Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale.

ABSTRACT: Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large-scale insect identification projects are time-consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.Here, we outline a methodology for training classification models to identify pitfall trap-collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, dat

SUBMITTER: Blair J 

PROVIDER: S-EPMC7713910 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature