Funded by the Illinois Soybean Association checkoff program.

LEARN

USING MULTISPECTRAL PLATFORMS TO MANAGE THE SOYBEAN CYST NEMATODE

Dr. Jason Bond
Professor of Plant Pathology
Southern Illinois University
618-453-4309
jbond@illinois.edu

How can we gain a better understanding of what soybean cyst nematode (SCN) is doing in a soybean field without collecting more time- and cost-intensive soil samples?
How can we use technology to better detect infestation levels of SCN and help guide management decisions?

SCN has earned the title of #1 soybean pathogen not just because of the economic loss it inflicts, but also because of its adaptability in overcoming varietal resistance. Current monitoring methods tend to lack accuracy and efficiency at the field and farm level. In addition, crop damage thresholds are difficult to establish.
While remote sensing technology is becoming more common in detecting crop diseases, little testing has been done in soybeans. This multi-year research project has already discovered that drone-based remote sensing provides cost-effective, reliable monitoring of SCN damage. It also identified Red and Near Infrared bands as most sensitive to SCN infestation and gained a better understanding of the sensitivity of different vegetation indices to SCN infestation and crop yield.
However, more research is needed to understand how soybean phenology impacts different levels of SCN infestation. Collected data will be used to develop a set of algorithms and tools farmers can use to detect, monitor and manage SCN.

By advancing technology capabilities, farmers will have a more clear and specific view of SCN population levels, activity and density across their fields. This will allow them to be more precise in their management of SCN.

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