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Robotics Research
Posted on July 04, 2018

Crop-counting robot earns top recognition

Structural Electronics 2018-2028
Today's crop breeders are trying to boost yields while also preparing crops to withstand severe weather and changing climates. To succeed, they must locate genes for high-yielding, hardy traits in crop plants' DNA. A robot developed by the University of Illinois to find these proverbial needles in the haystack was recognized by the best systems paper award at Robotics: Science and Systems, robotics conference held last week in Pittsburgh. For more information see the IDTechEx report on agricultural robots and drones.
"There's a real need to accelerate breeding to meet global food demand," said principal investigator Girish Chowdhary, an assistant professor of field robotics in the Department of Agricultural and Biological Engineering and the Coordinated Science Lab 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 cultivars, or varieties, of crops over hundreds of acres and measure key traits, like 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 first author Erkan Kayacan, now a postdoctoral researcher at the Massachusetts Institute of Technology. "But it's hard to make robotic systems that can count plants autonomously: the fields are vast, the data can be noisy (unlike benchmark datasets), and the robot has to stay within the tight rows in the challenging under-canopy environment."
Electric Vehicles and Autonomous Vehicles in Minin
Illinois' 13-inch wide, 24-pound TerraSentia robot is transportable, compact and autonomous. It captures each plant from top to bottom using a suite of sensors (cameras), 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 (ACES). "We developed a method that uses the camera motion to adjust to varying inter-plant spacing, which has led to a fairly robust system for counting plants in different fields, with different and varying spacing, and at different speeds."
Source: Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign
Top image: Terra-Mepp Project
Learn more at the next leading event on the topic: Sensors USA 2018 External Link on 14 - 15 Nov 2018 at Santa Clara Convention Center, CA, USA hosted by IDTechEx.