MACHINE-LEARNING methods can acc-urately predict wheat yield for the Australia crop two months before it matures, the University of Illinois says.
Wheat is grown on more than half Australia’s cropland and is a key export commodity. Accurate yield forecasting is necessary to predict regional and global food security and commodity markets.
Kaiyu Guan, assistant professor in the Department of Natural Resources and Environmental Sciences, says the researchers tested various machine-learning approaches and integrated large-scale climate and satellite data to come up with a reliable and accurate prediction of wheat production for the whole of Australia.
People have tried to predict crop yield almost as long as there have been crops. With increasing computational power and access to various sources of data, predictions continue to improve. In recent years scientists have developed fairly accurate crop yield estimates using climate data, satellite data, or both, but Guan says it wasn’t clear whether one dataset was more useful than the other.
“We use a comprehensive analysis to identify the predictive power of climate and satellite data,” he says. “We found that climate data alone is pretty good, but satellite data provides extra information and brings yield prediction performance to the next level.
Using climate and satellite datasets, the researchers were able to predict wheat yield with about 75 per cent accuracy two months before the end of the growing season.
Co-author David Lobell of Stanford University says the team compared the predictive power of a traditional statistical method with three machine-learning algorithms, and machine-learning algorithms outperformed the traditional method in every case.”
Lobell began the project during a 2015 sabbatical in Australia.