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Applications of Big Data and Predictive Analytics in the Agricultural Industry

How could their use be a game changer?

Big Data, Predictive Analytics, Artificial Intelligence, Machine Learning. All of these are popular keywords that refer to the processing of huge amounts of information that are collected during daily activities in various sectors of the economy in order to create rules and algorithms so that it is possible to predict the most efficient and productive use of any kind of resources. The agricultural industry as an important sector of the economy is among all other industries in line to take advantage of the abundance of information and innovation. But how could the use of big data and predictive data analysis change the agricultural industry? Equipped with data from ground sensors or data obtained from drones as well as from vital data and information received from external sources that incorporate management methods for a wide range of conditions, agricultural businesses gain unprecedented control over their daily operations. This helps them to optimize their planning and productivity while better manage their resources. Let's look at some of the factors that will contribute to change.

Pest and Disease Control

Monitoring hundreds of acres of crops to control pests or diseases is a difficult task. However, ground detectors and sensors as well as drones properly equipped to collect information can perform this task very effectively. Soil detectors and drones can detect various problems if they are properly equipped. They can, for example, take plant samples for analysis or take soil samples that will make it easier to determine the best possible action for pest control.

Soil Nutrient Levels Control

Gathering information on soil nutrient levels is an important part of agriculture, whether small or large-scale. By collecting large amounts of information, it is easy to determine which crops need to be changed or when and how they should be enriched with fertilizers or nutrients to ensure optimal growth. The problem with traditional data collection methods is that they primarily involve identifying problems that exist so that appropriate corrective action can then be taken. However, with modern big data analysis techniques and the application of appropriate algorithms to historical information, problems related to nutrients can be predicted before they appear or become significant and jeopardize crop yields.

Crop Predictions

By studying the performance of previous years and applying big data techniques, it is possible to predict with a relatively high degree of accuracy the crops that will produce the highest yield for a particular year or a specific field. Provided the information gathered in previous years is accurate, it is much easier to determine the best days for sowing, the best days to apply fertilizers or herbicides, and the best time to harvest to ensure higher yields. When farmers have access to plenty of data, they have the information and knowledge they need about when, where, and how to sow.

Better Supply Chain Management

Big data techniques are already being applied to supply chains in various industries. In terms of agriculture, all the ingredients starting from the sources of supply for crops - seeds, fertilizers, herbicides, insecticides, etc. - until the final delivery to the retailer are part of the supply chain. And all this data can be collected. Thus, the information that is necessary knowledge for everyone who participates in each stage of the supply chain becomes part of a larger set of big data. Farmers can easily track their products throughout the supply chain, while retailers, distributors and other key stakeholders are better equipped to tailor their products and services to the needs of farming thanks to the growing availability of rich data and information. Using predictive analytics techniques, farmers and other stakeholders can anticipate their needs for seeds, fertilizers, herbicides, etc. This allows them to procure what they need instead of facing shortages or creating surplus stocks that they may or may not use in the future, but will be required to store. The agricultural supply chain is ready to experience some of the most impressive effects of Big Data and data analysis technologies.

Weather and Environmental Challenges Management

While it is possible to accurately control various variables related to agriculture, the weather always poses a certain degree of variability beyond the typical seasonal data. For example, very hot summers require more irrigation, while particularly wet months can lead to mold or mold growth in crops. These issues must be addressed. 100% accurate weather forecasts have limitations, but what can be predicted is how different crops react to different weather situations and how best to offset the effects. Improving weather forecasting and data analysis can give farmers the knowledge they need on how and when to sow, what to plant, when to add fertilizers and herbicides, and when to harvest. Big data application practices will help them improve their performance and adapt better to unexpected natural phenomena. Climate change and other environmental challenges are among the biggest threats to agricultural productivity, but data-driven agriculture can make it easier for farmers to adapt to changing environmental conditions and help fight climate change by making smarter resource management. The climate is changing, whether people like it or not, and agriculture needs to adapt to ensure strong returns in the future.


The use of sensors, drones and detectors as well as big data applications, predictive analytics, artificial intelligence and machine learning will completely change in the future the way the agricultural industry works. As the conditions for greater crop control mature, more and more farmers will adopt Big Data-based solutions to monitor trends, track supplies, assess risk, create predictive models and increase their returns.

Technological developments will help both large and small agriculture facilities to adapt so that they can seamlessly feed the world's growing population.


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