AI In Agriculture Market by Offering (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-Aware Computing, Computer Vision), Technology (Machine Learning, Computer Vision, Predictive Analytics), Application (Precision Farming, Drone Analytics, Agriculture Robots, Livestock Monitoring), By Region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa); Trend Analysis, Competitive Market Share & Forecast, 2016-26
Published:
February 2020
Report Code:
BWC19397
Available Format:
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Pages:
185

ai in agriculture market bwc19397

Global 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.

Global 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.

Growth Drivers

Growing 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.

Rising 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 agriculture sector.

Health 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 abnormalities.

Automation 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.

Restraint

High 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 behind.

Global 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.

Global 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.


Global 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.

Global 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.



Competitive Landscape

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.

The 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 framework.

·         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.

Scope of the Report

Attribute

Details

Years Considered

Historical data – 2016-2019

Base Year – 2019

Forecast – 2020 – 2026

Facts Covered

Revenue in USD Million/Billion

Market Coverage

U.S, Canada, Germany, UK, France, Italy, Spain, Brazil, Mexico, Argentina, Japan, South Korea, China, India, UAE, South Africa, Saudi Arabia

Product/Service Segmentation

By Offering, By Technology, By Application and region

Key Players

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

By Offering

·         Hardware

o     Processor

o     Storage Device

o     Network

·         Software

o     AI Solutions

o     AI Platform

·         Services

o    Deployment & Integration

o    Support & Maintenance

By Technology

·         Machine Learning

·         Computer Vision

·         Predictive Analytics

By Application

·         Precision Farming

·         Yield Monitoring

o    Field Mapping

o    Crop Scouting

o    Weather Tracking and Forecasting

o    Irrigation Management

·         Livestock Monitoring

·         Drone Analytics

·         Agriculture Robots

·         Other Applications (Smart Greenhouse Application, Soil Management etc.)

By Region:  

·         North America

·         Europe

·         Asia Pacific

·         Latin America

·         Middle East & Africa

Recent Development

·   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.

Business 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.

Customization 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:

Additional Company Information

·  With five additional company detail analysis

·  Additional country analysis

·  Detailed segment analysis


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