Global Artificial Intelligence (AI) in Agriculture Market Size, Status and Forecast 2019-2025

SKU ID : QYR- 13268433

Publishing Date : 26-Mar-2019

No. of pages : 101

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  • Artificial Intelligence (AI) in Agriculture Market size is being driven by the growing adoption of the robots in agriculture. Increasing consumption and rising requirement of better yield of crops are estimated to be one of the major factors that is fueling the demand of robots in agriculture.
    Artificial Intelligence (AI) in Agriculture is mainly used for three applications: Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots, Others. And Precision Farming was the most widely used area which took up about 35.6% of the global total in 2018. However, in the future, Agriculture Robots will occupy more share.
    United States is the largest consumption countries of Artificial Intelligence (AI) in Agriculture in the world in the past few years and it will keep leader position in the next few years. North America market took up about 45.2% the global market in 2018, while Europe was about 24.7%.
    USA is now the key developers of Artificial Intelligence (AI) in Agriculture; most companies are in USA. Others are in Europe, China and India.
    In 2018, the global Artificial Intelligence (AI) in Agriculture market size was 330 million US$ and it is expected to reach 1550 million US$ by the end of 2025, with a CAGR of 25.0% during 2019-2025.

    This report focuses on the global Artificial Intelligence (AI) in Agriculture status, future forecast, growth opportunity, key market and key players. The study objectives are to present the Artificial Intelligence (AI) in Agriculture development in United States, Europe and China.

    The key players covered in this study
    IBM
    Intel
    Microsoft
    SAP
    Agribotix
    The Climate Corporation
    Mavrx
    aWhere
    Precision Hawk
    Granular
    Prospera Technologies
    Spensa Technologies
    Resson
    Vision Robotics
    Harvest Croo Robotics
    CropX
    John Deere
    Gamaya
    Cainthus

    Market segment by Type, the product can be split into
    Machine Learning
    Computer Vision
    Predictive Analytics

    Market segment by Application, split into
    Precision Farming
    Livestock Monitoring
    Drone Analytics
    Agriculture Robots
    Others

    Market segment by Regions/Countries, this report covers
    United States
    Europe
    China
    Japan
    Southeast Asia
    India
    Central & South America

    The study objectives of this report are:
    To analyze global Artificial Intelligence (AI) in Agriculture status, future forecast, growth opportunity, key market and key players.
    To present the Artificial Intelligence (AI) in Agriculture development in United States, Europe and China.
    To strategically profile the key players and comprehensively analyze their development plan and strategies.
    To define, describe and forecast the market by product type, market and key regions.

    In this study, the years considered to estimate the market size of Artificial Intelligence (AI) in Agriculture are as follows:
    History Year: 2014-2018
    Base Year: 2018
    Estimated Year: 2019
    Forecast Year 2019 to 2025
    For the data information by region, company, type and application, 2018 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

    Frequently Asked Questions



    This market study covers the global and regional market with an in-depth analysis of the overall growth prospects in the market. Furthermore, it sheds light on the comprehensive competitive landscape of the global market. The report further offers a dashboard overview of leading companies encompassing their successful marketing strategies, market contribution, recent developments in both historic and present contexts.

    • By product type
    • By End User/Applications
    • By Technology
    • By Region

    The report provides a detailed evaluation of the market by highlighting information on different aspects which include drivers, restraints, opportunities, and threats. This information can help stakeholders to make appropriate decisions before investing.
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