Unique imaging capabilities yield new knowledge, growth for bioeconomy


Scientists at the Department of Energy’s Oak Ridge National Laboratory have a powerful new tool in the quest to produce better plants for biofuels, bioproducts and agriculture.

A system of sophisticated robotics and sensors in the lab’s new Advanced Plant Phenotyping Laboratory, or APPL, collect an unprecedented amount of data on plant characteristics called phenotypes.

The automated system propels 500 large plants along more than 700 feet of track, sending them on a winding path through the imaging stations. During a typical experiment, the system collects terabytes of data, presenting both a challenge and tremendous opportunities for discovery.

Among its many capabilities, APPL allows scientists to investigate sustainability traits in poplar and switchgrass and to examine how those traits translate across scales, from the molecular to the cellular to the whole plant and, ultimately, to the ecosystem. These novel capabilities will also enable researchers to identify the impacts of microbes on plant growth and productivity.

APPL offers the world’s most diverse suite of imaging capabilities for plant phenotyping. An array of five imaging stations provides RGB (red-green-blue), hyperspectral, thermal, fluorescence and 3D laser imaging that captures plant growth, photosynthetic activity, changes in water and nitrogen distribution and many other characteristics.

“We’re fortunate that we’re located at ORNL,” said Jerry Tuskan, director of the DOE Center for Bioenergy Innovation. “We’ve got experts developing machine learning algorithms and using the laboratory’s supercomputing resources to combine these data into a 3D visualization of each plant that reveals its structural and chemical properties in high-resolution detail.”

To achieve this level of detail, the phenotyping system must capture images with the extreme precision that only automation can provide.

“We have the opportunity to visualize, across various spectra, plants as they develop and grow under normal and stressed conditions,” Tuskan said. “And we anticipate seeing cues and signals we’ve never been able to detect before, indicating shifts in plants’ physiology and morphology due to conditions like drought stress or pathogen attacks.”

“This potential to predict changes before they are visually apparent is exciting,” he continued. “This is unexplored territory.”

The establishment of APPL was supported through Institutional General Plant Project funding at ORNL.

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