EntoScan and BEEomass: a standardized imaging system and a physically motivated model for high-throughput dry biomass estimation of arthropods

Melika Baghooee , Robert Thalheim, Fevziye Hasan , Søren Toft, Torsten Nygård Kristensen, Quentin Geissmann

Computer vision and AI are now widely used for automated insect classification, but their potential for estimating other traits, such as biomass, is not yet fully explored. Insect biomass is a key measure of ecosystem function, informing ecosystem services, food webs, and environmental change. It is also used to track population trends and estimate the contribution of insects to ecosystem carbon. Currently, wet bulk biomass is used as a standard measure for insect monitoring because it is fast and practical. However, bulk biomass conflates individual contributions, obscuring biologically meaningful variation. Obtaining individual-level biomass is challenging as each specimen must be dried and weighed, making large-scale measurements practically intractable. Here, we propose EntoScan, an open-source, low-cost and standardized imaging system based on a modified flatbed scanner and Biomass Estimation in Entomology (BEEomass), a novel computer vision model that estimates dry biomass of terrestrial arthropods from images alone. BEEomass uses a physically motivated, scale-invariant representation of body size and mass that allows it to generalize across taxa differing widely in morphology. We show that this image-based approach provides accurate dry biomass estimates (R² > 0.95) while considerably reducing both the manual effort and specimen destruction compared to direct weighing. We then illustrate the practical utility of EntoScan and BEEomass through two proof-of-concept case studies: tracking temperature-induced size variation in laboratory-reared Drosophila and monitoring seasonal biomass dynamics in wild-caught Pachygnatha degeeri. Because they are affordable, scalable, and easy to use, our two methods have the potential to serve as standardized tools for monitoring insect biomass at scale, and ultimately improve our ability to quantify and predict ecosystem functions and services, supporting more effective conservation and agricultural management.