Currently, farmers can use precision ag to construct 2-D images or even 3-D reconstructions of their fields. But a collaborative research project from three Georgia institutions wants to take monitoring abilities into the fourth dimension.
Currently, farmers can use precision ag to construct 2-D images or even 3-D reconstructions of their fields. But a collaborative research project from three Georgia institutions wants to take these monitoring tools into the fourth dimension.
4-D is simply 3-D plus time, explains Jing Dong, Ph.D. student with Georgia Tech Research Institute.
“Of course, crops are constantly growing, moving in the wind, changing color,” he says. “[This] makes it very difficult to automate the precise alignment of static images over time. What we have been able to do is account for the dynamic nature of continuously growing crops and animate a whole growing season’s worth of 3-D images into a 4-D reconstruction that reveals a bounty of useful information to farmers and other precision agricultural systems.”
Dong and other researchers from Georgia Tech Research Institute, the University of Georgia and the Georgia Institute of Technology equipped a standard tractor with sensors to track color imaging, GPS, inertial measurement and other data from a peanut field in Tifton, Ga. Researchers ran 23 sessions in 89 days collecting a total of 36 million data points during the 2016 growing season.
Their output is called a “4-D point cloud,” which is a series of 3-D point clouds aligned into a single coordinate frame.
“With this, we can visualize multiple types of output like height curves, growth rate heat maps and detailed local mesh models that are accessible by farmers or other precision agriculture systems,” Dong says. “All of this information is very useful for making decisions about irrigation, pest control, harvesting, crop rotations and much more.”
Researchers presented their paper at the 2017 IEEE International Conference on Robotics and Automation earlier this year in Singapore.