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AI in Agriculture Industry Research Report Provides an In-Depth Analysis on the Future Growth, Prospects and Trends Adopted by Top Competitors IBM, Microsoft Corporation, Descartes Labs, Deere & Company, Granular, aWhere, The Climate Corporation
AI in Agriculture Industry
Some of the major players in global AI in agriculture market are IBM, Microsoft Corporation, Descartes Labs, Deere & Company, Granular, aWhere, The Climate Corporation¸ Agribotix LLC, Tule Technologies, Prospera, Mavrx Inc., Cropx, Harvest Croo, Farmbot, Trace Genomics, Spensa Technologies Inc., Resson, Vision Robotics and Autonomous Tractor Corporation among others.
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The global AI in agriculture market is consolidated due to the presence of limited number of players concentrated in few countries. These major players have adopted various organic as well as inorganic growth strategies such as mergers & acquisitions, new product launches, expansions, agreements, joint ventures, partnerships, and others to strengthen their position in this market.
Global AI in Agriculture Market By Offering (Hardware, Software, Service, AI-As-A-Service), By Technology (Predictive Analytics, Machine Learning, Computer Vision), By Application (Livestock Monitoring, Precision Farming, Agriculture Robots, Livestock Monitoring, Drone Analytics), By Geography (North America, Europe, Asia-Pacific, Europe, South America, Middle East and Africa) Industry Trends and Forecast to 2025
The Global AI in Agriculture Market accounted for USD 432.5 million in 2017 and is projected to grow at a CAGR of 22.7% during the forecast period of 2018 to 2025. The upcoming market report contains data for historic years 2014, 2015, the base year of calculation is 2017 and the forecast period is 2018 to 2025.
Artificial Intelligence (AI) in agriculture is utilization of cognitive technologies that helps in learning, reasoning, understanding and interacting, which in result increases the efficiency. It offers various advantages such as Image recognition and insight, chatbots for farmers, help IoT achieve its maximum potential and determine the best options to maximize return on crops.
Points Which Are Focused In the Report
1. Industry Chain Suppliers of Global AI in Agriculture Market with Contact Information
2. The key findings and recommendations highlight crucial progressive industry trends in the Global AI in Agriculture Market, thereby allowing players to develop effective long term strategies
3. To analyze opportunities in the market for stakeholders by identifying the high-growth segments of the Global AI in Agriculture Market
4. To strategically profile key players and comprehensively analyze their market position in terms of ranking and core competencies, and detail the competitive landscape for market leaders
5. To understand the future outlook and prospects for Global AI in Agriculture Market analysis and forecast 2018-2025.
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On the basis of offering, the global AI in agriculture market is segmented into hardware, software, service, AI-As-A-Service.
Hardware is sub segmented into network, storage device and processor.
Software is sub segmented into AI Platform and AI Solution.
Service is sub segmented into support and maintenance and deployment and integration.
On the basis of technology, the global AI in agriculture market is segmented into predictive analytics, machine learning and computer vision.
On the basis of application, the global AI in agriculture market is segmented into livestock monitoring, precision farming, agriculture robots, livestock monitoring, drone analytics and others.
Precision farming is sub segmented into yield monitoring, crop scouting, weather tracking and forecasting, field mapping and irrigation management.
Others are sub segmented into smart greenhouse applications, fish farming application and soil management.
Soil Management is further sub segmented into nutrient monitoring and moisture monitoring.
On the basis of geography, the global AI in agriculture market report covers data points for 28 countries across multiple geographies such as North America, South America, Europe, Asia-Pacific and Middle East & Africa.
Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others.
Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and forecasted using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please Request an Analyst Call or can drop down your inquiry.
The key research methodology used by DBMR Research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, other data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Top to Bottom Analysis and Vendor Share Analysis. To know more about the research methodology, drop in an inquiry to speak to our industry experts.
Reasons to buy this report:
1. To get a comprehensive overview of the Global AI in Agriculture Market,
2. To gain information about the top players in this industry, their product portfolios, and their key strategies.
3. To gain insights about the major regional insights in which the Global AI in Agriculture Market is flourishing.
4. Recent industry trends and developments
5. Potential and niche segments/regions exhibiting promising growth
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About Data Bridge Market Research:
Data Bridge Market Research set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavors to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process.
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