Corn is one of the three major cereal crops in the world, and given its importance, farmers utilize every tool and technique at their disposal to maximize crop yields.
The angles at which the leaves protrude from the corn stalk are integral to light interception and photosynthetic efficiency. Leaf angle has traditionally been measured manually, using a common tool from geometry class. Unfortunately, this methodology is generally considered by farmers to be both slow and labor intensive.
Looking to improve on this process, a team of researchers from North Carolina State University and Iowa State University has developed an automated technology capable of accurately measuring the angle of leaves on corn plants in the field. This technology, named AngleNet, makes data gathering on leaf angles more efficient than typical techniques, providing plant breeders with useful data in a speedier fashion.
“The angle of a plant’s leaves, relative to its stem, is important because the leaf angle affects how efficient the plant is at performing photosynthesis,” said Dr. Lirong Xiang, assistant professor of biological and agricultural engineering at NC State. “For example, in corn, you want leaves at the top that are relatively vertical but leaves further down the stalk that are more horizontal. This allows the plant to harvest more sunlight.
“Researchers who focus on plant breeding monitor this sort of plant architecture, because it informs their work. However, standard methods for measuring leaf angles involve measuring leaves by hand with a protractor – which is both time consuming and laborious,” Xiang said. “We wanted to find a way to automate this process – and we did.”
AngleNet comprises two key components: its hardware and its software.
The PhenoBot at work in the field. Its aim is to help plant breeders create more efficient corn plants. Photo courtesy of NCSU
The hardware consists of a robotic device named the PhenoBot 3.0. The PhenoBot moves about on four wheels, is steered manually and is narrow enough to navigate between 30-inch crop rows. Affixed to the PhenoBot at different heights are cameras that can photograph different levels of leaves on the plants it passes. Each tier includes two cameras, which allows it to capture a stereoscopic view of the leaves and enable 3-D modeling of plants.
The PhenoBot is programmed to capture multiple stereoscopic images, at multiple heights, of every plant in its field of view. After collection, the footage is input into a software program that computes the leaf angle for the leaves of each plant.
“For plant breeders, it’s important to know not only what the leaf angle is, but how far those leaves are above the ground,” said Xiang. “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.”
In an effort to gauge the accuracy of AngleNet technology, researchers juxtaposed the leaf angle measurements done by the PhenoBot to leaf angle measurements made by hand using conventional techniques. According to Xiang, the PhenoBot’s performance was rated a success.
“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,” she said.
She added that implementation of the AngleNet technology out in the field is already underway.
“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,” she added.
For more information visit bae.ncsu.edu/people/lxiang3.
by Enrico Villamaino
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