How Big Data Analytics Can Transform Agriculture and Farming

Big data analytics is used to process large amounts of data generated every day across industries and turn them into interpretable insights. According to Information Age, data analytics increased from 17% in 2015 to 59% in 2018. Next year, the data analytics industry is expected to increase even more, with Maryville University calculating that the annual growth rate will be 11.5%. This reflects how more industries are taking advantage of the power of big data analytics. For many businesses, big data analytics is the key to creating more services that lead to a more targeted customer experience and ultimately more profit.

This technology has pioneered many digital transformations across the globe, such as eCommerce, transportation, and healthcare. And agriculture and farming are no exception. Thanks to technology such as AI, machine learning, and the Internet of Things, here are some ways big data analytics can transform the agricultural sector.

Growing crops in an unfavorable climate can lead to poor harvests and a decrease in soil fertility. With the help of data gathered from previous cycles, scientists are procuring seeds that can survive any climate, temperature, and soil conditions.

Climate change is a constant global threat, but even more so for agriculture. The predictive capabilities of big data can provide accurate forecasts to enable farmers to prepare for the unusual weather. It can also provide parameters for other natural factors like rainfall, temperature, wind speed, sunshine, and cloud cover.

Learn more about Eka’s Weather Advisory app. 

Big data analytics analyzes decades of crop data to help farmers anticipate the number and state of the crops produced before the seeds are even planted. By choosing the best crop planting mixes and leveraging other farming factors, for example, the data can produce solutions that can maximize crop production in the shortest time possible.

Read the app sheet to learn how to improve yield forecasting. 

There are many risks involved in farming and agriculture, such as pest infestations and crop diseases, which are outside of farmers’ control. Before big data analytics, experienced farmers were able to spot signs of failing crops, but by then it was too late to do anything. Using data analytics, it is possible to track signs of plant diseases, and even predict if crops are under threat based on current and past harvests.

Watch the video to see disease identification in action. 

Farmers are not the only parties involved in the agricultural sector. There are also suppliers, traders and procurement managers that function in the marketing side of the industry. Any issues in the farming process, like delays in harvest and change in customer supply and demand, can cause a lot of fluctuations in market prices. Fortunately, big data also has the capacity to analyze the current status of the market and make price predictions based on it. This ensures that farmers have the best chance of making a profit.

Any data gathered throughout past cycles is stored and used via predictive analysis. Predictive analytics can be used to make informed decisions based on the gathered data, as well as suggest improvements to accommodate changing harvest conditions.

A recent report by Research and Markets shows that the global agriculture analytics market is expecting to reach $1.2 billion by 2023, leading to a growing demand for specialized tools that can collect, integrate and analyze data to improve farming productivity and increase the efficiency of agricultural businesses. This has led to the development of digital tools that can aid farmers. Eka Analytics brings the power of automation and data analytics to help your agriculture business grow and adapt to the ever-versatile crop market.