Summary

Global Smart Agriculture Market is valued approximately at USD 13.15 Million in 2020 and is anticipated to grow with a healthy growth rate of more than 10.6% over the forecast period 2021-2027. Smart agriculture refers to managing the farms by using technologies such as IoT, robotics, drones and Artificial intelligence to increase the quantity and quality of products while optimizing the human labor required by production. The global Smart Agriculture market is being driven by increase in agricultural productivity and rapid technological development across the world. Furthermore, rise in adoption of vertical farming and increase in public-private funding and partnership, will provide new opportunities for the global Smart Agriculture industry. For instance, according to Indian brand equity foundation, in India, the total food grain production in year 2020 was recorded at around 296.65 million tons which is up by about 11.44 million tons as compared to the production of year 2019 which was 285.21 million tons. Also, the government aims to purchase around 42.74 million tons from the central pool in year 2021. As a result, increase in agriculture activities owing to rising population will necessitate the integration of smart technologies in agriculture this propelling the Smart Agriculture industry forward in the future. However, lack of skilled workforce to operate smart agricultural technologies and high investment cost owing to deployment of expensive system in smart agriculture, may impede market growth over the forecast period of 2021-2027.

Asia Pacific, North America, Europe, Latin America, and Rest of the World are the key regions considered for the regional analysis of global smart agriculture market. Growing government initiatives and regulations to enhance the agriculture industry in the region makes North America the leading region across the world in terms of market share. Whereas Asia Pacific is also anticipated to exhibit the highest growth rate over the forecast period 2021-2027, due to the growing government support and rising awareness among cultivators in the region.
Major market player included in this report are:

Ag Leader Technology
AGCO Corporation
AgJunction, Inc.
AgEagle Aerial Systems Inc.
Autonomous Solutions, Inc.
Argus Control Systems Ltd.
BouMatic Robotic B.V.
CropMetrics
CLAAS KGaA mbH
CNH Industrial N.V

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Offering:
Hardware
Software
Service
By Application:
Livestock monitoring
Smart greenhouse application
Precision farming application, Others


By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019
Base year – 2020
Forecast period – 2021 to 2027.

Target Audience of the Global Smart Agriculture Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors

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Table of Contents

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2027 (USD Billion)
1.2.1. Smart Agriculture Market , by Region, 2019-2027 (USD Billion)
1.2.2. Smart Agriculture Market , by Offering , 2019-2027 (USD Billion)
1.2.3. Smart Agriculture Market , by Application, 2019-2027 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Smart Agriculture Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Smart Agriculture Market Dynamics
3.1. Smart Agriculture Market Impact Analysis (2019-2027)
3.1.1. Market Drivers
3.1.1.1. Sustainably increase agricultural productivity
3.1.1.2. Rapid technological development across the globe
3.1.2. Market Restraint
3.1.2.1. Lack of skilled workforce to operate smart agricultural technologies
3.1.2.2. High investment costs owing to deployment of expensive systems in smart agriculture
3.1.3. Market Opportunities
3.1.3.1. Rise in adoption of vertical farming
3.1.3.2. Increase in public–private partnership
Chapter 4. Global Smart Agriculture Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2027)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
Chapter 5. Global Smart Agriculture Market , by Offering
a. Market Snapshot
5.1. Global Smart Agriculture Market by Offering, Performance - Potential Analysis
5.2. Global Smart Agriculture Market Estimates & Forecasts by Offering 2018-2027 (USD Billion)
5.3. Smart Agriculture Market , Sub Segment Analysis
5.3.1. Hardware
5.3.2. Software
5.3.3. Service
Chapter 6. Global Smart Agriculture Market , by Application
b. Market Snapshot
6.1. Global Smart Agriculture Market by Application, Performance - Potential Analysis
6.2. Global Smart Agriculture Market Estimates & Forecasts by Application 2018-2027 (USD Billion)
6.3. Smart Agriculture Market , Sub Segment Analysis
6.3.1. Livestock monitoring.
6.3.2. Smart greenhouse application
6.3.3. Precision farming application
6.3.4. Others
Chapter 7. Global Smart Agriculture Market , Regional Analysis
7.1. Smart Agriculture Market , Regional Market Snapshot
7.2. North America Smart Agriculture Market
7.2.1. U.S. Smart Agriculture Market

7.2.1.1. Offering breakdown estimates & forecasts, 2018-2027
7.2.1.2. Application breakdown estimates & forecasts, 2018-2027
7.2.2. Canada Smart Agriculture Market
7.3. Europe Smart Agriculture Market Snapshot
7.3.1. U.K. Smart Agriculture Market
7.3.2. Germany Smart Agriculture Market
7.3.3. France Smart Agriculture Market
7.3.4. Spain Smart Agriculture Market
7.3.5. Italy Smart Agriculture Market
7.3.6. Rest of Europe Smart Agriculture Market
7.4. Asia-Pacific Smart Agriculture Market Snapshot
7.4.1. China Smart Agriculture Market
7.4.2. India Smart Agriculture Market
7.4.3. Japan Smart Agriculture Market
7.4.4. Australia Smart Agriculture Market
7.4.5. South Korea Smart Agriculture Market
7.4.6. Rest of Asia Pacific Smart Agriculture Market
7.5. Latin America Smart Agriculture Market Snapshot
7.5.1. Brazil Smart Agriculture Market
7.5.2. Mexico Smart Agriculture Market
7.6. Rest of The World Smart Agriculture Market
Chapter 8. Competitive Intelligence
8.1. Top Market Strategies
8.2. Company Profiles
8.2.1. Ag Leader Technology
8.2.1.1. Key Information
8.2.1.2. Overview
8.2.1.3. Financial (Subject to Data Availability)
8.2.1.4. Product Summary
8.2.1.5. Recent Developments
8.2.2. AGCO Corporation
8.2.3. AgJunction, Inc.
8.2.4. AgEagle Aerial Systems Inc.
8.2.5. Autonomous Solutions, Inc.
8.2.6. Argus Control Systems Ltd.
8.2.7. BouMatic Robotic B.V.
8.2.8. CropMetrics
8.2.9. CLAAS KGaA mbH
8.2.10. CNH Industrial N.V.

Chapter 9. Research Process
9.1. Research Process
9.1.1. Data Mining
9.1.2. Analysis
9.1.3. Market Estimation
9.1.4. Validation
9.1.5. Publishing
9.2. Research Attributes
9.3. Research Assumption