growth of the AI in Agriculture market-leading players.
– AI in Agriculture market latest innovations and major procedures.
– Favourable dip inside Vigorous high-tech and market latest trends remarkable the Market.
– Conclusive study about the growth conspiracy of AI in Agriculture market for forthcoming years.
Major questions answered:
– What are influencing factors driving the demand for AI in Agriculture near future?
– What is the impact analysis of various factors in the Global AI in Agriculture market growth?
– What are the recent trends in the regional market and how successful they are?
– How feasible is AI in Agriculture market for long-term investment?
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Major highlights from Table of Contents:
AI in Agriculture Market Study Coverage:
– It includes major manufacturers, emerging player’s growth story, and major business segments of AI in Agriculture Market Dynamics, Size, and Growth Trend 2024-2030 market, years considered, and research objectives. Additionally, segmentation on the basis of the type of product, application, and technology.
– AI in Agriculture Market Dynamics, Size, and Growth Trend 2024-2030 Market Executive Summary: It gives a summary of overall studies, growth rate, available market, competitive landscape, market drivers, trends, and issues, and macroscopic indicators.
– AI in Agriculture Market Production by Region AI in Agriculture Market Profile of Manufacturers-players are studied on the basis of SWOT, their products, production, value, financials, and other vital factors.
Key Points Covered in AI in Agriculture Market Report:
– AI in Agriculture Overview, Definition and Classification Market drivers and barriers
– AI in Agriculture Market Competition by Manufacturers
– AI in Agriculture Capacity, Production, Revenue (Value) by Region (2024-2030)
– AI in Agriculture Supply (Production), Consumption, Export, Import by Region (2024-2030)
– AI in Agriculture Production, Revenue (Value), Price Trend by Type {Product, Hardware, Software, Service, Professional, Managed}
– AI in Agriculture Market Analysis by Application {Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots, Other}
– AI in Agriculture Manufacturers Profiles/Analysis AI in Agriculture Manufacturing Cost Analysis, Industrial/Supply Chain Analysis, Sourcing Strategy and Downstream Buyers, Marketing
– Strategy by Key Manufacturers/Players, Connected Distributors/Traders Standardization, Regulatory and collaborative initiatives, Industry road map and value chain Market Effect Factors Analysis.
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