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An Open-Source, AI-Based Insect Monitoring Station

In: Advances and New Trends in Environmental Informatics

Author

Listed:
  • Christopher Galle

    (University of Applied Sciences Trier, Environmental Campus Birkenfeld)

  • Lara Hoffmann

    (University of Applied Sciences Trier, Environmental Campus Birkenfeld
    University of Duisburg-Essen, Faculty of Biology)

  • Stefan Stoll

    (University of Applied Sciences Trier, Environmental Campus Birkenfeld
    University of Duisburg-Essen, Faculty of Biology)

  • Stephan Didas

    (University of Applied Sciences Trier, Environmental Campus Birkenfeld)

Abstract

This study introduces a low-cost, open-source prototype for a non-invasive insect monitoring station that combines a Malaise trap with a camera module. The system has to date obtained a dataset of more than 1.4 million insect images. An automated preprocessing pipeline has been developed to filter and extract relevant frames, which have been annotated to address the critical lack of high-quality training data in AI-driven ecological monitoring. Leveraging this dataset, we have developed and trained a YOLOv9-based classification model that has achieved high detection accuracy and has demonstrated robust performance, even when trained on relatively small subsets. The classifier currently distinguishes between 14 insect taxa and can be easily extended to include additional groups. All hardware schematics, software code, and training data will be made freely available. Beyond taxonomic identification, the monitoring station supports near-real-time classification and will prospectively enable temporal analysis of insect activity patterns.

Suggested Citation

  • Christopher Galle & Lara Hoffmann & Stefan Stoll & Stephan Didas, 2026. "An Open-Source, AI-Based Insect Monitoring Station," Progress in IS, in: Volker Wohlgemuth & Stefan Naumann & Grit Behrens & Anna Zagorski & Maximilian Höb (ed.), Advances and New Trends in Environmental Informatics, pages 55-71, Springer.
    • Handle: RePEc:spr:prochp:978-3-032-22726-3_4
    DOI: 10.1007/978-3-032-22726-3_4
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