Uses of Big Data in Agriculture

Hello!

The word ‘data’ originates from the Latin word ‘datum’, which means ‘piece of information’. Fast forward to the late 2000s, and you’ll realize that ever since then, everything we create, consume and leave behind is essentially a ‘piece of information’. Whether it is liking a post, tweeting an article, or ordering a pizza, every digital action is forever immortalized on a server somewhere in the world. Given the ongoing excitement around data and analytics, there has never been a better time to pick up these skills. If you’re wondering where to start, you can learn Hadoop and Spark basics.

The Growing Popularity of Big Data

Uses of Big Data in Agriculture With an ever-growing number of devices connected to the internet, especially after the Internet of Things revolution, the volume of data is growing at an exponential pace.

On the other hand, raw computing power has seen only linear growth. In short, the amount of data has become so vast that traditional analysis tools no longer suffice.

This is why tech companies are investing heavily in big data solutions built on technologies like Hadoop. The goal is straightforward: make better decisions, boost profits, and gain deeper insights.

Consider a bank that wants to target loan offers at the 30-45 age group. Data scientists would apply machine learning algorithms to identify the most influential factors, such as age, salary and credit score. To generate meaningful results, the team needs access to an expansive dataset covering millions of potential customers. Big data tools have therefore become essential for collecting and processing information at this scale, driving strong growth in the sector over the past five years.

Big Data Explained

Uses of Big Data in Agriculture With the use case established, let’s examine what Big Data actually is. A common starting point is the mathematical question: how much data qualifies as ‘Big Data’? This threshold has shifted over time. In 1999, when the world’s total data volume stood at 1.5 exabytes, a 1 GB dataset could already be considered Big Data. According to a PWC prediction, by the end of 2026 the global volume had reached 44 zettabytes, with the term now typically applied to sources exceeding 1 terabyte. More broadly, any data that is dynamic in nature and cannot be handled by traditional relational databases falls under the Big Data umbrella.

Data from any source is usually assessed using five key dimensions, known as the Five V’s of Big Data:

1. Volume

Uses of Big Data in Agriculture The sheer size of the data collected from a source.

2. Velocity

The speed at which new data is generated. Many systems produce data in real time, requiring instantaneous processing.

3. Variety

Data generally falls into three categories: structured (fits a predefined schema of tables, rows and columns), unstructured (stored in its native format until processing, e.g. emails, images and sensor readings), and semi-structured (fundamentally unstructured yet containing metadata that simplifies analysis).

4. Veracity

The reliability and consistency of the data, indicating whether it contains significant errors or inconsistencies.

5. Value

Uses of Big Data in Agriculture The most critical dimension: whether the available data is substantial enough to yield actionable insights.

How Big Data Is Used in Agriculture

Agriculture is one sector poised to benefit enormously from Big Data. From choosing which crops to plant to determining the optimal harvest window, every stage can be enhanced by tools such as soil sensors and GPS-enabled tractors. Below are proven approaches that improve productivity.

1. Precision Agriculture

Precision Agriculture (PA), also known as Site-Specific Crop Management (SSCM), uses sensors, cameras and geo-positioning systems to gather detailed information about a farm and guide planting decisions. Soil sensors help farmers increase yields while optimising the use of seeds and fertilisers. Although still relatively new, US-based John Deere has demonstrated its practicality by equipping tractors with sensors and soil probes.

2. Supply Chain Management

Uses of Big Data in Agriculture Geo-tagging every step of the harvest journey increases transparency and reduces waste. Analytics can also identify the most efficient routes from field to storage, lowering transportation costs. A leading example is IBM’s Food Trust, a blockchain-based platform connecting producers, suppliers, manufacturers, retailers and consumers. Customers can trace the exact location and status of produce, including details about where each plant was grown.

3. Reducing Losses from Natural Calamities

Advanced systems can forecast, based on historical patterns, the probable timing of storms, heavy rainfall and other risks such as crop diseases or pest outbreaks. Timely alerts of this kind are invaluable to farmers.

4. Improved Crop Prediction

Uses of Big Data in Agriculture Traditionally, farmers spend years testing different crops to discover the highest-yielding options. Advanced analytics that combine soil data with local weather forecasts now enable accurate predictions of the best crop for a given season, delivering significant financial advantages.

5. Increased Investment in Agriculture

As data-driven farming becomes standard practice, major companies are expected to pour resources into agri-tech, competing to deliver the most affordable and user-friendly solutions. The resulting benefits will extend to farmers, retailers and consumers alike.

In conclusion, Big Data is here to stay, and agriculture stands to gain substantially from continued advances in Big Data and Big Data Analytics. Learning Hadoop and Spark basics can therefore open doors to impactful work in the agricultural sector.