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AI in Agriculture Market Size, Share Global Analysis Report, 2021 – 2026
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
Industry Insights
[249+ Pages Report] According to the report published by Facts Factors, the global AI in agriculture market size was worth around USD 1,002.02 million in 2019 and is predicted to grow to around USD 3,984.5 million by 2026 with a compound annual growth rate (CAGR) of roughly 25.2% between 2020 and 2026. The report analyzes the global AI in agriculture market drivers, restraints/challenges, and the effect they have on the demands during the projection period. In addition, the report explores emerging opportunities in the AI in agriculture market.
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.
Key Insights from Primary Research
- According to the primary CXO’s of the global AI in agriculture market, the market is estimated to witness a substantial growth of nearly 20% over the forecast period.
- The market was valued at USD 750 million, in 2019 and is expected to be valued over USD 2, 400 million.
- 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 component segment, the software category led the market in 2019 and it is anticipated to grow in the future years attributed to growing use of AI software to improve efficiency of farm, and increasing need for real-time data management systems.
- By application segment, in 2019, 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.
Key Recommendations from Analysts
- Our analysts recommend that the North American region continues to dominate the global market for the AI in agriculture owing toincreasing investment in R&D activities and high adoption of the new technologies.
- On the basis of deployment segment, cloud category is anticipated to boost the market in the future years. Cloud-based platform supports the access from any internet-connected device.
- Due to the rapid adoption of the agricultural robots, precision farming, advanced technology, the Asia Pacific region is projected to boost the market.
- Due to robust growth in the use of AI in agriculture, the machine learning category is estimated to enhance the market growth during the period 2020-2026.
- Owing to the adoption of the new techniques including predictive analysis, machine leaning, and computer vision helps the farmer to analyze real-time data of temperature, weather conditions, and improves crop quality, these factors may create lucrative opportunities in the market.
Farming techniques that use artificial intelligence can help increase growth and quality. As a result, agribusiness companies are embracing artificial intelligence in the form of predictive analytics-based solutions. In the entire food supply chain, AI-based applications and technologies help manage pests, produce healthier crops, track the soil, and enhance agriculture-related tasks. Since it aids in the analysis of farm data, artificial intelligence is increasingly being used in the agriculture industry to increase harvest quality and accuracy.
Growing population, the implementation of data management systems and new, emerging analytics for improving crop productivity, and growing initiatives by global governments promoting the adoption of modern agricultural techniques have been some of the factors contributing to the growth of artificial intelligence in agriculture. The lack of knowledge of high-tech machine learning solutions in farms, on the other hand, is a major factor that could limit artificial intelligence's growth in the agriculture sector. Furthermore, expanding the use of Unmanned Aerial Vehicles (UAVs) over agricultural farms is expected to fuel artificial intelligence's development in the agriculture sector.
Furthermore, agriculture automation aids in the allocation of resources such as fertilizers and water, the determination of the best date for crop sowing, and the detection of weeds, all of which drive demand for artificial intelligence solutions. It also uses the historical long-term weather predictions, output data, commodity pricing estimates, and seed details, among other inputs, to help recommend how many seeds should be sown. The numerous advantages of artificial intelligence-based applications have prompted many tech giants and start-ups to build IoT-enabled devices for large-scale AI application deployment in agriculture.
Among components, the software category is considered to have a great market share. Prediction-based analytics and computer vision are used by AI-based software to increase crop productivity and yield. Furthermore, the software segment's growth is boosted by the growing penetration of predictive analytics-based software. Artificial intelligence solutions assist farmers in determining the best dates for planting crops, detecting crop diseases, monitoring crop yield, and calculating the amount of soil, water, fertilizer, and pesticides required. The benefits of AI-enabled software for proposed application areas such as drone analytics and precision farming are fuelling the AI in agriculture market's software segment's growth.
Over the forecasted era, the precision farming segment is estimated to hold a significant market share. Precision farming is among the most rapidly growing AI-enabled agricultural applications. It aids farmers in reducing costs and efficiently using capital. AI is used in precision farming to capture, translate, and analyze digital data. Farmers can tailor fertilizers or pesticides based on the results of the study and predictions. Agriculture robots powered by artificial intelligence incorporate field sensors, artificial intelligence, and data analytics, and can be used for a wide range of tasks. Since they can weed and hoe, these robots are effective harvesting systems. The agriculture robots segment is driven by the increasing implementation of AI in agriculture and advanced robotics innovations.
Because of the fastest-growing industrial automation industry and acceptance of artificial intelligence technologies, North America held a market share of more than 30% in 2020. North America is marked by increased population buying power, ongoing automation investments, significant IoT investments, and increased government emphasis mostly on AI equipment manufacturing. The presence of various agricultural technology providers implementing artificial intelligence solutions benefits the industry as well.
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, developing markets such as India and China are leveraging artificial intelligence solutions including remote monitoring technology and predictive analysis. Furthermore, in these economies, the growing demand for smart cities is prompting agribusiness companies to implement AI-based solutions and services.
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 |
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, 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
The global AI in Agriculture market is segmented as follows:
By Component Segment Analysis
- Hardware
- Software
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 Regional Segment Analysis
- North America
- U.S.
- Canada
- Europe
- UK
- France
- Germany
- Italy
- Spain
- 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 and Africa
- GCC Countries
- South Africa
- Rest of MEA
Table of Content
Industry Major Market Players
- 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
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