How might accurately predicting crop harvests help curb malnutrition? Lillian Kay Petersen, a 17-year old from Los Alamos, N.M., has created a tool that’s intended to do that.
First, she analyzed daily satellite imagery on known domestic crop data. Then she applied that data to countries in Africa and successfully predicted crop yields.
Petersen’s project won her first place and $250,000 in the Regeneron Science Talent Search on Wednesday night. It’s one of the most prestigious science and math competitions for high school seniors.
Inspired by her siblings, Petersen created a scientific model that could reduce food insecurity.
“Nine years ago, my family adopted my three younger siblings, all of whom faced food insecurity in their childhoods.” Petersen said. “I have watched my younger siblings struggle with the lifelong effects of malnutrition.”
After reading about a devastating 2015 drought in Ethiopia, she put her computer science skills to use.
“I wanted to find a way to help aid organizations monitor crop conditions as droughts evolve, to help them respond to food crises with a better time to help children so that they don’t face malnutrition and lifelong consequences,” she tells NPR.
During her sophomore year of high school, she began monitoring droughts in Africa using satellite imagery. Less than two years later, she published her first paper on the subject in a peer-reviewed journal. She recently met with the International Food Policy Research Institute to discuss the global pandemic and its effects on crop production in Africa.
She plans to continue her studies at Harvard this fall.
Petersen was among 40 finalists in the Regeneron competition whose math and science research projects were judged by leading scientists. Second place went to Jagdeep Bhatia, 18, of Green Brook, N.J. for developing machine-learning algorithms. Third place went to Brendan Crotty, 18, of Muskogee, Okla., for his project aimed at reducing emissions in industrial gas burners.