Drone Data Provides Early Identification Of Southern Rust In Corn


Texas A&M AgriLife algorithms can help producers prevent economic damage.

Texas A&M AgriLife researchers discovered they can predict corn southern rust epidemic outbreaks by utilizing unmanned aerial systems, UAS, or drones, early enough to help prevent economic damage for growers.

The paper outlining the work was recently selected for publication by Scientific Reports. The lead author was Aaron DeSalvio, a Department of Soil and Crop Sciences graduate student in the Genetics and Genomics program at Texas A&M University.

Leadership for the project was provided by Seth Murray, Texas A&M AgriLife Research corn breeder and Eugene Butler Endowed Chair in the Department of Soil and Crop Sciences, and Tom Isakeit, Texas A&M AgriLife Extension Service plant pathologist in the Department of Plant Pathology and Microbiology. Other contributors included post doctorate researcher Alper Adak, who helped analyze data, and Scott Wilde, who helped with drone flights.

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