, doi: 10.3897/arphapreprints.e213643
From collection trays to AI-ready data: An operational framework for automated batch entomological specimen processing
Alyson East‡§, S M Rayeed|, Elizabeth G. Campolongo¶, Nathan Cain#, Chandra Earl¤, Michael Belitz«, Isabelle Betancourt», Eric R Sokol˄, Leah Cotton˅, Jacqueline Dominguez˅, Brennan Hays˅, Fangxun Liu#, Samuel Stevens#, Charles Stewart¦, Tanya Berger-Wolf#¶, Wei-Lun Chaoˀ, Hilmar Lappˁ, Sydne Record‡§‡ Department of Wildlife, Fisheries, and Conservation Biology, University of Maine, Orono, ME, United States of America§ Maine Agriculture and Forest Experiment Station, University of Maine, Orono, ME, United States of America| Rensselaer Polytechnic Institute, Troy NY, United States of America¶ Imageomics Institute & ABC Global Center, The Ohio State University, Columbus OH, United States of America# Computer Science and Engineering, The Ohio State University, Columbus OH, United States of America¤ National Ecological Observatory Network (NEON) Biorepository, Arizona State University, Tempe, AZ, United States of America« Entomology, University of Wisconsin-Madison, Madison WI, United States of America» Arizona State University, Tempe, United States of America˄ National Ecological Observatory Network (NEON), Battelle, Boulder, CO, United States of America˅ School of Life Sciences, Arizona State University, Tempe, AZ, United States of America¦ Computer Science, Rensselaer Polytechnic Institute, Troy NY, United States of Americaˀ Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States of Americaˁ Neuromatch Inc., Beaverton, OR, United States of America
Corresponding author: Alyson East ( alyson.east@maine.edu ) Corresponding author: Sydne Record ( srecord@brynmawr.edu ) © Alyson East, S M Rayeed, Elizabeth Campolongo, Nathan Cain, Chandra Earl, Michael Belitz, Isabelle Betancourt, Eric Sokol, Leah Cotton, Jacqueline Dominguez, Brennan Hays, Fangxun Liu, Samuel Stevens, Charles Stewart, Tanya Berger-Wolf, Wei-Lun Chao, Hilmar Lapp, Sydne Record. This is an open access preprint distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Citation:
East A, Rayeed SM, Campolongo EG, Cain N, Earl C, Belitz M, Betancourt I, Sokol E, Cotton L, Dominguez J, Hays B, Liu F, Stevens S, Stewart C, Berger-Wolf T, Chao W-L, Lapp H, Record S (2026) From collection trays to AI-ready data: An operational framework for automated batch entomological specimen processing. ARPHA Preprints. https://doi.org/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.
Keywords
Computer vision, Foundation models, object detection, Image segmentation, Natural history collections, Biodiversity informatics, Carabidae (ground beetles), Darwin Core, Collection digitization, FAIR for AI