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% forecast to 2025.
Data Bridge Market research has recently released expansive research titled “Global AI in Agriculture Market 2020” guarantees you will remain better informed than your competition. In this global business document, market overview is given in terms of drivers, restraints, opportunities and challenges where each of this parameter is studied scrupulously. The report includes a range of inhibitors as well as key driving forces of the market which are analysed in both qualitative and quantitative approach so that readers and users get precise information and insights about this industry.
The study of AI in Agriculture report helps businesses to define their own strategies about the development in the existing product, modifications to consider for the future product, sales, marketing, promotion and distribution of the product in the existing and the new market. This report gives exhaustive study of new market entry, industry forecasting, investment calculation, future directions, opportunity identification, strategic analysis and planning, target market analysis, insights and innovation. This Study provides a deep insight into the activities of key competitors such as IBM, Microsoft Corporation, Descartes Labs, Deere & Company, Granular, aWhere, The Climate Corporation¸ Agribotix, and others.
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Major Industry Competitors: 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.
Revealing the Competitive scenario
In today’s competitive world you need to think one step ahead to chase your competitors, our research offers reviews about key players, major collaborations, merger & acquisitions along with trending innovation and business policies to present better insights to drive the business into right direction
Key Segmentation: 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)
North America (US, Canada, Mexico)
South America (Brazil, Argentina, rest of south America)
Asia and Pacific region (Japan, china, India, New Zealand, Vietnam, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, etc)
Middle east and Africa (UAE, Saudi Arabia, Oman, etc)
Europe (Germany, Italy, U.K, France, Spain, Netherlands, Belgium, Switzerland, Russia, etc)
Rapid Business Growth Factors
In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor that’s helping the market grow faster than usual is the tough competition.
What are the major market growth 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
Research strategies and tools used of AI in Agriculture Market:
This AI in Agriculture market research report helps the readers to know about the overall market scenario, strategy to further decide on this market project. It utilizes SWOT analysis, Porter’s Five Forces Analysis and PEST analysis.
Key Points of this Report:
The depth industry chain include analysis value chain analysis, porter five forces model analysis and cost structure analysis
The report covers North America and country-wise market of AI in Agriculture
It describes present situation, historical background and future forecast
Comprehensive data showing AI in Agriculture capacities, production, consumption, trade statistics, and prices in the recent years are provided
The report indicates a wealth of information on AI in Agriculture manufacturer
AI in Agriculture market forecast for next five years, including market volumes and prices is also provided
Raw Material Supply and Downstream Consumer Information is also included
Any other user’s requirements which is feasible for us
Some extract from Table of Contents
Overview of Global AI in Agriculture Market
AI in Agriculture Size (Sales Volume) Comparison by Type
AI in Agriculture Size (Consumption) and Market Share Comparison by Application
AI in Agriculture Size (Value) Comparison by Region
AI in Agriculture Sales, Revenue and Growth Rate
AI in Agriculture Competitive Situation and Trends
Strategic proposal for estimating availability of core business segments
Players/Suppliers, Sales Area
Analyze competitors, including all important parameters of AI in Agriculture
Global AI in Agriculture Manufacturing Cost Analysis
The most recent innovative headway and supply chain pattern mapping
Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe, MEA or Asia Pacific.
Table Of Contents Is Available [email protected] https://www.databridgemarketresearch.com/toc?dbmr=global-ai-agriculture-market&AK
Why Is Data Triangulation Important In Qualitative Research?
This 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. Triangulation is one method used while reviewing, synthesizing and interpreting field data. Data triangulation has been advocated as a methodological technique not only to enhance the validity of the research findings but also to achieve ‘completeness’ and ‘confirmation’ of data using multiple methods
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