Insects and other arthropods make up almost 85% of all known animals on the planet, and most existing species are still unknown to science. For centuries, researchers have collected arthropod samples from habitats worldwide, many of which contain thousands of specimens. The time and expertise required to sort through these samples and make identifications are prohibitive. Consequently, hundreds of millions of specimens, representing vast amounts of undiscovered biodiversity, are unstudied and stored away in museums and research collections. There are few scientists capable of working on the backlog of specimens, and therefore, samples continue to sit in storage. This project will develop artificial intelligence-based tools to automate the imaging and identification of arthropods in samples from terrestrial habitats. Researchers will simply pour a raw sample into an imaging system, allowing the computer to image and identify the specimens, thereby greatly minimizing the time and expertise required to process samples. Successful development of this tool will have profound impacts on both ecology and biodiversity sciences, as it will allow researchers to extract more data from samples than previously possible and will unlock tremendous amounts of biodiversity data from the immense backlog of samples. Since many of these samples are decades or centuries old and come from habitats that have degraded or destroyed, identification of specimens will provide data on how biodiversity is