Next month, a team of researchers will load one small aerial drone and two ground drones with sensors and release them on a field planted with hundreds of varieties of sorghum. In just one trip, the three drones, along with several stationary sensors, are expected to gather enough intel to construct a 3-D model of the field that will help researchers do what’s historically taken plant breeders ages to accomplish—pinpoint when a single plant in the field of sorghum varieties is thriving beyond expectation.
Compared to corn, its leading biofuel competitor, sorghum requires less water and can thrive in drought and heat conditions where other crops die. But the best varieties of sorghum for biofuel production aren’t well known. This team’s objective is to use drones and automated sensors to measure as many physical characteristics of each individual plant as possible—everything from height and thickness to the angle the leaves are growing to photosynthetic activity.
The aerial drone—a 25-pound autonomous helicopter—will be equipped with lidar as well as with visible imaging, thermal infrared, and hyperspectral cameras. At least once every two weeks, the tiny chopper will make a 20-minute flight across a 10-acre plot while ground drones trove through the field, taking their own measurements and placing tiny sensors on plant stalks and leaves.
“The big picture goal is to get a big increase in the yield for this bioenergy sorghum,” says Paul Bartlett, a senior robotics engineer with Near Earth Autonomy, the company that’s building the aerial drone sensor system for the project. If the yield is substantially increased “it could really make [sorghum] a sustainable bioenergy source,” he adds.
Growing fuel domestically could have a massive impact on both the environment and the economy. Sorghum biofuel produces less than half of the greenhouse gas emissions as traditional petroleum products. Replacing traditional fuels will require a steep increase in bioenergy sorghum production, which is why the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) is sinking $30 million into projects like Bartlett’s that use robots and automated sensor systems to gather physical “phenotyping” data on plants much faster than humans taking measurements in the field ever could.
That’s a major issue that’s holding up plant genetics research, says Edgar Spalding, a plant physiologist at the University of Wisconsin-Madison who is not involved in Bartlett’s project. While scientists can generate gobs of data on a plant’s genetic constitution thanks to genome sequencing, there’s far less data on how that genetic information translates to a plant’s physiology, or phenotype. Because manual phenotyping is slow and expensive, it limits the scope of the experiments researchers can carry out.
That could change soon. Working in tandem with the Carnegie Mellon Robotics Institute, the Donald Danforth Plant Science Center, and plant researchers at Clemson University, Bartlett’s team is one of six ARPA-E funded groups that are racing over the next two to four years to construct automated systems that can gather massive amounts of accurate phenotype data, analyze crop growth, and develop algorithms for selecting the best plants to reproduce. A team at the Danforth Plant Science Center in St. Louis will use research generated from these groups to build open-source phenotype data sets that scientists around the world can use in their own work.
Drone phenotyping projects could eliminate the phenotyping bottleneck, Spalding says, but doing so will require plant researchers to join forces with experts who can help sift through the data.
“My sense is that once [researchers] get good at flying their sensors, they’re going to realize that they have a major computational task ahead of them,” Spalding says. “Computing on all this data is not a desktop scenario.”