|Real-time Selective Weed Management|
Researchers from UT's Biosystems Engineering & Environmental Science Department are leading an effort funded by the U.S. Department of Agriculture to reduce herbicide usage and lower agricultural production costs.
Corn, cotton, and peanuts are major crops in the southern US, with herbicides used on more than 90% of their acreage. Current technologies apply herbicides uniformly across the entire field, even though weeds typically have a patchy distribution and are found on less that 40% of the field area.
Since herbicides represent the majority of applied pesticides, reducing herbicide use is critical to reducing overall pesticide use. In addition to ecological concerns, low commodity prices are forcing producers to continuously search for new technologies and production practices that will reduce expenses.
One technology that promises environmental and cost savings is selective herbicide application. Selective sprayers work by using reflected light measurements to differentiate between bare soil or stubble, and growing plants. Herbicide is then precisely applied, resulting in significant material savings, since no spray is wasted on bare soil/stubble.
In contrast to other precision application techniques that depend on application maps and positioning systems, selective sprayers make application decisions during the operation. Because decisions are made in real time, no application map is required. However, a spatial map of sprayed material can be created for use the following season to spatially apply pre-emergence herbicides where the weed density was the greatest during the previous growing season.
Led by John Wilkerson, UT Sensors and Controls Lab researchers have developed a system capable of recording the information needed to generate maps of selective sprayer herbicide applications. The mapping system records data from a selective sprayer while simultaneously logging positional data generated by an external GPS receiver. Collaborators at North Carolina State University, Texas Tech University, and Mississippi State University will be evaluating and helping implement the technology.
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