Illinois' crop-counting robot earns top recognition at leading robotics conference

July 2, 2018, University of Illinois at Urbana-Champaign
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. Credit: TERRA-MEPP Project

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, the preeminent robotics conference held last week in Pittsburgh.

"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 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 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."

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. Credit: TERRA-MEPP Project

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, as reported at this conference.

"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."

Explore further: Ag robot speeds data collection, analyses of crops as they grow

More information: Erkan Kayacan et al, High-precision control of tracked field robots in the presence of unknown traction coefficients, Journal of Field Robotics (2018). DOI: 10.1002/rob.21794

Related Stories

New 3-D model predicts best planting practices for farmers

June 23, 2017

As farmers survey their fields this summer, several questions come to mind: How many plants germinated per acre? How does altering row spacing affect my yields? Does it make a difference if I plant my rows north to south ...

Breeding better Brazilian rice

June 6, 2018

Outside Asia, no other country produces as much rice as does Brazil. It is the ninth largest rice producer in the world. Average annual yields are close to 15 million tons.

Recommended for you

Team breaks world record for fast, accurate AI training

November 7, 2018

Researchers at Hong Kong Baptist University (HKBU) have partnered with a team from Tencent Machine Learning to create a new technique for training artificial intelligence (AI) machines faster than ever before while maintaining ...

0 comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.