Drones to seed biofuel crop production boost
Drones could help boost biofuel production through their ability to gather information of sorghum varieties quickly and accurately, providing scientists and researchers with mass amounts of phenotype data. Researchers have created three drones to analyse a field with different varieties of sorghum in an effort to create an algorithm to determine which varieties of sorghum should be reproduced for the most effective production of biofuel. Sorghum requires less water, can withstand unfavourable weather conditions and produces less than half of the greenhouse gas emissions as traditional fuel products.

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.”