U.S. soybean, corn yields could be increased through use of machine learning

Research guided by a plant pathologist in Penn State's College of Agricultural Sciences suggests that machine-learning algorithms that are programmed to recognize changing weather patterns could show producers and agricultural managers how to increase soybean and corn yields in the United States. The approach could prove valuable in addressing climate change realities that have presented challenges in growing enough food for a rising global population, noted Paul Esker, associate professor of epidemiology and field crop pathology. "Soybean and corn are among the most valuable crops in terms of food supply and economic output in the U.S. agricultural sector," said Esker, who pointed to U.S. Department of Agriculture statistics that place corn as the most widely produced crop in the U.S., with soybean following close behind. Not only are these crops vital to food security in the U.S. and beyond, but their combined total value to the nation's economy is more than $100 billion. While Esker acknowledges that is an impressive figure, he points out that many scientists predict that that by 2050, the world must feed 9 billion people, so current outputs must increase.