AI in Agriculture Market Forecast and Trends
The global market of AI in
Agriculture anticipated to witness significant growth rate with a CAGR rise of
21.6% to reach USD 3,013 million by 2026, owing to growing demand for
agricultural production due to the growing population and increased adoption of
information management systems.
In addition advanced
technologies for improving crop productivity, increased crop productivity
through the introduction of in-depth learning techniques, and the initiatives
by governments around the world to promote the adoption of modern agricultural
techniques. The combination of the IoT’s and artificial intelligence
technologies such as machine learning, computer vision, and predictive
analytics further enables farmers to analyze real-time data on market weather,
temperature, soil moisture, plant health, and crop prices. The growing use and
growing need for better product yield were assessed as one of the key factors
that drive robots ' demand in agriculture.
AI in Agriculture Market: Overview
Artificial Intelligence (AI)
is a program-based technology that enables digital computers and robots to
perform real-time tasks through the use of human cognitive intelligence such as
speech recognition, visual perception, decision making, etc. In addition AI is
commonly used in agriculture industry for precision cultivation, crop
monitoring, crop analytics, etc.
Artificial Intelligence has
different applications in agriculture sector including rural automations,
facial recognition, computerized water systems and driverless tractors. Such
implementations are performed concerning an alternate kind of AI-dependent
sensors, GPS systems, radars, and other cutting edge contraptions. AI is
receiving a fascinating reaction from investors all around considering these
broad applications. Human-made reasoning is one of those critical innovations
in advanced agriculture which is being updated and conveyed for more
sustainable use of available assets.
demand for monitoring of livestock
Increasing demand for
livestock tracking is one of the key drivers of AI in the agricultural
industry. Livestock plays a key role in meeting the demand for meat, milk,
eggs, and wool. With changing lifestyles, growing population, and rising per
capita income levels, meat, eggs, and milk consumption are increasing globally.
In addition, to meet the demand for protein-rich foods, livestock health needs
to be regularly monitored. Through applying advanced AI technologies, such as
facial recognition for livestock and image classification combined with body
condition score and feeding habits, dairy farms can now track all behavioral
aspects of a herd individually.
adoption of IoT
Another major factor driving
AI in the agricultural market is the rising adoption of IoT. The IoT industry is
growing globally with the increasing use of mobile devices and cloud computing.
Different advantages provided by IoT, such as the ability to handle large
amounts of structured and unstructured data format, drive IoT demand in
monitoring of crops
Together with hyperspectral
imaging and 3D laser scanning, remote sensing techniques are crucial for
creating crop metrics through thousands of acres. It has the likelihood of
leading revolutionary change in the way farmland is observed by farmers from
both the perspective of time and effort. It system will also be used to track
crops during their entire lifecycle that includes reporting in the event of
techniques in irrigation and enabling farmers
Irrigation is one of the
processes involving human-intensive methods of agriculture. Machines trained on
historical weather patterns, soil quality and crop types to grow will automate
irrigation and increase overall yield production. With nearly 65-75% of the
world's freshwater used in irrigation, automation will help farmers handle
their water problems better.
cost & lack of standardization
High device and service
costs are a major factor hampering global artificial intelligence (AI)
development in the agricultural industry. Furthermore, the lack of
standardization limits market growth as data collection requirements are
lacking and the lack of data sharing is strong, and machine learning and
artificial intelligence and advanced algorithm design have progressed so
rapidly, but the collection of well-tagged, usable agricultural data is far
AI in Agriculture Market: Offering
Based on the global
Agriculture market, AI being offered fragmented into hardware, software &
services. During the forecast period, the Hardware segment market is expected
to rise at the highest CAGR. The growing demand for processors and data storage
devices for farm operations is the significant factor that is likely to
contribute to the hardware market's high growth rate in the coming years.
Followed by the hardware industry, the software provides significantly to the
global market growth rate. The software category is driven primarily by the
convergence of mobile devices with farming techniques, increased use of artificial
intelligence tools to boost farm productivity and increased demand for data
management systems in real-time.
AI in Agriculture Market: Technology
The market is categorized
according to technology into machine learning, computer vision, and predictive
analytics. The machine learning group is expected to continue to lead the
market throughout the forecast period, amongst these. Machine learning plays an
important role in AI in the agricultural market with a combination of agronomic
sciences and data technologies that are increasingly being adopted by farmers
and farmers across the globe. Increasing deployment of information technology
(IT) in agriculture applications such as field conditions management, crop
management, and livestock management pushes the AI machine learning segment
market in the agriculture industry considerably. Additionally, the use of
computer vision technology for agricultural applications, such as plant image
recognition and the increasing demand for continuous monitoring and analysis of
crop health, are the major factors contributing in the growth of the market for
computer vision-based AI solutions.
AI in Agriculture Market: Application
The market is segmented,
based on application, into agricultural robots, precision farming, drone
analytics, livestock monitoring, and others, including smart greenhouse
management, soil management, and fishery management. The segment of precision
farming is expected to account for a substantial market share during the
forecast period. It helps farmers minimize costs and effectively optimize
resources. Precision agriculture uses AI to capture, view and analyze digital
data. For example, GPS-equipped harvesters deploy artificial intelligence to
track harvest yields for field variability analysis, such as variations in
water, soil composition or fungus, to generate geo-referenced data this is due
to precision farming, which is gaining popularity among farmers as a result of
the growing need for optimum yield with limited resources available, resulting
in a cost-effective approach. In addition, the rapid use of IoT in the
agriculture sector leads to the growth of the precision agricultural market.
AI in Agriculture Market: Regional Insights
In the agricultural market,
North America is assessed as the dominant locale in worldwide AI. Market growth
is due to the high variety of trend-setting products and agricultural goods. In
addition, selecting the prescient review and remote monitoring innovation in
agriculture also contributes substantially to the growth of the industry.
Besides, higher technological knowledge promotes the development of the AI
market in agriculture. Moving on from North America, Asia-Pacific expected a
significant growth during the 2020-2026 forecast period. The significant
factors contributing to the growth of AI in APAC's agricultural sector include
increasing precision farming practices to increase crop yields, drone analytics
services, and growing farm robots ' adoption. Farmers in countries such as
Australia, China, Japan, and India are rapidly embracing technology for machine
learning, deep learning, computer vision and predictive analytics.
Artificial intelligence in Agriculture market is fragmented into large-sized companies, mid-sized & small-sized companies, and many start-ups that provide artificial intelligence in Agriculture industry. However, the companies that hold the majority share of artificial intelligence in Agriculture market are IBM Corporation, Microsoft Corporation, Bayer AG, Deere & Company, A.A.A. Taranis Visual Ltd, AgEagle Aerial Systems Inc., AGCO Corporation, Raven Industries, Ag Leader Technology, Trimble Inc., Google LLC, Gamaya SA, Granular Inc., PrecisionHawk, SAP and Other Prominent Players.
objective of the Study:
To analyze and forecast the global market
size of the AI in agriculture.
Analyzing careful market segmentation and
estimating value-based market size through region-based segmentation.
· The global AI market for agriculture is
divided into five regions: North America, Europe, Asia-Pacific, Middle East,
South America, and their leading countries.
· To outline, categorize and forecast the
global AI in the agricultural market based on the range of products,
technology, and applications linked to the region.
· To examine competitive developments in the
global AI market, such as technological advancement, services, and regulatory
To emphasize the impact study of market
dynamics variables such as users, constraints, opportunities, and challenges.
· Profiling key players strategically and
analyzing their market shares comprehensively, together with detailing the
competitive environment for market leaders.
of the Report
Historical data – 2016-2019
Base Year – 2019
Forecast – 2020 – 2026
Revenue in USD Million/Billion
U.S, Canada, Germany, UK, France, Italy,
Spain, Brazil, Mexico, Argentina, Japan, South Korea, China, India, UAE,
South Africa, Saudi Arabia
By Offering, By
Technology, By Application and region
IBM Corporation, Microsoft Corporation, Bayer AG, Deere &
Company, A.A.A. Taranis Visual Ltd, AgEagle Aerial Systems Inc., AGCO
Corporation, Raven Industries, Ag Leader Technology, Trimble Inc., Google
LLC, Gamaya SA, Granular Inc., PrecisionHawk, SAP and Other Prominent Players
Weather Tracking and Forecasting
Applications (Smart Greenhouse Application, Soil Management etc.)
Middle East & Africa
· June 2019 - At the 3rd AI
Good Global Summit in Geneva, XAG, a Chinese company, presented its innovative
solutions for integrating drones with AI and IoT technologies to achieve
precision farming and introduce revolutionary changes to the food system. XAG
drives AI-powered intelligent devices such as drones and sensors to develop
digital farming infrastructure in rural areas and enables precision agriculture
wherever it is required, which, for example, targets precisely pesticides,
seeds, fertilizers and water.
· April 2019 - Yara and IBM
Services have joined forces in innovating and selling digital agricultural
technologies that will help boost global food production. Yara and IBM will be
designing digital technologies that enable skilled and smallholder farmers to
improve farming practices in a sustainable manner to increase yields, crop
quality and profits. The collaboration will focus on all aspects of farm
optimization and apply AI, machine learning and in-field data to unlock new
insights for farmers, especially in the field of weather data, where weather
companies will provide real-time actionable recommendations for hyperlocal
weather forecasts tailored to the specific needs of individual fields / crops.
Questions Answer by the Report
· How will the market drivers, restraints, and
opportunities affect the market dynamics?
· What will be the market size in terms of
value and volume and market statistics with
a detailed classification?
· Which segment dominates the market or
region, and one will be the fastest-growing, and why?
· A comprehensive survey of the competitive
landscape and the market participant players
· Analysis of strategy adopted by the key
player and their impact on other players.
Scope for the Client
Client satisfaction is our first and last priority, and that’s why
BlueWeave Consulting offers customization according to Company’s specific
needs. The following customization options are available for the report:
· With five
additional company detail analysis
· Detailed segment