Big data has been touted as a tool for multiple industries. Healthcare, finance, and environmental work are just three sectors that stand to benefit from collecting and analyzing large swaths of data. But, in the environmental category, agriculture is poised to reap particularly important benefits from big data. In the U.S., Michigan State University (MSU) is looking closer at how to use big data to improve agricultural practices, bolstered by government support — notably $4.9 million grant from the U.S. Department of Agriculture’s National Institute of Food and Agriculture.
MSU will lead a team of scientists to develop better managerial approaches to water use, fertilization, and modification of existing agricultural practices with regard to varying climates. Bruno Basso, a MSU ecosystems scientist, stated in a press release from the university:
Our research shows the interactions between soil, crop, climate, hydrology and agricultural management, and determines their effects on crop yield and the environment. This project links science with technology and big data analytics; we aim to help farmers better adapt to temperature extremes, droughts or excess water in fields so that they can make better decisions for the environment and maximize production and/or profits.
This is one of many projects targeting so-called “precision agriculture,” meaning the use of data and analytics to yield better and more crops, adjust to weather patterns, predict seasonal changes, and overall improve agricultural tasks. IBM did some research into precision agriculture and concluded that using predictive analytics can help future generations of farmers feed the steadily increasing global population.
IBM further noted that predictive analytics and big data analysis can be used to collect real-time data on weather, soil and air quality, crop maturity, and equipment and labor costs. Such technology could, in theory, aid farmers to communicate with crop experts. IBM researcher and Distinguished Engineer Ulisses Mello said:
A farmer could take a picture of a crop with his phone and upload it to a database where an expert could assess the maturity of the crop based on its coloring and other properties. People could provide their own reading on temperature and humidity and be a substitute for sensor data if none is available.
The challenge now is integrating these types of technologies into the farming world. It will be a slow but steady process, but the kind (and amount) of government funding backing the MSU project will be crucial to driving the next generation of agricultural practices.