WASHINGTON — Every year, Rob DeFeo consults nature to figure out when Washington’s cherry blossoms will bloom. The National Park Service chief horticulturist, who has rough hands and wears a battered leather jacket, studies plants and trees, ponders seasons past and taps instincts born of decades watching winter turn to spring.
Millions of dollars ride on his forecast, as does as the fate of the city’s annual tourist extravaganza, which began Saturday. DeFeo says he has been on target 13 out of 16 years.
But now come Virginia Tech’s Vidhya Dass, 33, and Elizabeth Brennan, 24, students armed with artificial neural networks, evolutionary computations, the Arrhenius equation, linear regression and something called fuzzy logic to suggest an alternative to DeFeo’s seasoned eye.
Which is to say: Might the brain of a computer someday match human blossom intuition? It is, if you will, algorithm vs. biorhythm, a finger on the “enter” button vs. a finger in the wind, artificial intelligence vs. a guy who once had 300 species of azaleas in his yard.
The students’ idea grew out of an artificial intelligence class they took last spring as part of a master’s program at Virginia Tech’s Falls Church, Va., campus. Their teacher, assistant professor Chang-Tien Lu, suggested that they try using artificial intelligence to predict the peak bloom period.
The task has traditionally been done by DeFeo, 52, a wry New Jersey native and lifelong horticulturist who is an expert on the life and lore of the renowned cherry blossoms.
DeFeo scrutinizes such things as early flowering elms, maples and cornelian cherry dogwoods, as well as the weather and other recurring clues to the advent of spring.
This year, according to the forecast he issued this month, the peak bloom period is from March 27 through April 3. He said Friday that Saturday was the peak bloom day, when 70 percent of the blossoms would open. The bloom generally continues for several days beyond the peak period, depending on the weather.
Dass and Brennan’s approach was far different from that of DeFeo, whom they consulted. The two did not hazard a forecast but plugged in historical data about past blooms and associated weather conditions. Because they used recorded data and outcomes, they were able to see which models worked best. They found several models that were accurate to within a few days of past peak dates.
The students say some models, according to their calculations, came three days closer to the peak bloom date than DeFeo’s predictions.
But DeFeo focuses more on a bloom range, and, anyway, “it’s a crapshoot,” he said, adding: “The trees will be in full bloom when the blossoms are fully open.”
