The hardware, in this case, is a robotic device that is mounted on wheels. The device is steered manually and is narrow enough to navigate between crop rows that are spaced 30 inches apart — the standard width used by farmers. The device itself consists of four tiers of cameras, each of which is set to a different height to capture a different level of leaves on the surrounding plants. Each tier includes two cameras, allowing it to capture a stereoscopic view of the leaves and enable 3D modeling of plants.
As the device is steered down a row of plants, it is programmed to capture multiple stereoscopic images, at multiple heights, of every plant that it passes.
All this visual data is fed into a software program that computes the leaf angle for the leaves of each plant at different heights.
"For plant breeders, it's important to know not only what the leaf angle is, but how far those leaves are above the ground," Xiang says. "This gives them the information they need to assess the leaf angle distribution for each row of plants. This, in turn, can help them identify genetic lines that have desirable traits — or undesirable traits."
To test the accuracy of AngleNet, the researchers compared leaf angle measurements done by the robot in a corn field to leaf angle measurements made by hand using conventional techniques.
"We found that the angles measured by AngleNet were within five degrees of the angles measured by hand, which is well within the accepted margin of error for purposes of plant breeding," Xiang says.
"We're already working with some crop scientists to make use of this technology, and we're optimistic that more researchers will be interested in adopting the technology to inform their work. Ultimately, our goal is to help expedite plant breeding research that will improve crop yield."
Adds Robert Fleischmann, a program director in NSF's Division of Biological Infrastructure, "NSF investments through its Major Research Instrumentation program lead to advances like this one in sensing and robotics technology that impact real-world outcomes in farming, plant breeding and crop production."