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Global Smart Agriculture Market to reach USD 26.62 million by 2027.

Global Smart Agriculture Market Size study, by Offering (Hardware, Software, Service), by Application (Livestock monitoring. Smart greenhouse application, Precision farming application, Others), and Regional Forecasts 2021-2027

Product Code: OIRAL-31123871
Publish Date: 30-11-2021
Page: 200

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.
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:
By Application:
Livestock monitoring
Smart greenhouse application
Precision farming application, Others

By Region:
North America

Asia Pacific
South Korea
Latin America
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

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 Sustainably increase agricultural productivity Rapid technological development across the globe
3.1.2. Market Restraint Lack of skilled workforce to operate smart agricultural technologies High investment costs owing to deployment of expensive systems in smart agriculture
3.1.3. Market Opportunities Rise in adoption of vertical farming 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 Offering breakdown estimates & forecasts, 2018-2027 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 Key Information Overview Financial (Subject to Data Availability) Product Summary 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

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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