"Big Data Analytics in Agriculture Market Report helps businesses quickly gain essential data and analysis, combining primary and secondary research to offer the best market insights. It provides a comprehensive view of industrial trends, market forces, and growth rates.
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.
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Gathering data from various sources in agriculture, such as sensors, satellites, drones, weather stations, and farm equipment. This data can include information about soil conditions, weather patterns, crop growth, livestock health, and more. Combining and integrating data from multiple sources and formats into a unified and structured dataset for analysis. This may involve data cleansing and transformation to ensure data quality. Big data analytics in agriculture aims to enhance productivity, reduce costs, increase sustainability, and contribute to global food security by harnessing the potential of data to make more informed and precise decisions throughout the agricultural value chain.
**Segments**
- **Technology:** Big data analytics in agriculture relies on various technologies such as IoT sensors, satellite imagery, and drones to collect data related to crop health, soil conditions, weather patterns, and more. These technologies enable farmers to make data-driven decisions to optimize crop yields and resource usage.
- **Application:** The application of big data analytics in agriculture is vast, including crop monitoring, precision farming, livestock management, supply chain optimization, and predictive analytics. By analyzing data from different sources, farmers can improve operational efficiency and sustainability.
- **End-User:** The end-users of big data analytics in agriculture include farmers, agricultural cooperatives, food manufacturers, and agricultural researchers. Each end-user segment leverages big data analytics to address specific challenges and enhance productivity in their respective fields.
**Market Players**
- **IBM Corporation:** IBM offers various big data analytics solutions for agriculture, such as IBM Food Trust and Watson Decision Platform for Agriculture. These platforms help farmers and stakeholders in the food supply chain improve transparency, traceability, and decision-making.
- **Deere & Company:** Deere & Company, known for its agriculture machinery, also provides data-driven solutions through its Precision Ag Technology. This technology integrates machine data with agronomic insights to enable precision farming practices and optimize crop management.
- **Microsoft Corporation:** Microsoft's Azure FarmBeats is a platform that utilizes AI and IoT technologies for data-driven agriculture. It enables farmers to gather insights from data collected by sensors and drones, leading to optimized farming practices and increased yields.
- **AGCO Corporation:** AGCO offers Precision Planting technology, which utilizes big data analytics to enhance planting decisions and improve crop performance. By analyzing data on seed placement and soil conditions, farmers can achieve better crop outcomes.
The global big data analytics in agriculture market is expected to witness significant growth in the coming years, driven by the increasing adoption of digital technologies in the agricultural sector. Factors such as the growing demand for sustainable farming practices, the need for improved cropThe global big data analytics in agriculture market is poised for substantial growth in the foreseeable future, propelled by the escalating integration of digital technologies within the agricultural domain. One of the primary drivers of this growth is the surging demand for sustainable farming practices. With the global population on the rise and natural resources depleting, there is an increasing emphasis on sustainable agriculture to ensure food security for present and future generations. Big data analytics play a crucial role in helping farmers optimize their resource usage, minimize waste, and maximize productivity, all of which are essential components of sustainable farming practices.
Moreover, the need for improved crop monitoring and management is also fueling the adoption of big data analytics in agriculture. By leveraging advanced technologies such as IoT sensors, satellite imagery, and drones, farmers can gather real-time data on various aspects of their crops, including health, growth patterns, and potential threats. This data-driven approach enables farmers to make informed decisions regarding irrigation, fertilization, pest control, and other critical aspects of crop management. As a result, farmers can optimize their yields, reduce operational costs, and mitigate risks associated with crop failures.
Furthermore, big data analytics in agriculture provide valuable insights into market trends and consumer preferences, allowing farmers, agricultural cooperatives, and food manufacturers to tailor their production strategies accordingly. By analyzing data on supply chain dynamics, pricing trends, and demand forecasts, stakeholders in the agricultural sector can optimize their operations, minimize wastage, and enhance profitability. Additionally, predictive analytics tools enable stakeholders to anticipate market fluctuations and make proactive decisions to stay ahead of the competition.
In terms of market players, key companies such as IBM Corporation, Deere & Company, Microsoft Corporation, and AGCO Corporation play a pivotal role in driving innovation and adoption of big data analytics in agriculture. These companies offer a wide range of solutions tailored to the specific needs of farmers, cooperatives, and other end-users in the agricultural ecosystem. From traceability platforms to precision farming technologies, these market players are at the forefront of harness**Segments**
- **Technology:** Big data analytics in agriculture relies on advanced technologies such as IoT sensors, satellite imagery, and drones to collect crucial data related to crop health, soil conditions, weather patterns, and more. These technologies empower farmers to make informed decisions, optimize crop yields, and efficiently utilize resources.
- **Application:** The application of big data analytics in agriculture spans across various areas including crop monitoring, precision farming, livestock management, supply chain optimization, and predictive analytics. By harnessing data from diverse sources, farmers can enhance operational efficiency and promote sustainability practices.
- **End-User:** The end-users of big data analytics in agriculture encompass farmers, agricultural cooperatives, food manufacturers, and agricultural researchers. Each segment leverages data analytics to address specific challenges and drive productivity in their respective domains.
**Market Players**
- **IBM Corporation:** IBM offers cutting-edge solutions like IBM Food Trust and Watson Decision Platform for Agriculture to enhance transparency, traceability, and decision-making within the agricultural sector.
- **Deere & Company:** Deere & Company's Precision Ag Technology integrates machine data with agronomic insights to facilitate precision farming practices and optimize crop management.
- **Microsoft Corporation:** Microsoft's Azure FarmBeats platform utilizes AI and IoT technologies to enable data-driven agriculture, leading to optimized farming practices and increased yields.
- **AGCO Corporation:** AGCO's Precision Planting technology uses big data analytics to improve planting decisions and enhance crop performance through data analysis of seed placement and soil
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, Canada, 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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