Global Artificial Intelligence in Agriculture Market Expected to Grow in Value Over the Coming Years, with a CAGR of 38.3% -

DUBLIN--()--The "Artificial Intelligence in Agriculture Market Research Report: By Type, Technology, Application, Geographical Outlook - Global Industry Analysis and Growth Forecast to 2024" report has been added to's offering.

According to a report by the publisher, the global AI in agriculture market generated revenue of $584.0 million in 2018 and is predicted to witness a CAGR of 38.3% in the coming years.

As per the United Nations (UN) report, the world population, which is currently 7.7 billion, is predicted to reach 8.6 billion by 2030. This surge in the population is sure to increase the demand for agricultural products. This demand is primarily rising in countries including India, China, Brazil, and the U.S. because of the rapid urbanization, changing consumption habits of the populace, and increasing disposable income. With the increasing population, the current sources of agricultural production will not be enough, due to which there is a growing need for increasing the productivity. For this reason, the key agricultural product-producing countries are incorporating artificial intelligence (AI) into their agricultural practices.

AI, the imitation of human intelligence, empowers machines, especially computer systems, with capabilities such as self-correction, learning, and reasoning. In the agricultural sector, AI can be implemented for farming and gardening, in order to increase the precision and efficacy in maintaining, planting, and harvesting the crops. The major applications of AI in the agricultural sector include drone analytics, agricultural robots, livestock monitoring, and precision farming. Among these, the highest demand for AI was created by the precision farming application in 2018, and it is also going to be at the top in the coming years. This is because of the rising popularity of precision farming among the agrarian community, as there is a surging need for optimum yield using the limited available resources, which will eventually result in a reduction in the cost of crop production.

Among the above-mentioned applications, the demand for drone analytics in agricultural farms is projected to grow significantly in the near future. This is because drones that are enabled with AI are able to fly autonomously in an obstacle-filled environment. Moreover, drones are increasingly being used in the agricultural sector for assisting in irrigation schedules, estimating yield data, scanning soil health, and applying fertilizers. For instance, there is a rising demand for drones in the Xinjiang province of China for spraying pesticides in cotton fields, as by using drones, over 1,544 square miles of cotton fields can be sprayed at once, making the process time-efficient and improving the agricultural output. Because of all these advantages, several government initiatives are encouraging the adoption of drones for modernizing agricultural practices.

The demand for AI in the agricultural sector is also increasing due to the growing use of robotics in the field. Due to the increasing population and lack of skilled farm workers, the automation of agricultural processes has resulted in easier, modernized, and sophisticated farming practices via the deployment of robots. Furthermore, agricultural stakeholders are majorly focusing on refining the productivity using advanced farming practices and reducing the carbon footprint created by the entire agricultural process. Due to these factors, manufacturers in the robotics niche are coming up with offerings, which are equipped with AI, for operating in the dynamic and unstructured agricultural environment.

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

4.2 Value Chain Analysis

4.3 Market Dynamics

4.3.1 Trends

4.3.2 Drivers

4.3.3 Restraints

4.3.4 Opportunities

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

Chapter 12. Company Profiles

12.1 International Business Machines (IBM) Corporation

12.2 Microsoft Corporation

12.3 Bayer AG

12.4 Deere & Company

12.5 A.A.A Taranis Visual Ltd.

12.6 AgEagle Aerial Systems Inc.

12.7 AGCO Corporation

12.8 Raven Industries Inc.

12.9 Ag Leader Technology

12.1 Trimble Inc.

12.11 Google LLC

12.12 Gamaya SA

12.13 Granular Inc.

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Laura Wood, Senior Press Manager
For E.S.T Office Hours Call 1-917-300-0470
For U.S./CAN Toll Free Call 1-800-526-8630
For GMT Office Hours Call +353-1-416-8900

Laura Wood, Senior Press Manager
For E.S.T Office Hours Call 1-917-300-0470
For U.S./CAN Toll Free Call 1-800-526-8630
For GMT Office Hours Call +353-1-416-8900