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Crop-Counting, Gene-Finding Robot Wins Systems Paper Award

BioPhotonics
Sep 2018
A robot developed by the University of Illinois to find genes for high-yielding, hearty traits in the DNA of crop plants was recognized with the best systems paper award at the Robotics: Science and Systems conference.

Developed by the University of Illinois, the TerraSentia robot that autonomously monitors crops earned the best systems paper award at Robotics: Science and Systems, the preeminent robotics conference held in Pittsburgh. Courtesy of TERRA-MEPP Project.
Developed by the University of Illinois, the TerraSentia robot that autonomously monitors crops earned the best systems paper award at Robotics: Science and Systems, the preeminent robotics conference held in Pittsburgh. Courtesy of TERRA-MEPP Project.

"There's a real need to accelerate breeding to meet global food demand," said principal investigator Girish Chowdhary, an assistant professor of field robotics at Illinois. "In Africa, the population will more than double by 2050, but today the yields are only a quarter of their potential."

Crop breeders run massive experiments comparing thousands of different varieties of crops over hundreds of acres and measure key traits, such as plant emergence or height, by hand. The task is expensive, time-consuming, inaccurate, and ultimately inadequate – a team can only manually measure a fraction of plants in a field.

"The lack of automation for measuring plant traits is a bottleneck to progress," said the paper’s first author, Erkan Kayacan, now a postdoctoral researcher MIT. "But it's hard to make robotic systems that can count plants autonomously: the fields are vast, the data can be noisy, unlike benchmark data sets, and the robot has to stay within the tight rows in the challenging under-canopy environment."

Illinois' 13-in. wide, 24-lb TerraSentia robot is transportable, compact, and autonomous. It captures each plant from top to bottom using a suite of sensors, algorithms, and deep learning. Using a transfer learning method, the researchers taught TerraSentia to count corn plants with just 300 images.

"One challenge is that plants aren't equally spaced, so just assuming that a single plant is in the camera frame is not good enough," said co-author ZhongZhong Zhang, a graduate student in the College of Agricultural Consumer and Environmental Science at Illinois. "We developed a method that uses the camera motion to adjust to varying interplant spacing, which has led to a fairly robust system for counting plants in different fields, with different and varying spacing, and at different speeds."

The work was supported by the Advanced Research Project Agency-Energy as part of the TERRA-MEPP project at the Carl R. Woese Institute for Genomic Biology. The robot is now available through the startup company EarthSense Inc., which is equipping the robot with advanced autonomy and plant analytics capabilities.

BusinessUniversity of IllinoisUrbana-ChampaignSensors & DetectorsimagingroboticsGirish ChowdharycropsErkan KayacanZhongZhong ZhangBiophotonicsAmericasrobotsagricultureRapidScan

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