How big data is used in agriculture


Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in roles and power relations among different players in current food supply chain networks.

Big data provides farmers granular data on rainfall patterns, water cycles, fertilizer requirements, and more. This enables them to make smart decisions, such as what crops to plant for better profitability and when to harvest. The right decisions ultimately improve farm yields.


How will big data change agriculture?

Big data can truly revolutionize the agricultural sector only by having a cloud-based ecosystem with the right tools and software to integrate various data sources. These tools should be able to consolidate data on climate, agronomy, water, farm equipment, supply chain, weeds, nutrients, and so much more to aid the farmer make decisions.

What is the role of big data in agriculture?

Top 4 use cases for big data on the farm

  1. Feeding a growing population. This is one of the key challenges that even governments are putting their heads together to solve. …
  2. Using pesticides ethically. Administration of pesticides has been a contentious issue due to its side effects on the ecosystem. …
  3. Optimizing farm equipment. …
  4. Managing supply chain issues. …

What is AG big data?

When it comes to the year-to-date metrics, the WISeKey International Holding AG (WKEY) recorded performance in the market was -43.44%, having the revenues showcasing -35.32% on a quarterly basis in comparison with the same period year before.

How can we use data analytics in agriculture?

The opportunities of big data agriculture cannot omit

  • Increase farming productivity. Big data analytics in agriculture has already shown great results in forecasting crop production and improving crop yields.
  • Improve farming operations. …
  • Stop migration of the labor force. …
  • Reduce food waste. …
  • Attract greater investments in AgriTech. …

How Data Analytics is used in agriculture?

Data analytics can help farmers monitor the health of crops in real-time, create predictive analytics related to future yields and help farmers make resource management decisions based on proven trends. Reducing waste and improving profits.

What is big data in smart farming?

In conclusion, Big Data is to provide predictive insights to future outcomes of farming (predictive yield model, predictive feed intake model, etc.), drive real-time operational decisions, and reinvent business processes for faster, innovative action and game-changing business models (Devlin, 2012).

Why data is important in agriculture?

Data based decisions at the farm level can improve resource utilization and conservation practices. Similar efforts at regional level, tracking inputs per kilogram of produce or impact of production on natural resources can contribute towards long term policies for land and water conservation.

Which of the following are the future applications of big data in the field of agriculture?

Practical Application of Big Data in AgriculturePesticides use optimization. Pesticides use is considered an issue due to its side effects on the ecosystem. … Farm equipment management. Remote management of agricultural machinery helps large farms reduce costs. … Supply chain problems management. … Yield prediction. … Food safety.

How do farmers use data?

Weather stations and sensors allow you to monitor the weather remotely. This is particularly important for farms that grow vegetables and fruits. Sensors help to prepare for a critical change in temperature and to calculate the irrigation. Also, weather data helps to predict plant diseases and the emergence of pests.

How AI can be used in agriculture?

AI systems are helping to improve the overall harvest quality and accuracy – known as precision agriculture. AI technology helps in detecting disease in plants, pests and poor nutrition of farms. AI sensors can detect and target weeds and then decide which herbicide to apply within the region.

What is data collection in agriculture?

With respect to the application of RS in agricultural management, field data collection enables us obtain relevant information such as the crops cultivated, agronomic practices adopted by farmer and data on the temporal growth of crops (e.g. height, density, groundcover, etc.).

What is agricultural data?

Agricultural data is mainly used to maximize land yield to ensure that you are getting the best from your land. Weather forecasting data can be used to advise on planting, crop care or harvesting schedules.

What is the connection between IoT big data and the cloud in agriculture?

The relationship between IoT, Big Data and Cloud Computing creates ample opportunity for business to harness exponential growth. Put simply, IoT is the source of data, Big Data is an analytic platform of data, and Cloud Computing is the location for storage, scale and speed of access.

How is Blockchain used in agriculture?

Blockchain food supply chain can reduce food frauds with the help of the following steps:Step 1: IoT sensors generating data or Farmers storing data.Step 2: Distribution of grown crops to the food processing companies.Step 3: Supply of Processed Food to Wholesalers and Retailers.More items…

How can IOT help in agriculture?

On farms, IOT allows devices across a farm to measure all kinds of data remotely and provide this information to the farmer in real time. IOT devices can gather information like soil moisture, chemical application, dam levels and livestock health – as well as monitor fences vehicles and weather.

Why do farmers need information?

Use of information in agriculture sector is enhancing farming productivity in a number of ways. Providing information on weather trends, best practice in farming, timely access to market information helps farmer make correct decisions about what crops to plants and where to sell their product and buy inputs.

What do you know about big data?

Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered (known as the “three v’s” of big data).

How technology is enabling smart farming?

Various sensors and IoT-based devices are placed across the farm and the data generated from these helps to measure soil properties. The technology aids farmers in three key areas: farm management with personalized agronomic intelligence, access to inputs, and post-harvest commerce.

What is smart farming PDF?

Smart farming involves the integration of information and communication technology for better utilization of resources from sowing, irrigation, fertilizer, pesticide, and herbicide application, and finally harvesting.

What is precision farming?

Precision farming is an approach where inputs are utilised in precise amounts to get increased average yields, compared to traditional cultivation techniques. In India, one major problem is the small field size. More than 58 per cent of operational holdings in the country have size less than one hectare (ha).

How is big data used in agriculture?

Big data in the agriculture industry relies on the utilization of information, technology, and analytics in order to create useful data that can be utilized by farmers. Big data can be used to provide information for the agricultural industry as a whole, or it can help specific segments or locations with improving their efficiency.

Why is big data important for agriculture?

By eliminating the variables and allowing farmers to stay one step ahead of their ecosystem, big data can ensure that farming will remain a stable and feasible line of work for farmers.

How much is the agricultural big data market worth in 2025?

The agricultural big data analytics market alone is estimated to hit 1.4 billion USD by 2025, and this speaks highly of the interest being generated by the segment. In order to step into this future, it’s important to improve the analytical capabilities of machine learning and artificial intelligence systems.

How does Big Data help farmers?

Big data can not only help farmers save money by improving efficiency , but it can also help them make more money by improving vital processes.

Why is precision farming important?

By monitoring such vital data points closely, farmers can stay one step ahead of their problems and better marshall their resources during times of trouble. Precision farming methods allow farms to keep an eye on their soil and crop health round the clock remotely.

Why do farmers use data?

Farmers can also utilize the data in order to understand, observe, and solve various specific issues in their farms. The overall purpose would remain to improve the operational efficiency of the farmers.

How much is big data worth in 2020?

The current global value of the industry is estimated at 138.9 billion USD in 2020.

What is big data in agriculture?

Big data in agriculture. Big data applications in agriculture are a combination of technology and analytics. It entails the collection, compilation, and timely processing of new data to help scientists and farmers make better and more informed decisions. Farming processes are increasingly becoming data-enabled and data-driven, …

What are the issues that are being addressed by big data applications in agriculture?

Sustainability, global food security, safety, and improved efficiency are some of the critical issues that are being addressed by big data applications in agriculture. Undoubtedly, these global issues have extended the scope of big data beyond farming and now cover the entire food supply chain. With the development of the Internet of Things, various components of agriculture and the supply chain are wirelessly connected, generating data that is accessible in real-time.

What is big data?

Big data is an extensive collection of both structured and unstructured data that can be mined for information and analyzed to build predictive systems for better decision making. Besides the government, telecom, healthcare, marketing, education, and several industrial sectors, big data applications in agriculture are gaining momentum as technologies like livestock monitoring gadgets, drones, and soil sensors are generating large volumes of data to support data-driven farming. The ultimate goal is to help farmers, agriculturists, and scientists adopt beneficial farming practices.

Why is big data not processed?

Due to complexity, big data cannot be processed by conventional data processing and data management applications and requires advanced tools that can analyze and process large volumes of data. Big data is characterized by some unique features – volume, variety, velocity, variability, veracity, and complexity. …

What is data influx?

Government – Data influx from sources such as sensors, satellites, CCTV and traffic cameras, calls, emails, social media, IT spaces, academia, etc. calls for efficient data storage and analysis for better governance and management of the public sector.

Why is big data important for farmers?

Big data offers opportunities for smart and precise pesticides application, helping the farmer to easily make decisions on what pesticide to apply, when, and where.Such monitoring helps food producers to avoid the overuse of chemicals. Besides, it increases farmers’ profits by cutting costs on unnecessary pesticides use.

How does big data help the supply chain?

Big data makes it possible to achieve supply chain efficiency by offering tracking and optimization opportunities for delivery truck routes. As a result, food delivery cycles, from producer to the market, become much shorter, ensuring no food is wasted in the process.

How does remote management help farmers?

Thanks to big data applications that can process and analyze streams of data retrieved by a variety of sensors, ranging from satellites to farming equipment, farmers can remotely track their machinery in the field. This way they can eliminate all the unnecessary routes, considerably lowering spendings on fuel.

Why is smart farming important?

Ultimately, smart farming and precision agriculture practices help farmers to save costs and open new business opportunities. Here are the main possibilities that come with big data use in agribusinesses.

How does modern farming help?

And one of the tasks of modern farming is to enable instant detection of microbes and signs of contamination. This can be done by collecting data on temperature, humidity, and chemicals to assess the health of a growing plant.

What is the technological revolution in agriculture?

Technological revolution that is currently happening in the agricultural sector became possible, among other things, due to big data. Collecting and analyzing big data can not only improve the productivity of individual farms but also help halt a global food crisis. The significance of this lies in the growing need to produce more food …

Is it hard to imagine the modern world without data?

It’s hard to imagine the modern world without data. More and more data is produced and used worldwide. But for the successful operation of an agricultural business , having an opportunity for big data analysis and management is key.

Where is the big data in agriculture coming from?

According to Research and Markets, the global agriculture analytics market is to reach $1,670.4 million by 2026. Momentum around data-driven farming is gathering thanks to technology like soil sensors, drones and livestock monitoring gadgets to produce reams of priceless information.

How to use ?

The uses of big data in agriculture are diverse. Some of the more prominent include:


Every farmer has a goal for their operation. Some of the more commonly cited are around improving profitability and efficiency, reducing the cost of an operation, or increasing product value.

Why is big data important for agriculture?

Big data, when analyzed and layered together with other datasets within the data ecosystem, may help stakeholders in agriculture and nutri- tion to make better decisions across the entire food system . Although there are actions specific stakeholders can take towards making big data work for agriculture and nutrition, some actions are universal.

Why is it important to consider big data?

It is important to consider that big data fulfill s a specific role within the larger data ecosystem. The data ecosystem includes all sizes and types of datasets. Data may not become big data unless they are analyzed at a certain scale. Import- ance and impact of the dataset may not be correlated with the dataset size.

What is closed data?

The standard operating procedure of business, science, and management is closed data, meaning data that are not open or shared (ODI, 2015). If big data is to be used optimally, organizations need to share or open their data. However, this process may require them to change their busi- ness models, the people they hire, their business relationships, and their institutional culture. Such a process is slow and potentially threaten- ing to risk-averse organizations, or those that do not have the financial or human capacity to change. Researchers in universities are espe- cially averse to opening and sharing data, for fear of others stealing their results. However, they are open to reusing data that others have published (Digital Science, 2017). Other cul- tural considerations include bureaucracy and other social structures that impede data sharing, norms and structures that can be highly variable across countries or regions.

How does big data help the SDGs?

As the international community works to fulfill the SDGs, big data will drive many of the efforts tied to linking agriculture and nutrition and re- shaping the global food system. The collection of high-quality data is not sufficient. This vast well of information must translate into knowledge that is easily accessible by non-technical audiences, including policy-makers and civil society. By carefully building a system for open and big data, one that includes clear definitions, rules over ownership and use, and transparency and accountability, we can ensure that the benefits of big data are passed on to the most vulnerable segments of society.

What are the SDGs for food?

The amount of data collected on global food systems is immense, and the Sustainable Development Goals (SDGs) of the United Nations (UN) (especially SDGs 2, 3 and 17) encourage the sharing of information and data on agriculture and nutrition.

Where does big data come from?

While big data can be sourced from industry, academia, and government, it can also be generated by the users of farm equipment, mobile phones, and social media. When people use an app, the information they input and their behavior while using the app then becomes big data for others to interpret and use.

Do governments collect data?

Most governments across the world have minis- tries of agriculture, food, and health that collect and organize a tremendous amount of data. Governments are often the stewards of the data that they collect (Smith and Jellema, 2016), can own the data, and host it. Much of the data that exist across the world collected by governments may not be considered big data, especially within developing countries. However, governments have a responsibility to interpret big data and act upon it for the benefit of their citizens.


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