@inproceedings{boettger2026lepmon,
    type = {inproceedings},
    key = {boettger2026lepmon},
    author = {Dennis Böttger and Peter Grobe and Christian Bräunig and Paul Bodesheim and Joachim Denzler and Jorrit van Gils and Julie Koch Sheard and Corinne Jampou and Roel van Klink and Gunnar Brehm},
    title = {LEPMON: Recording the biodiversity of moths (Lepidoptera) with automated cameras and artificial intelligence},
    year = {2026},
    abstract = {Monitoring insect populations has become an urgent priority given the ongoing biodiversity crisis, and the resulting changes to ecosystem services. However, the available data on insect population trends are severely limited in terms of taxonomic, spatial, and temporal resolution, due to the time-consuming nature of insect collection and identification. The goal of LEPMON (LEPidoptera MONitoring) is to develop a powerful, stable and scalable automated nocturnal insect recording system for long-term monitoring and answering ecological questions. The project runs from December 2024 to November 2027. We provide an overview of the entire project, summarizing the original research proposal and current developments as of August 2026. LEPMON uses time-lapse digital photography of nocturnal insects attracted to a white screen using UV light (LepiLED) with a high resolution of 16 px/mm. Artificial intelligence (AI) is applied for automated processing of the collected images. The recording system comprises two different models of Automated Recorders for Nocturnal Insects (ARNIs). The ARNI-Pro is the high-end model with the highest image quality and durable components, designed for professional users. ARNI-CS is the more affordable and portable alternative with slightly reduced image quality, built largely using 3D-printed components. As of August 2026, 67 ARNI-Pros and 34 ARNI-CS's have been installed in the field. The ARNI-Pro models were set up (1) along eight urbanization gradients to test the system’s ability to detect community changes, and (2) in a variety of natural habitats across Germany to capture as many species as possible, ranging from raised bogs in the north to alpine habitats in the south. We also determine technical limits at extreme locations such as forest canopies and tropical environments and shortly assess the project’s risks and exploitation perspectives.  The ARNI-CS models further support the recording of the community composition of nocturnal insects across various habitats in a citizen science context. The images and data generated by the ARNIs are uploaded to a scalable data management platform (LAUP = LEPMON Annotation and Upload Portal). LAUP processes the images and uses AI to enable large-scale object detection and species identification. As accurate AI models require extensive species-labelled training data, large numbers of manually identified images are needed. To obtain these identifications, we involve both taxonomic experts and citizen scientists. We aim to build international collaborations and share knowledge between countries, extending moth monitoring beyond Germany to strengthen LEPMON as a long-term biodiversity monitoring network.},
    groups = {finegrained,lepmon,biodiversity},
    doi = {10.3897/arphapreprints.e214480},
    booktitle = {ARPHA Preprints},
    url = {https://preprints.arphahub.com/article/214480/download/pdf/},
    eprint = {e214480},
    code = {},
    note = {},
}