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How to Implement Big Data Analytics in Agriculture: 5 Business Use Cases

Learn about the principles of smart farming and the best ways to apply data science in agriculture

Updated: May 10, 2024 6 mins read Published: January 21, 2022

Today, data is transforming one of the oldest of human activities – agriculture. Technology advancement of data-driven agriculture can solve global problems of the society. Especially in areas where farming is a mean of survival and people struggle with environmental and climate factors such as crop loss, a data-driven approach makes a real difference.

So, what are the best ways to apply big data in agriculture?

Is it a tool to fight environmental negligence, feed the starving, or earn a better return from each square meter of soil?

It’s all in one and even more.

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Data-driven agriculture offers cost savings and business opportunities. Let’s try to measure them

In AgriTech, cooperation between private farmers, big agricultural corporations, communities, and governments can show tremendous results. But we shouldn’t forget about reliable tech service providers for AgriTech. Such cooperation can result in the wider promotion of agriculture and rural development, a reduction in poverty, and food security. But to start solving humanitarian and environmental issues, this technology should first prove its economic strength.

AgriTech startups know how to lure investors. AgFunder reports a total of $17 billion in investment in AgriTech in 2018, showing a 43% increase year over year.

Funding for AgriTech startups
How to Implement Big Data Analytics in Agriculture: 5 Business Use CasesSource: AgFunderNews

The adoption rate of data science service and big data analytics in farming is consistently increasing. MarketsAndMarkets expects growth of the AgriTech analytics market from $585 million in 2018 to $1,236 million by 2023.

Agriculture analytics market by region
How to Implement Big Data Analytics in Agriculture: 5 Business Use CasesSource: MarketsandMarkets

Stakeholders at the global level are looking for solutions to economic issues related to agriculture and food provision. Recent studies confirm that the data-driven approach to these problems is a sensible move. Fighting challenges facing food and agriculture has the potential to save an estimated $2.3 trillion.

Another study explains that $250 billions of those yearly savings could come from AI and data analytics alone.

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. While data analysis for agriculture businesses increases yields, it also reduces the consumption of resources like water and electricity thanks to smart metrics and reports.
  • Stop migration of the labor force. The increasing use of big data in agriculture proves that technology can be central to the world’s oldest industry, making it more attractive for specialists and preventing them from searching for other occupations.
  • Reduce food waste. 20% to 30% of food is wasted today at various stages of the supply chain. Through fighting this challenge, AgriTech can save as much as $155–405 billion a year by 2030.
  • Attract greater investments in AgriTech. Big data is one of the many technological advancements reshaping the AgriTech industry. To achieve better results, investments should be made in other spheres of agribusiness. The success of big data in smart farming can justify investing in technologies like sensors and cloud computing.

How big data can modernize agriculture in practice: Business use cases

Simplified data management through automated reporting, dashboards, and analytics

Smart, digital, and precision farming have dramatically increased the amount of data available for the numerous improvement scenarios in agriculture for smart spraying, seeding, and harvesting. But what counts the most is not data alone; it’s the ability to extract meaningful information from it applying data science in agriculture. Insightful metrics in the form of schemas, dashboards, and analytical reports are critical for an industry with so many variables. Even though making farming technologies transparent and simple for farmers to adopt is a tall task, it’s a realistic objective for tech providers.

Ideal AgriTech dashboards and analytics solutions should leverage data science in agriculture to automate and visualize as much as possible for farmers. When data is pulled together into a backend system, it can be placed into a customizable dashboard with easy-to-use data views. For instance, it could present mapping information and field and crop data, enable collaboration, and show the status of integrated equipment. A customizable dashboard can track all set conditions and alert farmers when important changes take place. Once the dashboard is set up, all data gathered by sensors, irrigation equipment, weather forecasts, and other sources can be automatically updated and secured.

The use of big data in agriculture also changes the way farmers receive their yield analytics. They can use smartphones to document the progress of their crops throughout the season. Or satellites, drones, and robots can do it for them. Analytical software can then estimate the yield potential according to the weather conditions, historical data, and information captured by farmers. As the next step, a big data-powered system can automatically generate yield reports to help farmers see actual automatic calculations. According to these yield reports, farmers can plan their actions to improve the management of their crops and increase yields.

Big data for weather prediction

Practically all agricultural production is reliant on natural conditions such as climate, soil, pests, and weather. With the help of data analysis for agriculture businesses, farmers can observe the impact that extreme weather conditions and other phenomena can have on their crops. But even more valuable is the ability to predict and adjust to these things. Incorporating big data in smart farming software, you can see changes in weather conditions in real time and respond promptly. For example, data from sensors in soil and images taken by drones can help farmers establish expected growth rates. When a smart system knows what to expect, it can automatically detect anomalies or deviations and warn farmers of them.

Discover how to combine GIS and stations for weather monitoring used by the top agencies in the US and Canada

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Supply chain tracking

Various technologies are disrupting agriculture. Precision agriculture is more about how crops are produced, while smart farming can cover all stages of the agricultural supply chain. There are many stakeholders in an agricultural supply chain, and big data has proven useful for all parties throughout all stages. At the production stage, automated systems handle data to show performance and reveal issues in critical equipment. When we deal with such sensitive materials as seeds, plants, and food products, preventing spoilage is a matter of serious concern. Big data helps farmers and suppliers optimize fleet management software to increase delivery reliability. Moreover, big data tracking solutions, smart meters, and GPS-oriented analytics improve routing, cutting transportation costs and offering advanced mapping of the locations of animals and vehicles.

Configuration of stakeholders and functions in the agricultural supply chain
How to Implement Big Data Analytics in Agriculture: 5 Business Use CasesSource: Risks and opportunities for systems using blockchain and smart contracts

Risk assessment

In a broad sense, big data analytics in farming risk assessment is applied for benchmarking, sensor deployment, analytics, and predictive modeling. Applying these approaches to make predictions using big data can help farmers model and manage risks connected with raising livestock and growing crops.

Another perfect combo that’s now popular in risk assessment for agriculture is big data-powered smart contracts built on a blockchain platform. In agricultural insurance, such an approach converts the complex framework into faster and automated systems. Farmers want to make their economic models more resilient, while insurers wish to be more certain as to the insured events. Big data can help both farmers and insurers. Smart insurance contracts deal with various risks, including natural phenomena. Insurers then calculate a premium based on the likelihood of a particular weather event and the impact it would have on livestock or crops at a specific point in time. Farmers get paid automatically when the number of occurrences exceeds a predefined threshold.

Learn how a multinational agriculture company has established an R&D Lab and Risk Assessment Suite for Farm Sustainability

Read more

Food security

The insights into food production offered by big data help customers to establish confidence in food safety and security. In both smart agricultural facilities and in fields, devices like sensors, drones, and smartphones capture data at specific locations. This enables businesses to carefully collect and consider high-resolution data on humidity, temperature, chemicals, and so on. On top of that, data helps consumers find where and how products were grown, transported, and processed. It’s yet another motivating factor for producers and logistics agents to maintain quality.

Final thought

Data-driven agriculture boosts productivity and helps to fight agricultural-based challenges including food demand and starvation. Businesses supported by reliable technology providers have already demonstrated the tangible benefits of smart farming. The rate of investments in this domain only confirms the upward trajectory of big data for retail and data-driven agriculture development.

Reach out to Intellias experts for custom software development service and big data services specifically for the agriculture industry.

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