A robot for measuring the angles of corn stalk leaves may sound like a ridiculously niche invention, but it’s a device with potentially major benefits for farmers. As detailed in a paper recently published in the Journal of Field Robotics, researchers from Iowa State University and North Carolina State University have designed an autonomous wheeled device narrow enough to move between corn rows spaced a standard 30 inches apart. As the robot traverses a field, its four tiers of dual cameras take an array of photos to allow a stereoscopic view for 3D plant modeling via a separate software program.
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When it comes to corn, the curves and angles are important, as far as the leaves are concerned. In relation to the stalk itself, the crop’s leaves ideally will angle upwards at the top before bending more horizontally as they progress lower, thus allowing optimal sunlight harvesting for photosynthesis. Unfortunately, measuring this attribute—important to optimizing future crop generations—is a painstakingly slow and rudimentary chore for farmers, who often resort to hand measurements via basic protractors.
Enter onto the field AngleNet, the name given to the two part robot-software system.
In a press statement on Tuesday, Lirong Xiang, the paper’s first author, as well as assistant professor of biological and agricultural engineering at North Carolina State University, explained that, “For plant breeders, it’s important to know not only what the leaf angle is, but how far those leaves are above the ground. 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.”
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Researchers also found that AngleNet measured corn stalk leaves’ angles within 5 degrees of those measured by hand, or “well within the accepted margin of error for purposes of plant breeding,” Xiang said.
It may not seem like it at first thought, but the agricultural industry is often home to extremely advanced automation technologies—albeit not without their own controversies and concerns. Moving forward, however, researchers hope to further optimize AngleNet’s algorithms for even more precise measurements, as well as work alongside other crop scientists to utilize the technology. By deploying the system in the real world, the team also hopes to speed plant breeding research to eventually improve farmers’ future crop yields.
Correction 3/8/23: An earlier version of this story incorrectly stated that the University of Iowa participated in this research. We regret the error.
