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Global Autonomous Crop Management Market to reach USD 4.82 billion by the end of 2029

Global Autonomous Crop Management Market Size study & Forecast, by Solution (Software, Services), by Application (Crop tracking and management, Weather Tracking & Forecasting, Irrigation Management, Labor and Resource Tracking, Others), by Deployment (On-premises, Cloud-based) and Regional Analysis, 2022-2029

Product Code: OIRAL-70306510
Publish Date: 8-02-2023
Page: 200

Global Autonomous Crop Management Market is valued at approximately USD 1.45 billion in 2021 and is anticipated to grow with a healthy growth rate of more than 16.2% over the forecast period 2022-2029. Autonomous crop management is a method of collecting agricultural data in order to improve crop growth, production, and development by utilizing advanced technologies and software. The increasing number of governments initiatives about the digitalization of the agricultural industry, rising concerns about climate change and food security, and high integration of artificial intelligence and machine learning are some chief factors that are fostering market growth worldwide.

The escalating global population is resulting in rising demand for food that is directly influencing market growth across the globe. As per the World Bank, the population around the world was recorded at 7.84 billion in 2021 an increase from 7.6 billion in 2018. Also, the United Nations Food and Agriculture Organization (FAO) estimated that 70% of world food production is anticipated to increase by 2050 globally. Accordingly, the rising population is boosting demand for food that impels the need for precision farming, thus, in turn, the autonomous crop management market is anticipated to expand at a substantial rate. Moreover, the increasing introduction of innovative products by the key market players, as well as the rising adoption of smart farming technologies are presenting various lucrative opportunities over the forecasting years. However, the dearth of technical expertise and high initial capital investment are hindering the market growth throughout the forecast period of 2022-2029.

The key regions considered for the Global Autonomous Crop Management Market study include Asia Pacific, North America, Europe, Latin America, and the Rest of the World. North America dominated the market in terms of revenue, owing to the increasing technological advancements, favorable government support, and rising infrastructure development. Whereas, the Asia Pacific is also expected to grow with the highest CAGR during the forecast period, owing to factors such as the rising number of government initiatives, development of the agriculture industry, and growing awareness among cultivators in the regional market space.

Major market players included in this report are:
Farm ERP
Raven Industries
Topcon Corporation
Microsoft Corporation (Farm Beats)
IBM (Regenerative Agriculture)

Recent Developments in the Market:
 In 2020, Trimble and Ecobot- a software provider entered into a collaborative agreement to offer a quick, sub-meter accurate wetland delineation. Under this collaboration, Trimble’s GNSS is intend to incorporate Ecobot’s Natural Resources Platform.

Global Autonomous Crop Management Market Report Scope:
Historical Data 2019-2020-2021
Base Year for Estimation 2021
Forecast period 2022-2029
Report Coverage Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Segments Covered Solution, Application, Deployment, Region
Regional Scope North America; Europe; Asia Pacific; Latin America; Rest of the World
Customization Scope Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

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 years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential 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 Solution:

By Application:
Crop tracking and management
Weather Tracking & Forecasting
Irrigation Management
Labor and Resource Tracking

By Deployment:

By Region:
North America
Asia Pacific
South Korea
Latin America
Rest of the World

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2029 (USD Billion)
1.2.1. Autonomous Crop Management Market, by Region, 2019-2029 (USD Billion)
1.2.2. Autonomous Crop Management Market, by Solution, 2019-2029 (USD Billion)
1.2.3. Autonomous Crop Management Market, by Application, 2019-2029 (USD Billion)
1.2.4. Autonomous Crop Management Market, by Deployment, 2019-2029 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Autonomous Crop Management 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 Autonomous Crop Management Market Dynamics
3.1. Autonomous Crop Management Market Impact Analysis (2019-2029)
3.1.1. Market Drivers Rising burden of food because of escalating global population Increasing number of governments initiatives about the digitalization of the agricultural industry
3.1.2. Market Challenges Dearth of technical expertise High initial capital investment
3.1.3. Market Opportunities Increasing introduction of innovative products by the key market players Rising adoption of smart farming technologies
Chapter 4. Global Autonomous Crop Management 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
4.2. Futuristic Approach to Porter’s 5 Force Model (2019-2029)
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. Industry Experts Prospective
4.7. Analyst Recommendation & Conclusion
Chapter 5. Risk Assessment: COVID-19 Impact
5.1. Assessment of the overall impact of COVID-19 on the industry
5.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global Autonomous Crop Management Market, by Solution
6.1. Market Snapshot
6.2. Global Autonomous Crop Management Market by Solution, Performance – Potential Analysis
6.3. Global Autonomous Crop Management Market Estimates & Forecasts by Solution 2019-2029 (USD Billion)
6.4. Autonomous Crop Management Market, Sub Segment Analysis
6.4.1. Software
6.4.2. Services
Chapter 7. Global Autonomous Crop Management Market, by Application
7.1. Market Snapshot
7.2. Global Autonomous Crop Management Market by Application, Performance – Potential Analysis
7.3. Global Autonomous Crop Management Market Estimates & Forecasts by Application 2019-2029 (USD Billion)
7.4. Autonomous Crop Management Market, Sub Segment Analysis
7.4.1. Crop tracking and management
7.4.2. Weather Tracking & Forecasting
7.4.3. Irrigation Management
7.4.4. Labor and Resource Tracking
7.4.5. Others
Chapter 8. Global Autonomous Crop Management Market, by Deployment
8.1. Market Snapshot
8.2. Global Autonomous Crop Management Market by Deployment, Performance – Potential Analysis
8.3. Global Autonomous Crop Management Market Estimates & Forecasts by Deployment 2019-2029 (USD Billion)
8.4. Autonomous Crop Management Market, Sub Segment Analysis
8.4.1. On-premises
8.4.2. Cloud-based
Chapter 9. Global Autonomous Crop Management Market, Regional Analysis
9.1. Autonomous Crop Management Market, Regional Market Snapshot
9.2. North America Autonomous Crop Management Market
9.2.1. U.S. Autonomous Crop Management Market Solution breakdown estimates & forecasts, 2019-2029 Application breakdown estimates & forecasts, 2019-2029 Deployment breakdown estimates & forecasts, 2019-2029
9.2.2. Canada Autonomous Crop Management Market
9.3. Europe Autonomous Crop Management Market Snapshot
9.3.1. U.K. Autonomous Crop Management Market
9.3.2. Germany Autonomous Crop Management Market
9.3.3. France Autonomous Crop Management Market
9.3.4. Spain Autonomous Crop Management Market
9.3.5. Italy Autonomous Crop Management Market
9.3.6. Rest of Europe Autonomous Crop Management Market
9.4. Asia-Pacific Autonomous Crop Management Market Snapshot
9.4.1. China Autonomous Crop Management Market
9.4.2. India Autonomous Crop Management Market
9.4.3. Japan Autonomous Crop Management Market
9.4.4. Australia Autonomous Crop Management Market
9.4.5. South Korea Autonomous Crop Management Market
9.4.6. Rest of Asia Pacific Autonomous Crop Management Market
9.5. Latin America Autonomous Crop Management Market Snapshot
9.5.1. Brazil Autonomous Crop Management Market
9.5.2. Mexico Autonomous Crop Management Market
9.5.3. Rest of Latin America Autonomous Crop Management Market
9.6. Rest of The World Autonomous Crop Management Market

Chapter 10. Competitive Intelligence
10.1. Top Market Strategies
10.2. Company Profiles
10.2.1. Trimble Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
10.2.2. Farm ERP
10.2.3. Conservis
10.2.4. Raven Industries
10.2.5. Topcon Corporation
10.2.6. Proagrica
10.2.7. Cropin
10.2.8. CropX
10.2.9. Microsoft Corporation (Farm Beats)
10.2.10. IBM (Regenerative Agriculture)
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. 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:
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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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