This specialized and expertise oriented industry research report scrutinizes the technical and commercial business outlook of the AI in agriculture industry. The report analyzes and declares the historical and current trends analysis of the AI in agriculture industry and subsequently recommends the projected trends anticipated to be observed in the AI in agriculture market during the upcoming years.
The AI in agriculture market report analyzes and notifies the industry statistics at the global as well as regional and country levels in order to acquire a thorough perspective of the entire AI in agriculture market. The historical and past insights are provided for FY 2016 to FY 2019 whereas projected trends are delivered for FY 2020 to FY 2026. The quantitative and numerical data is represented in terms of value (USD Million) from FY 2016 – 2026.
The quantitative data is further underlined and reinforced by comprehensive qualitative data which comprises various across-the-board market dynamics. The rationales which directly or indirectly impact the AI in agriculture industry are exemplified through parameters such as growth drivers, restraints, challenges, and opportunities among other impacting factors.
Throughout our research report, we have encompassed all the proven models and tools of industry analysis and extensively illustrated all the key business strategies and business models adopted in the AI in agriculture industry. The report provides an all-inclusive and detailed competitive landscape prevalent in the AI in agriculture market.
The report utilizes established industry analysis tools and models such as Porter’s Five Forces framework to analyze and recognize critical business strategies adopted by various stakeholders involved in the entire value chain of the AI in agriculture industry. The AI in agriculture market report additionally employs SWOT analysis and PESTLE analysis models for further in-depth analysis.
The report study further includes an in-depth analysis of industry players' market shares and provides an overview of leading players' market position in the AI in agriculture sector. Key strategic developments in the AI in agriculture market competitive landscape such as acquisitions & mergers, inaugurations of different products and services, partnerships & joint ventures, MoU agreements, VC & funding activities, R&D activities, and geographic expansion among other noteworthy activities by key players of the AI in agriculture market are appropriately highlighted in the report.
The AI in agriculture market research report delivers an acute valuation and taxonomy of the AI in agriculture industry by practically splitting the market on the basis of different types, category, and regions. Through the analysis of the historical and projected trends, all the segments and sub-segments were evaluated through the bottom-up approach, and different market sizes have been projected for FY 2020 to FY 2026. The regional segmentation of the AI in agriculture industry includes the complete classification of all the major continents including North America, Latin America, Europe, Asia Pacific, and Middle East & Africa. Further, country-wise data for the AI in agriculture industry is provided for the leading economies of the world.
Agriculture is the oldest and most important profession around the world. With the rising population, producing more crops in less land is very necessary. Farmers can produce healthier crops, track their soil condition and growing conditions and manage pests with the implementation of AI technologies. AI in agriculture helps the farmer by organizing farmers ' data; it helps with the workload and improves a wide range of related tasks. Rapid population growth is increasing demand for agricultural production. Innovative technologies have been used in recent years to increase the efficiency. Growing adoption of advanced technology, growing need for monitoring of livestock, increasing use of drones in agriculture, increasing demand for smart sensors, GPS tracker in agriculture will drive the AI in the agricultural sector.
High maintenance costs, huge investment in research and development may restraint market growth. However, the use of the AI bots that helps the farmer to work faster than human labors and to produce a large volume of crops may create sufficient opportunities in the market.
The AI in agriculture market is segmented based on technology, application, component, deployment, and region. On the basis of technology segmentation, the market is classified into machine learning, computer vision, and predictive analytics. By application segmentation, the market is divided into precision farming, livestock monitoring, drone analytics, agriculture robots, others. By component, the market is separated into hardware, software, AI-as-a-service, service. By deployment segmentation, the market is bifurcated into cloud, on-premise, and hybrid.
Some of the essential players operating in the AI in agriculture market, but not restricted to include Microsoft, Gamaya, Precision Hawk, Agribotix(A AgEagle Company), ec2ce, Descartes Labs, IBM, John Deere, The Climate Corporation, aWhere, Granular, Vineview, Taranis, DTN, Connecterra, Prospera, Cainthus, Resson, FarmBot, Vision Robotics, Trace Genomics, CropX, Harvest Croo, Autonomous Tractor Corporation, and others.
The taxonomy of the AI in agriculture industry by its scope and segmentation is as follows:
Global AI in agriculture Market: By Technology Segmentation Analysis
Global AI in agriculture Market: By Component Segmentation Analysis
Global AI in agriculture Market: By Application Segmentation Analysis
Global AI in agriculture Market: By Deployment Segmentation Analysis
Global AI in agriculture Market: Regional Segmentation Analysis
KEY REPORT POINTERS & HIGHLIGHTS:
Table of Content
Key Insights from Primary Research
Key Recommendations from Analysts
Market Share Analysis by Component Segment
Industry Major Market Players
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