Can machine learning be used for accurate species identification of beetles and other invertebrates? Researchers using carabid beetle data from the National Ecological Observatory Network created an algorithm that reached 85 percent accuracy on unidentified images. Eventually, they hope machine learning could one day be used to classify unidentified species in NEON bycatch and answer new questions about invertebrate diversity and abundance across North America.
With multiple species of termites responsible for structural damage in the U.S., rapid identification is a critical part of management efforts. A team of researchers has developed a faster ID method that uses a genetic tool called inter-simple sequence repeats.
The Rasopone genus of tropical leaf-litter ants gets a thorough taxonomic revision, and the researchers behind the long-term project present their identification manual in a "bird guide" format rather than the traditional dichotomous key.
Meet Michael Skvarla, Ph.D., extension entomologist and director of the Insect Identification Laboratory at Penn State University, whose career path began with a Cub Scout insect-collection project. Skvarla is the subject of the next edition of our "Standout Early Career Professionals" series.
So, you want to know what that bug is. Here at the Entomological Society of America, we know the experts. Check out this list for a variety of resources for bug and insect identification.