New York, NY, Jan. 12, 2021 (GLOBE NEWSWIRE) — Facts and Factors have published a new research report titled “AI in Agriculture Market By Technology (Machine Learning, Computer Vision, Predictive Analytics), By Component (Hardware, Software, AI-as-a-Service, Service), By Application (Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots, Others), and By Deployment (Cloud, On-premise, and Hybrid): Global Industry Outlook, Market Size, Business Intelligence, Consumer Preferences, Statistical Surveys, Comprehensive Analysis, Historical Developments, Current Trends, and Forecasts, 2020–2026”.
According to the research study, the global AI in Agriculture Market was estimated at USD 750 million in 2019 and is expected to reach USD 2,400 million by 2026. The global AI in Agriculture Market is expected to grow at a compound annual growth rate (CAGR) of 20% from 2019 to 2026.
Farming and agriculture are the most important and oldest professions in the world. With the growing population, it is very important to produce more crops in less land and increase productivity. With the introduction of AI technologies, farmers can yield healthier crops, monitor their soil and growing conditions, and control pests. AI in agriculture helps the farmer by organizing data for farmers; it helps with the workload and enhances a wide range of the tasks which are related to agriculture and the entire food supply chain. Artificial Intelligence in agriculture is used for various applications such as driverless tractors, rural automation, computerized water system frameworks, facial acknowledgment, etc.
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- Market Size & Forecast by Revenue | 2020−2026
- Market Dynamics – Leading trends, growth drivers, restraints, and investment opportunities
- Market Segmentation – A detailed analysis by product, types, end-user, applications, segments, and geography
- Competitive Landscape – Top key vendors and other prominent vendors
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Rapid growth in the population increases the demand for agricultural products. In recent years, innovative technologies are used to enhance production. The increasing adoption of advanced technologies, growing need for livestock monitoring, increasing use of drones in agriculture, growing requirement for smart sensors, GPS tracker in agriculture may drive the AI in agriculture market. Moreover, in many countries, the government is also supporting modern agricultural techniques. These techniques including predictive analysis, machine learning, and computer vision that help the farmer to analyze real-time data of temperature, weather conditions, crop prices, soil moisture, and plant health which further propelling the market growth. In emerging countries, many private organizations are focusing on automation in the agriculture sector; thereby major farmers are focusing on automation in agriculture.
Furthermore, to improve crop quality robots and drones are becoming an important part of crop production. These factors are estimated to drive the market in the future years. However, the lack of standards in data sharing and data collection may restraint market growth. It requires a lot of money to buy robots, poor farmers cannot afford it. They prefer to farming in a traditional way. Moreover, the high cost of research and development, and high investment in maintenance may impede the market growth. On the other hand, the traditional farmers needed more labor to crop and keep the farm productive. AI bots help the farmer to perform faster work than human laborers and harvest crops at a high amount. It can accurately identify and eliminate weeds and reduce farm costs. These factors may create attractive opportunities for the farmer, in turn; it can propel the market growth.
Top Market Players
Top key players operating in the market are Gamaya, Precision Hawk, Microsoft, Agribotix(A AgEagle Company), IBM, John Deere, ec2ce, Descartes Labs, The Climate Corporation, Vineview, Taranis, aWhere, Granular, DTN, Resson, FarmBot, Connecterra, Prospera, Cainthus, Vision Robotics, Trace Genomics, CropX, Harvest Croo, Autonomous Tractor Corporation, and others.
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By technology segmentation, the computer vision category is expected to contribute the largest market share in the global AI in agriculture market. Computer vision technology helps the farmer to detect nutritional deficiency of the plant and monitor crop health.
On the basis of the component segment, the software category led the market in 2019 and it is anticipated to grow in the future years attributed to the growing use of AI software to improve the efficiency of the farm, and the increasing need for real-time data management systems.
By the application segment, in 2019, the precision farming category headed the market and it is anticipated to grow over the forecast period owing to its increasing need for maximum yield production with the growing population.
By geography, in 2019, North America dominated the market for AI in agriculture owing to increasing investment in R&D activities and the high adoption of the new technologies.
Browse the full “AI in Agriculture Market By Technology (Machine Learning, Computer Vision, Predictive Analytics), By Component (Hardware, Software, AI-as-a-Service, Service), By Application (Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots, Others), and By Deployment (Cloud, On-premise, and Hybrid): Global Industry Outlook, Market Size, Business Intelligence, Consumer Preferences, Statistical Surveys, Comprehensive Analysis, Historical Developments, Current Trends, and Forecasts, 2020–2026” report at https://www.fnfresearch.com/global-ai-in-agriculture-market-by-technology-machine-1122
On the basis of the deployment segment, the cloud category headed the market in 2019. The cloud-based platform supports access from any internet-connected device.
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, categories, 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 the Middle East & Africa. Further, country-wise data for the AI in agriculture industry is provided for the leading economies of the world.
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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 efficiency. The growing adoption of advanced technology, the growing need for monitoring of livestock, increasing use of drones in agriculture, increasing demand for smart sensors, GPS tracker in agriculture will drive 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 laborers and to produce a large volume of crops may create sufficient opportunities in the market.
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This report segments the AI in Agriculture market as follows:
Global AI in Agriculture Market: By Technology Segmentation Analysis
- Machine Learning
- Computer Vision
- Predictive Analytics
Global AI in Agriculture Market: By Component Segmentation Analysis
Global AI in Agriculture Market: By Application Segmentation Analysis
- Precision Farming
- Livestock Monitoring
- Drone Analytics
- Agriculture Robots
Global AI in Agriculture Market: By Deployment Segmentation Analysis
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