Why Big Data is Crucial for Modern Agriculture
To keep pace with the expected population growth on Earth, current agricultural production must increase up to 70 percent by 2050, according to the Food and Agriculture Organization of the United Nations. Addressing this food production challenge will require solutions that improve agricultural methods and radically adapt them to environmental pressures. Big data, machine learning and artificial intelligence (AI) can play a key role in this process.
⚡The original article was published in the EPAM blog.
Let’s talk about numbers. Despite efforts to reach the United Nations Zero Hunger goal by 2030, more than 820 million people still experienced a lack of food last year. And at the same time, more than a third of all produced food never reached the table — it was either spoiled in transit or thrown out by consumers in developed countries. This results in around 1.3 billion tons of wasted food, worth nearly $1 trillion. The world is facing challenges at every stage of food production. As global food demand is projected to double in 30 years due to rising populations, farmers and agricultural suppliers will increasingly be expected to do more with less by improving productivity from limited resources and inputs.
Fortunately, a number of innovations already exist to increase crop yields and ensure the sustainable growth of the global agricultural industry. While traditional farming relied on managing fields based on volatile predictions and intuition, new collaborations between the technology and agriculture industries are challenging the conventional methods of farming.
Now, agronomy is focusing on ‘precision farming’ — a farming management system that uses modern technologies throughout every stage of work.
The concept of precision farming emerged in the US in the 1980s, but the methodology only became widespread in the last five years thanks to developments in mobile technology, high-speed internet and satellite data. Some early adopters have even shared their experience on YouTube and gained hundreds of thousands of followers, like MN Millennial Farmer or Ryan Custer who is the creator of How the Farm Works. Today, precision farming marries satellite data, sensors, drones and GPS mapping tools to provide field insights for farmers and enhance food production to a new level of productivity.
The more information there is, the more accurate analytics and insights will become. That’s why collecting and analyzing big data is extremely foundational to precision farming.
Understanding big data in precision farming
Big data sources in precision farming involve sensors that collect information from the ground and satellite imagery from space. The combination of these two sources of data provide the best results for farmers.
The major shift in the use of big data in precision farming happened with the launch of the Sentinel-2 satellite in 2015. The satellite operates under the Copernicus program, the European Union’s Earth Observation program initiated by the European Commission. The satellite monitors the Earth’s surface and, every five days, provides high-resolution, multispectral images with an incredible spatial resolution of 10m. The launch of this satellite was a breakthrough — Sentinel-2 delivers near-real-time data on a global level, and the images are free to use for the public. Both research institutions and businesses of all kinds receive a rich source of information, but in order to leverage and gain valuable insights from this data, intelligent and cognitive computing technologies like machine learning and AI are necessary to analyze this information.
Big data analysis for precision farming
There are several ways that big data analysis can help farmers, and it all starts with field and crop mapping across the world. Before saving the planet, you need to know what’s out there, right? OneSoil was the first to approach this complicated task at a global level. The interactive OneSoil Map provides information on fields and crops in Europe and in the USA, which can be used to explore national and regional trends, as well as check the development of a specific field. The interactive map runs on machine learning algorithms and satellite imagery.
Satellite imagery analysis helps monitor fields remotely via changes in various vegetation indices. Combining space and ground truth data, a farmer can calculate and apply the right dosage of fertilizers and chemicals for each part of the field, referred to as the variable rate application, which helps reduce the pollution of groundwater. Machine learning enables farmers to analyze decades of weather and crop records and look for patterns in the data to predict crop yields. Additionally, monitoring water and air conditions helps predict farming problems in specific regions. By understanding the scale of global catastrophes like wildfires, earthquakes or hurricanes, resources can be managed accordingly.
With numerous agricultural solutions on the market, it’s important that these technologies and applications are understandable, affordable and easy-to-use.
To help farmers, you need to think like a farmer.
OneSoil’s platform serves as a one-stop solution to help farmers gain quick access to valuable information like crop conditions, fertilizer dosages and weather records that is critical to crop production. As the agricultural industry looks to address the world’s food production challenges, partnering with technology companies like EPAM can help bring these sustainable solutions to life.