26-Apr-2021 | Facts and Factors
As per the Facts and Factors market research report, the global AI in Agriculture market was valued at USD 1,002.02 million in 2020 and is expected to grow to USD 3,984.5 million by the end of 2026, with a CAGR of about 25.2% between 2021 and 2026.
The surging penetration of the internet of things (IoT) in the agriculture industry, escalating growth for high crop yield, the emphasis on improved crop management, and the use of drones for spraying the pesticide are all positive factors influencing the sector.
The need for AI in agriculture is being driven by the world's rapidly rising population. The need for a green revolution powered by artificial intelligence, the Internet of Things (IoT), and data analytics is driven by limited arable land supply and the need for increasing agricultural productivity for food security. Predictive and recommendation analytics, recognizing plant diseases, detecting insect infestations, and soil monitoring are all areas where AI-enabled applications can help in the agriculture industry.
Browse the full “AI in Agriculture Market By Component (Hardware, and Software) By Technology (Predictive Analytics, Machine Learning & Deep Learning, and Computer Vision), By Application (Drone Analytics, Livestock Monitoring, Precision Farming, Agriculture Robots, and Others): Global Industry Perspective, Comprehensive Analysis, and Forecast, 2021 – 2026” report at https://www.fnfresearch.com/global-ai-in-agriculture-market-by-technology-machine-1122.
Artificial Intelligence in agriculture has a wide range of applications, including agricultural automatons, face recognition, computerized water system systems, and autonomous vehicle tractors. These applications are carried out in conjunction with a variety of AI-based sensors, GPS systems, radars, and other cutting-edge devices.
The factors propelling the global of the AI in agriculture market include increased implementation of innovative technological advancements and IMS, increased demand for agricultural production, and government initiatives to adopt modern agricultural techniques. Drone use on farms is becoming more common, which presents an opportunity. The high cost of collecting precise field data is limiting AI's use in agriculture. One of the problems facing the AI in agriculture sector is a lack of standardization.
According to technology, the predictive analytics segment in the AI in agriculture market is expected to grow at the fastest rate during the forecast period. This is due to the agrarian community's growing demand for advanced analytics solutions, which analyze actual weather conditions, moisture levels of soil, and plant health and provide deep information to improve production and crop quality.
The demand for agricultural products is projected to fall in FY 2020 as a result of COVID-19. This will also mean that technical advances in the industry would be less widely implemented, resulting in slower business growth. This is primarily due to the downturn in economic development, low spending on agricultural products, and the existing global lockout condition, which has resulted in food supply chain disruptions.
A main trend in the AI in agriculture market is the increased use of smart sensors in agriculture. Farmers can now precisely map their fields and track and apply crop treatment products only to the areas that need it due to sensor-based technology. Farmers are benefiting from the production of various operations explicit sensors, such as soil moisture sensors, airflow sensors, position sensors, and weather sensors, which are assisting them in effectively tracking and maximizing their yield as well as adapting to changing environmental factors.
Drones are being used more and more to track crop health and development. Drones are used to scan soil health, assist with irrigation schedules, estimate yield data, and apply fertilizers, among other things. The demand for drones in agriculture is rising year over year as a result of numerous government initiatives aimed at modernizing agricultural practices through the use of drones. For example, the state government of India partnered with the World Economic Forum in January 2019 to increase agricultural yield in the state by collecting data from agricultural farms using drones. As a result, the AI in agriculture market is being driven by the growing demand for drones in agricultural fields.
During the historical period, North America held the largest the AI in agriculture industry, and it is projected to continue to lead the global market in terms of revenue during the forecast period. This is due in large part to the early implementation of innovations like machine learning and computer vision for agricultural applications such as precision farming, livestock management, cultivation management, and soil management. Furthermore, with the growing adoption of technologies such as IoT in conjunction with computer vision in agriculture, the market is expected to expand positively over the forecast period.
Over the forecast period, the Asia Pacific market is projected to grow at the fastest pace. Its rise can be due to the growing use of artificial intelligence in agriculture. In the food industry, emerging economies such as India and China are leveraging artificial intelligence technologies such as remote monitoring tech and predictive analysis. Furthermore, in these economies, the growing demand for smart cities is prompting agribusiness companies to implement AI-based solutions and services.
Furthermore, some regional players are partnering with other leading players to provide services to regional consumers. Companies like IBM Corporation and Raven Industries Inc. are constantly partnering with other players in the agriculture industry to improve their offerings. Raven Industries Inc., for example, teamed up with Farmers Edge Inc., a manufacturer of precision digital solutions, to create a new precision agriculture technology in 2020.
As a result of the growing use of AI in agriculture to provide advanced analytical perspectives to farmers, the market in the area is expanding. In the coming years, APAC is expected to see the fastest growth in demand for AI-enabled agricultural solutions. The high widespread adoption of AI in the agriculture sector in significant countries like China, India, Japan, and Australia is largely responsible for the development.
Report Scope
Report Attribute |
Details |
Market Size in 2020 |
USD 1,002.02 million |
Projected Market Size in 2026 |
USD 3,984.5 million |
CAGR Growth Rate |
55.2% CAGR |
Base Year |
2020 |
Forecast Years |
2021-2026 |
Key Market Players |
GAMAYA, Deere & Company, Descartes Labs, Inc., ec2ce, Microsoft, IBM Corporation, Granular, Inc., VineView, Tule Technologies Inc., The Climate Corporation, Taranis, Prospera Technologies, PrecisionHawk, and others. |
Key Segment |
By Component, By Technology, By Application, By Region |
Major Regions Covered |
North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa |
Purchase Options |
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Some of the leading players in the global market include
AgEagle Aerial Systems Inc., GAMAYA, Deere & Company, Descartes Labs, Inc., ec2ce, Microsoft, IBM Corporation, Granular, Inc., VineView, Tule Technologies Inc., The Climate Corporation, Taranis, Prospera Technologies, PrecisionHawk, amongst others.
The global AI in Agriculture market is segmented as follows:
By Component Segment Analysis
By Technology Segment Analysis
- Predictive Analytics
- Machine Learning & Deep Learning
- Computer Vision
By Application Segment Analysis
- Drone Analytics
- Livestock Monitoring
- Precision Farming
- Agriculture Robots
- Others
By Region Segment Analysis
- North America
- Europe
- France
- The UK
- Spain
- Germany
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Southeast Asia
- Rest of Asia Pacific
- Latin America
- Brazil
- Mexico
- Rest of Latin America
- Middle East & Africa
- GCC Countries
- South Africa
- Rest of Middle East & Africa
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