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.
Big Data Analytics in Agriculture Market Analysis and Size
Big data analytics in agriculture refers to the application of advanced data analysis techniques to large and complex datasets generated within the agricultural sector. It involves collecting, processing, and analyzing vast amounts of data from various sources, such as sensors, satellites, drones, weather stations, and farm equipment, to extract valuable insights and inform decision-making processes in farming and agricultural management.
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.
Big Data Analytics in Agriculture Market Scope and Segmentation
2023 to 2030
2021 (Customizable to 2015-2020)
Revenue in USD Billion, Volumes in Tons, Pricing in USD
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)
U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Saudi Arabia, U.A.E., South Africa, Egypt, Israel, Rest of Middle East and Africa, Brazil, Argentina and Rest of South America.
Market Players Covered
NTT Data Corporation (Japan), The Climate Corporation (U.S.), OnFarm (U.S.), Farmers Edge Inc. (Canada), Agribiotix (U.S.) , AgDNA (U.S.), Awhere (U.S.), Farmersedge (Canada) and Conservis (U.S.)
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.
Global Big Data Analytics in Agriculture Market Dynamics
- Increasing Availability of Data
The increasing availability of data from various sources, including sensors, satellites, and drones, is a primary driver. This data provides valuable insights into crop conditions, weather patterns, and soil health. Data availability is a critical factor in the success of big data analytics in agriculture. To effectively harness the power of data analytics in agriculture, it's essential to have access to a wide range of data from various sources.
- Technological Advancements
Advances in data analytics, machine learning, and IoT technologies enable more sophisticated data analysis, prediction, and decision support in agriculture. Advanced sensors, including soil moisture sensors, temperature sensors, and remote sensing devices mounted on drones and satellites, have become more affordable and accessible. These sensors provide real-time data on soil conditions, weather patterns, crop health, and more, allowing farmers to monitor their fields with precision.
- Precision Agriculture
Big data analytics enables precision agriculture by providing real-time insights. Farmers can optimize inputs such as water, fertilizer, and pesticides, resulting in higher yields and resource efficiency. Precision agriculture relies on the collection of vast amounts of data from various sources, including sensors, satellites, drones, and farm machinery. These data sources provide real-time information about soil conditions, weather patterns, crop health, and more. Big data analytics platforms aggregate and process this data to create a comprehensive picture of the farm.
- Market Intelligence
Farmers can use data analytics to monitor market trends, optimize pricing strategies, and make informed decisions about crop selection and planting. Market intelligence begins with data collection. This includes gathering information from diverse sources, such as government reports, trade publications, social media, weather data, commodity prices, and supply chain data. Big data analytics systems can process both structured and unstructured data from these sources.
- Data Privacy Concerns
Farmers may be hesitant to share their data due to privacy concerns and the fear of misuse by third parties. Agricultural data often includes personal information about farmers, such as their names, contact details, and financial data. Protecting this personal data from unauthorized access and misuse is crucial. Many agricultural technologies, such as GPS-enabled equipment and drones, collect location data. The exposure of this data can lead to privacy breaches and potential security risks if not properly safeguarded.
- Data Integration
Integrating data from different sources and formats can be complex. It requires standardized data formats and interoperability between various data systems. Oil moisture sensors, weather stations, GPS-equipped farm equipment, and livestock monitoring devices generate real-time data. Satellites and drones capture high-resolution images of fields, which provide insights into crop health and growth.
Impact and Current Market Scenario of Raw Material Shortage and Shipping Delays
Data Bridge Market Research offers a high-level analysis of the market and delivers information by keeping in account the impact and current market environment of raw material shortage and shipping delays. This translates into assessing strategic possibilities, creating effective action plans, and assisting businesses in making important decisions.
Apart from the standard report, we also offer in-depth analysis of the procurement level from forecasted shipping delays, distributor mapping by region, commodity analysis, production analysis, price mapping trends, sourcing, category performance analysis, supply chain risk management solutions, advanced benchmarking, and other services for procurement and strategic support.
Expected Impact of Economic Slowdown on the Pricing and Availability of Products
When economic activity slows, industries begin to suffer. The forecasted effects of the economic downturn on the pricing and accessibility of the products are taken into account in the market insight reports and intelligence services provided by DBMR. With this, our clients can typically keep one step ahead of their competitors, project their sales and revenue, and estimate their profit and loss expenditures.
- In November, 2020, SAS Institute Inc. in partnership with Boragen Inc. are offering platform for combining crop science and data science to protect crop life from plant disease. The companies are using artificial intelligence and machine learning to study the data and predict the damage in real-time.
Global Big Data Analytics in Agriculture Market Scope
The global big data analytics in agriculture market is segmented on the basis of type, application and end users. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.
- Capturing Data
- Storing Data
- Sharing Data
- Analyzing Data
- Crop Production
- Farm Equipment
- Agriculture Regulatory Bodies
- Weather Forecast
- Farm Equipment Industries
Global Big Data Analytics in Agriculture Market Regional Analysis/Insights
The global big data analytics in agriculture market is analysed and market size insights and trends are provided by country, type, application and end users as referenced above.
The countries covered in the global big data analytics in agriculture market report are U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Saudi Arabia, U.A.E., South Africa, Egypt, Israel, Rest of Middle East and Africa, Brazil, Argentina and Rest of South America
Asia-Pacific dominates the global big data analytics in agriculture market due to the major number of companies implement the big data results to prefigure the condition of the crop.
The country section of the report also provides individual market impacting factors and changes in regulation in the market domestically that impacts the current and future trends of the market. Data points like down-stream and upstream value chain analysis, technical trends, and porter's five forces analysis, case studies are some of the pointers used to forecast the market scenario for individual countries. Also, the presence and availability of global brands and their challenges faced due to large or scarce competition from local and domestic brands, the impact of domestic tariffs, and trade routes are considered while providing forecast analysis of the country data.
Competitive Landscape and Global Big Data Analytics In Agriculture Market Share Analysis
The global big data analytics in agriculture market competitive landscape provides details by competitors. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, global presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width, and breadth, application dominance. The above data points provided are only related to the companies' focus related to the global big data analytics in agriculture market.
Some of the major players operating in the global big data analytics in agriculture market are:
- NTT Data Corporation (Japan)
- The Climate Corporation (U.S.)
- OnFarm (U.S.)
- Farmers Edge Inc. (Canada)
- Agribiotix (U.S.)
- AgDNA (U.S.)
- Awhere (U.S.)
- Farmersedge (Canada)
- Conservis (U.S.)