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

Market Analysis: Global AI in Agriculture Market

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.

Market Definition: Global AI in Agriculture Market

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.

Major Market Drivers:

Increasing adoption of new advanced technologies and IMS

Rising demand for agricultural production

Government support and initiatives for the adoption of modern agricultural techniques

Maximizing crop productivity along with the implementation of various techniques

Increasing use of drones in agricultural farms

Market Restraint:

High Cost involved during precise field data collection.

Market Segmentation: Global AI in Agriculture Market

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.

Competitive Landscape: Global AI in Agriculture Market

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.

Major Market Competitors: Global AI in Agriculture Market

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.

Research Methodology: Global AI in Agriculture Market

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.

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