Global Big Data Analytics in Agriculture Market, By Type (Capturing Data, Storing Data, Sharing Data, Analyzing Data, and Others), Application (Crop Production, Farm Equipment, Weather, and Chemicals), End users (Farmers, Agriculture Regulatory Bodies, Weather Forecast, Agrochemical and Farm Equipment Industries) – Industry Trends and Forecast to 2030.
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Data Bridge Market Research analyses that the global big data analytics in agriculture market which was USD 1.24 billion in 2022, is expected to reach USD 3.95 billion by 2030, and is expected to undergo a CAGR of 15.60% during the forecast period of 2023 to 2030. “Capturing data” dominates the type segment of the global big data analytics in agriculture market because data is the foundational element that fuels the entire analytics process. In addition to the insights on market scenarios such as market value, growth rate, segmentation, geographical coverage, and major players, the market reports curated by the Data Bridge Market Research also include in-depth expert analysis, geographically represented company-wise production and capacity, network layouts of distributors and partners, detailed and updated price trend analysis and deficit analysis of supply chain and demand.
**Segments**
- **Solution**: This segment includes software solutions specifically designed for big data analytics in agriculture, such as data management platforms, predictive analytics tools, and visualization software. These solutions help farmers and agribusinesses make data-driven decisions to improve crop yield, optimize resource allocation, and mitigate risks.
- **Deployment**: The deployment segment encompasses on-premise and cloud-based solutions for implementing big data analytics in agriculture. Cloud-based deployments offer scalability, cost-effectiveness, and accessibility benefits, while on-premise deployments provide greater control and customization options.
- **Application**: Within the application segment, key areas where big data analytics are applied in agriculture include precision farming, livestock monitoring, supply chain management, and weather forecasting. These applications enable stakeholders to enhance operational efficiency, sustainability, and profitability in the agricultural ecosystem.
**Market Players**
- **IBM**: IBM offers a range of big data analytics solutions tailored for the agriculture sector, including Watson Decision Platform for Agriculture. This platform leverages AI and machine learning to optimize farming operations, crop management, and supply chain logistics.
- **Microsoft**: Microsoft Azure provides cloud-based big data analytics services that can be customized for agricultural applications. The platform offers advanced analytics tools, IoT integration, and data visualization capabilities to empower farmers with actionable insights.
- **Oracle**: Oracle's Agriculture Data Cloud is designed to streamline data management and analytics for agribusinesses. The platform enables farmers to collect, store, and analyze large volumes of agricultural data for improved decision-making and operational efficiency.
- **SAS Institute**: SAS offers robust analytics solutions for the agriculture industry, including SAS Viya for Agriculture. This platform combines data visualization, machine learning, and predictive analytics to optimize crop production, risk management, and sustainability practices.
The global big data analytics in agriculture market is poised for significant growth as agricultural stakeholders increasingly recognize the value of data-driven insights in improving productivity, sustainability, and profitability. By leveraging advanced analytics tools and technologies, players in the market can unlock new opportunities for innovationThe global big data analytics in agriculture market is experiencing a transformative period driven by the growing adoption of advanced technologies to enhance productivity and sustainability in the agriculture sector. Key market players such as IBM, Microsoft, Oracle, and SAS Institute are at the forefront of providing cutting-edge solutions tailored for the specific needs of agricultural stakeholders. These companies offer a range of big data analytics tools and platforms designed to address challenges related to data management, decision-making, and operational efficiency in agriculture. By leveraging AI, machine learning, and predictive analytics, these solutions enable farmers and agribusinesses to gain valuable insights from large volumes of data, leading to improved crop yields, optimized resource allocation, and risk mitigation.
The solution segment of big data analytics in agriculture plays a crucial role in providing software solutions that cater to the unique requirements of the sector. These solutions encompass data management platforms, predictive analytics tools, and visualization software that enable stakeholders to make informed decisions based on data-driven insights. By utilizing these solutions, farmers can optimize their farming operations, enhance crop management practices, and improve supply chain logistics, ultimately resulting in increased efficiency and profitability within the agricultural ecosystem.
In terms of deployment, both on-premise and cloud-based solutions are being widely adopted in the agriculture sector for implementing big data analytics. Cloud-based deployments offer scalability, cost-effectiveness, and accessibility advantages, making it easier for farmers to access and analyze data from anywhere. On the other hand, on-premise deployments provide greater control and customization options for those who prioritize data security and privacy. Both deployment options have their strengths and are being utilized based on the specific needs and preferences of agricultural stakeholders.
The application segment of big data analytics in agriculture highlights key areas where these technologies are being effectively utilized to drive innovation and efficiency in the sector. Precision farming, livestock monitoring, supply chain management, and weather forecasting are among the key applications where big data analytics are making a significant impact. These applications enable stakeholders to optimize resource usage, improve decision-making processes, and enhance overall sustainability practices**Global Big Data Analytics in Agriculture Market**
- **Type**: The market is segmented by capturing data, storing data, sharing data, analyzing data, and others.
- **Application**: Key applications include crop production, farm equipment, weather, and chemicals.
- **End users**: Farmers, agriculture regulatory bodies, weather forecast, agrochemical, and farm equipment industries.
The global big data analytics in agriculture market is witnessing substantial growth driven by the increasing recognition among agricultural stakeholders of the benefits of data-driven insights in enhancing productivity, sustainability, and profitability. Key market players such as IBM, Microsoft, Oracle, and SAS Institute are leading the way by offering tailored solutions to address the unique needs of the agriculture sector. These companies provide advanced analytics tools and platforms infused with AI, machine learning, and predictive analytics capabilities to empower farmers and agribusinesses to extract valuable insights from vast amounts of data, ultimately leading to improved crop yields, optimized resource allocation, and better risk management.
The solution segment in the big data analytics in agriculture market holds significant importance in providing specialized software solutions that cater to the sector's specific requirements. Through data management platforms, predictive analytics tools, and visualization software, stakeholders can enhance decision-making processes based on data-driven insights. By utilizing these solutions, farmers can streamline farming operations, optimize crop management practices, and enhance supply chain logistics, resulting in increased operational efficiency and overall profitability within the agricultural ecosystem.
In terms of deployment, the adoption of both on-premise and cloud-based solutions is
The Big Data Analytics in Agriculture market research report displays a comprehensive study on production capacity, consumption, import and export for all the major regions across the globe. The target audience considered for this market study mainly consists of Key consulting companies & advisors, Large, medium, and small-sized enterprises, Venture capitalists, Value-added resellers (VARs), Third-party knowledge providers, Investment bankers, and Investors. This global market analysis report is the believable source for gaining the market research that will exponentially accelerate the business growth. The top notch Big Data Analytics in Agriculture market report is the best option to acquire a professional in-depth study on the current state for the market.
Table of Contents: Big Data Analytics in Agriculture Market
1 Introduction
2 Global Big Data Analytics in Agriculture Market Segmentation
3 Executive Summary
4 Premium Insight
5 Market Overview
6 Big Data Analytics in Agriculture Market, by Product Type
7 Big Data Analytics in Agriculture Market, by Modality
8 Big Data Analytics in Agriculture Market, by Type
9 Big Data Analytics in Agriculture Market, by Mode
10 Big Data Analytics in Agriculture Market, by End User
12 Big Data Analytics in Agriculture Market, by Geography
12 Big Data Analytics in Agriculture Market, Company Landscape
13 Swot Analysis
14 Company Profiles
Countries Studied:
- North America (Argentina, Brazil, copyright, Chile, Colombia, Mexico, Peru, United States, Rest of Americas)
- Europe (Austria, Belgium, Denmark, Finland, France, Germany, Italy, Netherlands, Norway, Poland, Russia, Spain, Sweden, Switzerland, United Kingdom, Rest of Europe)
- Middle-East and Africa (Egypt, Israel, Qatar, Saudi Arabia, South Africa, United Arab Emirates, Rest of MEA)
- Asia-Pacific (Australia, Bangladesh, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Sri Lanka, Thailand, Taiwan, Rest of Asia-Pacific)
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