From collection trays to AI-ready data: An operational framework for automated batch entomological specimen processing

East, Alyson & Rayeed, S M & Campolongo, Elizabeth & Cain, Nathan & Earl, Chandra & Belitz, Michael & Betancourt, Isabelle & Sokol, Eric & Cotton, Leah & Dominguez, Jacqueline & Hays, Brennan & Liu, Fangxun & Stevens, Samuel & Stewart, Charles & Berger-Wolf, Tanya & Chao, Wei-Lun & Lapp, Hilmar & Record, Sydne. 2026. DOI:10.3897/arphapreprints.e213643

Abstract

Natural history collections house over three billion specimens critical to biodiversity research, yet fewer than 2% of North American arthropod specimens have been imaged, creating a bottleneck for trait-based analyses at broad temporal and spatial scales. Batch photography of multiple specimens improves throughput, but linking detected individuals to database records at scale remains an unsolved operational challenge. The National Ecological Observatory Network (NEON) collects specimens from across the US alongside co-located biotic and abiotic data providing an unprecedented opportunity to explore how digitization efforts can be linked to extended specimen data across space and time. Notably NEON’s curation practice of ordering specimens logically in alignment with metadata enables linking image regions to individual metadata from images containing multiple specimens in a unit tray. We present an accessible framework for high-throughput batch imaging and individual specimen extraction from pinned entomological collections, demonstrated on 65,236 ground beetles (Carabidae) from the NEON Biorepository. The workflow integrates standardized tray photography using consumer equipment, redundant metadata capture by trained technicians, automated validation against the NEON database, and foundation model-based detection and segmentation (Grounding DINO, SAM) to produce individual specimen images with validated metadata linkages. Automated logical queries successfully linked specimens in 78% of trays; the remaining 23% were resolved through structured manual review. The iterative detection pipeline achieved 96% tray-level accuracy across 3,649 trays spanning 518 species. Additionally, this pipeline enables the detection of minor curatorial errors providing an opportunity for imaging and metadata linkage efforts to give back to the collections housing specimens by flagging areas in need of curatorial attention. Complete code, documentation, and standard operating procedures are provided.