Big Data Applications in Agriculture: Role, Importance & Challenges

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. 

What is big data? 

When a question arises what is big data, it is a collection of large, complex, and unprocessed data is called ‘big data’. 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. This vast reservoir of information must be studied, stored, and processed systematically for its applications in the public sector, scientific research, agriculture, industry, etc.

Applications of big data 

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.

Banking – The big data applications in banking & insurance sector handles enormous amounts of data. Big data analytics are being used to store data, improve scalability, and derive business insights.

Healthcare – The problem of communication silos that plagues the healthcare industry can be considerably reduced with the application of big data-based protocols.

Telecom – Real-time analysis of big data provides useful predictions to derive business insights and strategies such as delivering revenue-generating services while keeping in mind network and customer considerations.

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, thanks to smart machines and sensors that generate vast amounts of farm data.

Traditional tools are being replaced by sensor-equipped machines that can collect data from their environments to control their behavior – such as thermostats for temperature regulation or algorithms for implementing crop protection strategies. Technology, combined with external big data sources like weather data, market data, or standards with other farms, is contributing to the rapid development of smart farming. 


Role of big data 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.

Primary sources of data include operations, transactions, and images and videos captured by sensors and robots. However, extracting the full potential of this data repertoire lies in efficient analytics. The development of applications related to risk management, sensor deployment, predictive modeling, and benchmarking, has been possible due to big data. 

Stakeholders involved 

Technology and input suppliers are the traditional players who offer their platforms and solutions to the farmers. Data privacy and security risks compel farmers to form coalitions to benefit from their data, creating a close and proprietary environment. Big data also attract start-ups, private firms, non-agricultural tech companies, and public institutions.

The organization of the stakeholders determines the infrastructure of big data solutions – either proprietary or an open-source system. The development of big data applications in agriculture will result in either the farmers becoming franchisers in integrated long supply chains or a scenario in which farmers collaborate with suppliers and the government to engage in short supply chains.


Read: Big Data Project Ideas and Topics

How is big data analytics transforming agriculture? 

Boosting productivity – Data collected from GPS-equipped tractors, soil sensors, and other external sources has helped in better management of seeds, pesticides, and fertilizers while increasing productivity to feed the ever-increasing global population.

Access to plant genome information – This has allowed the development of useful agronomic traits.

Predicting yields – Mathematical models and machine learning are used to collate and analyze data obtained from yield, chemicals, weather, and biomass index. The use of sensors for data collection reduces erroneous manual work and provides useful insights on yield prediction.

Risk management– Data-driven farming has mitigated crop failures arising due to changing weather patterns.

Food safety – Collection of data relating to temperature, humidity, and chemicals, lowers the risk of food spoilage by early detection of microbes and other contaminants.

Savings – AI and data analytics-driven farming generate significant savings for the agriculture industry.

Read: Big Data Applications That Surrounds Us

Challenges in implementing big data solutions in agriculture 

  • The generation of good-quality data is a critical concern in farm management information systems, and big real-time data does little to alleviate the problem.
  • The strict application of data-ownership, privacy, and security issues impedes innovation.
  • The large volume of unstructured and heterogeneous data demands domain experts and skilled data scientists.
  • A successful business model calls for sustainable integration of data from all sources, which is often a Herculean task.
  • The business models have to be such that they allow a fair share among the stakeholders.
  • There is a challenge of developing affordable solutions for farmers in developing countries.


Big data analytics has influenced some of the most critical sectors of the economy and will continue to do so. The big data applications in agriculture are still in their early days, with challenges that need to be addressed. The full potential of big data will be realized if farmers and stakeholders come together to develop and adopt innovative crop management techniques that are data-driven and data-enabled.

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