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Artificial Intelligence in Agriculture Markets, 2030: Agricultural Robots, Precision Farming, Drone Analytics, & Livestock Monitoring

Research and Markets
·6-min read

Dublin, Feb. 10, 2021 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Agriculture Market Research Report: By Type, Technology, Application - Global Industry Analysis and Growth Forecast to 2030" report has been added to's offering.

The revenue generated in the global AI in agriculture market share is expected to increase to $11,200.1 million in 2030 from $671.6 million in 2019, at a 30.5% CAGR during 2020-2030

Service, based on type, is projected to be the faster-growing category during the forecast period. With an increasing number of farmers wanting to implement AI in their practices, the demand for training and equipment installation and maintenance services is also rising.

The highest CAGR, under the application segment of the AI in the agriculture market, would be experienced by the drone analytics division. With the surging requirement for high-quality crops by the continuously growing population, heavy investments are being put in agricultural drones. The demand for such devices is rising rapidly in China and the U.S., which is driving the advance of the drone analytics division.

The most important factor leading to the growth of the AI in the agriculture market is the increasing demand for food. The United Nations Department of Social and Economic Affairs (UN-DESA) claims that the worldwide population would rise from 7.7 billion currently to 8.6 billion by 2030. Additionally, with the changing consumption pattern of the populace, increasing disposable income, and high rate of urbanization, the demand for agricultural products is burgeoning. Due to this, the agrarian community is pursuing an increase in the farms' productivity, by leveraging AI.

Developing regions are expected to offer ample opportunities to the players in the AI in the agriculture market in the coming years. In emerging economies such as Brazil, India, and South Africa, the usage of AI in the agricultural domain is quite low; however, with the governments in these countries extending their support for the adoption of advanced technologies to grow crops, market players can hope to augment their revenue substantially here. For instance, the Maharashtra government began a partnership with the World Economic Forum in January 2019, to use drones for collecting insights on farmlands.

Software is expected to witness the fastest advance in the AI in the agriculture market, on the basis of product type, in the coming years. This is attributed to the fact that the use of AI for smart greenhouse management, soil management, and livestock monitoring necessitates advanced software to control and operate the complex devices and instruments. In 2019, machine learning was the largest technology category in the market, as farmers are rapidly adopting it to augment their yield, by combining data technologies with advanced agricultural science.

On a geographical basis, Europe and North America dominated the AI in the agriculture market in 2019, with a combined revenue share of around 70.0%. During the forecast period, the highest CAGR would be witnessed in Asia-Pacific (APAC), as the developing countries in the region, including China, India, Thailand, and Indonesia, are rapidly integrating agricultural robots, drone analytics, precision farming, and other advanced techniques to raise the productivity of farms.

Owing to the presence of numerous leading players, such as Microsoft Corporation, IBM Corporation, Deere & Company, Bayer AG, AgEagle Aerial Systems Inc., A.A.A Taranis Visual Ltd., Raven Industries, AGCO Corporation, Trimble Inc., Ag Leader Technology, Gamaya SA, Google LLC, and Granular Inc., the global AI in agriculture market is quite competitive.

Key Topics Covered:

Chapter 1. Research Background
1.1 Research Objectives
1.2 Market Definition
1.3 Research Scope
1.4 Key Stakeholders

Chapter 2. Research Methodology
2.1 Secondary Research
2.2 Primary Research
2.3 Market Size Estimation
2.4 Data Triangulation
2.5 Assumptions for the Study

Chapter 3. Executive Summary

Chapter 4. Introduction
4.1 Definition of Market Segments
4.1.1 By Type Product Hardware Software Service Professional Managed
4.1.2 By Technology Machine Learning Computer Vision Predictive Analytics
4.1.3 By Application Agricultural Robots Precision Farming Drone Analytics Livestock Monitoring Others
4.2 Value Chain Analysis
4.3 Market Dynamics
4.3.1 Trends Increasing use of robotics in agriculture Increasing use of smart sensors in agriculture
4.3.2 Drivers Growing demand for agricultural production Rising adoption of internet of things (IoT) Increasing demand for monitoring of livestock Increasing demand for drones in agricultural farms Impact analysis of drivers on market forecast
4.3.3 Restraints Lack of awareness and high cost of AI solutions Impact analysis of restraints on market forecast
4.3.4 Opportunities Growth opportunities from developing countries AI powered chatbots
4.4 Porter's Five Forces Analysis

Chapter 5. Global Market Size and Forecast
5.1 By Type
5.1.1 By Product
5.1.2 By Service
5.2 By Technology
5.3 By Application
5.4 By Region

Chapter 6. North America Market Size and Forecast

Chapter 7. Europe Market Size and Forecast

Chapter 8. APAC Market Size and Forecast

Chapter 9. LATAM Market Size and Forecast

Chapter 10. MEA Market Size and Forecast

Chapter 11. Competitive Landscape
11.1 Analysis of Key Players in the Market
11.2 List of Key Players and Their Offerings
11.3 Competitive Benchmarking of Key Players
11.4 Global Strategic Developments of Key Players
11.4.1 Mergers and Acquisitions
11.4.2 Product Launches
11.4.3 Partnerships

Chapter 12. Company Profiles

  • International Business Machines (IBM) Corporation

  • Microsoft Corporation

  • Bayer AG

  • Deere & Company

  • A.A.A Taranis Visual Ltd.

  • AgEagle Aerial Systems Inc.

  • AGCO Corporation

  • Raven Industries Inc.

  • Ag Leader Technology

  • Trimble Inc.

  • Google LLC

  • Gamaya SA

  • Granular Inc.

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