AI in Agriculture Market by Technology (Machine Learning, Computer Vision, Predictive Analytics), Offering, Application (Precision Farming, Drone Analytics, Agriculture Robots, Livestock Monitoring), Offering, and Geography - Global Forecast to 2025
“AI in agriculture market anticipated to grow at a CAGR of 22.5% from 2017 to 2025”
The AI in agriculture market is estimated to be worth USD 2,628.5 million by 2025 from USD 518.7 million in 2017, growing at a CAGR of 22.5% between 2017 and 2025. The major factors driving the growth of the AI in agriculture market include the growing demand for agricultural production owing to the increasing population, rising adoption of information management systems and new, advanced technologies for improving crop productivity, increasing crop productivity by implementing deep learning techniques, and growing initiatives by worldwide governments supporting the adoption of modern agricultural techniques. The high cost of gathering precise field data is a major factor restraining the growth of the AI in agriculture market.
“AI in agriculture market for computer vision technology estimated to grow at the highest CAGR during the forecast period”
The market for computer vision is expected to grow at the highest CAGR during the forecast period. The increasing use of computer vision technology for agriculture applications, such as plant image recognition, and the rising demand for continuous monitoring and analyzing crop health are the major factors contributing to the growth of the market for AI solutions based on computer vision technology.
“Market for drone analytics applications expected to grow at the highest CAGR during the forecast period”
The market for drone analytics is expected to grow at the highest CAGR during the forecast period owing to its extensive use in diagnosing and mapping crop health and making real-time decisions. Further, favorable government mandates for using drones in agriculture field is expected to fuel the growth of the AI in agriculture market for the drone analytics application.
“AI in agriculture market in Asia Pacific expected to grow at the highest CAGR during the forecast period”
The increasing adoption of deep learning and computer vision technologies for agricultural applications is a major driving factor for the market growth in the APAC region. Further, the increasing awareness among growers regarding benefits associated with artificial intelligence technologies, such as computer vision, machine learning, and deep learning, for various farming applications is also a major factor contributing toward the growth of the AI in agriculture market in this region.
The breakup of primaries conducted during the study is depicted below:
• By Company Type: Tier 1 – 35 %, Tier 2 – 40%, and Tier 3 – 25%
• By Designation: C-Level Executives – 57%, Directors – 29%, and Others – 14%
• By Region: Americas – 40%, Europe – 30%, APAC – 20%, and RoW – 10%
The key players operating in the AI in agriculture market are IBM (US), John Deere (US), Microsoft (US), Agribotix (US), The Climate Corporation (US), ec2ce (Spain), Descartes Labs (US), Sky Squirrel Technologies (Canada), Mavrx (US), aWhere (US), Gamaya (Switzerland), Precision Hawk (US), Granular (US), Prospera (Israel), Cainthus (US), and Spensa Technologies (US).
This report covers the AI in agriculture market based on offering, technology, application, and region. The AI in agriculture market based on offering has been segmented into hardware, software, AI-as-a-Service, and Service. Based on applications, the AI in agriculture market has been segmented into precision farming, livestock monitoring, drone analytics, agriculture robots, and others. On the basis technology, the AI in agriculture market has been classified into machine learning, computer vision, and predictive analytics. On the basis of region, the market has been segmented into the Americas, Europe, Asia Pacific (APAC), and RoW.
Key Benefits of Buying the Report:
• This report includes the market statistics pertaining to offering, application, technology, and region, along with their respective market sizes.
• In-depth analysis of the value chain has been done to provide an in-depth insight into the AI in agriculture market.
• Major drivers, restraints, challenges, and opportunities pertaining to the AI in agriculture market have been detailed in this report.
• Illustrative segmentation, analysis, and forecast for the markets on the basis of offering, application, technology, and region have been conducted to provide an overall view of the AI in agriculture market.
• The report includes a detailed competitive leadership mapping, including key players and their in-depth analysis and ranking of the key players.
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