UPCOMING WEBINARS
Past Webinars
Deep learning and computer vision will transform entomology Toke Hoye (Department of Bioscience and Arctic Research Centre, Aarhus University, Ronde, Denmark) Date: Tuesday, December 1, 2020 Host: Neil Cobb (neil.cobb@nau.edu or neilscobb@gmail.com) Summary: Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and non-invasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the lab. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behaviour, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of arthropods.
Bee ID Guides in DiscoverLife September 17, 2020; Sam Droege USGS – Native Bee Inventory and Monitoring Program
iNat in Your Specimen Data November 5, 2020; Mason Heberling and Bonnie Isaac (Carnegie Museum)