NextGen Collecting & Automating Research-Ready Data
Advances in 3D printing, sensors, computer vision and deep learning provide 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.
This webinar was presented by Toke Hoye on December 1, 2020.
An Automated Light Trap to Monitor Moths
Deep learning and computer vision will transform entomology