Laura S. Marshall, email@example.com
WAGENINGEN, Netherlands – Machine vision technologies can help
improve food production right at its roots by helping farmers identify and kill
weeds in their fields. Researchers at Wageningen University have developed a system
that automatically does just that; such a system could help raise productivity,
lower costs and protect the environment.
The researchers, who work in the university’s Farm Technology
Group, set out to develop a system that would recognize and attack a weed called
the wild potato, which can be a nuisance to sugar beet farmers. When crops are rotated
from potatoes to beets, the leftover potato plants that grow back wild during sugar
beet years release nematodes and also can spread diseases across the field.
The weed-fighting system can distinguish between intentionally cultivated crops, in
green, and weeds, in red. Images courtesy of Wageningen University.
A home garden hobbyist can dig up wild potatoes by hand, but in
a large multiacre field, this is impractical, if not impossible. Large-scale beet
farmers often must turn to sprayed herbicides, which naturally will come into contact
with the beets as well as the potatoes.
The researchers’ aim was to automatically distinguish the
potato weeds from the beet plants, so they turned to machine vision. Dutch imaging
specialist Data Vision, a sales partner of Allied Vision Technologies (AVT) in the
Benelux countries, helped the team design a portable scanner system that can be
towed over a field by a tractor. When the scanner spots wild potatoes, a microsprayer
releases herbicide over that area. This eliminates the need for spraying chemicals
blindly over large patches of ground, cutting down on the farmer’s herbicide
expenses and reducing the amount of pesticide sprayed onto the beet crop.
The team knew that the image analysis system had to be adaptive.
“On a mechanically planted field, the path of the furrows is a clearly defined
constant,” said researcher Dr. Ard Nieuwenhuizen. “Anything growing
between the furrows can only be weed.” But weeds also can grow right from
the furrows themselves, sneaking in between the intended cultivated plants.
Shown is a schematic of the weed-fighting system.
So the researchers taught the software to tell the difference
– in terms of IR properties and colors – between sugar beets and wild
potatoes. Two Marlin F-201 industrial digital cameras from AVT – each equipped
with 2-megapixel sensors, one in color and the other an IR-sensitive monochrome
sensor with a 780-nm IR pass filter – distinguish the plants from the earth
and then identify each as either weed or crop.
Five Xenon lamps illuminate the ground below the unit, and a distance-measurement
device on one of the trailer’s wheels pinpoints the image’s location.
National Instruments hardware (NI PXI system with Virtex-5 FPGA) and software (NI
LabView) are used to capture and analyze the images.
The system homes in on plants that have grown outside the furrow
but then also looks for possible weeds within the furrow. And it recalibrates itself
every 10 m so that it compares adjacent plants only to each other: Nieuwenhuizen
noted that, in nature, variations in ground properties including water and nitrogen
content can result in varying color properties even within the same species of plant.
When the system confirms the presence of a weed, it deploys the
microsprayer, which dispenses herbicide in 5-µl-drop increments directly onto the
wild potato’s leaves with a precision of ±15 mm.
Although the team has declared the prototype a success, the system
needs further development before it can be marketed and distributed commercially.
The researchers want to teach it to fight various kinds of weeds. To address the
needs and wishes of organic farmers, another research team at Wageningen University
and Research Center is working on another weed-recognition system that will mechanically
remove invasive plants instead of spraying them.