Multisensor data fusion for automatized insect monitoring (KInsecta)

Martin Tschaikner; Danja Brandt; Henning Schmidt; Felix Bießmann; Teodor Chiaburu; Ilona Schrimpf; Thomas Schrimpf; Alexandra Stadel; Frank Haußer; Ingeborg Beckers

doi:10.1117/12.2679927

17 October 2023 Multisensor data fusion for automatized insect monitoring (KInsecta)

Martin Tschaikner, Danja Brandt, Henning Schmidt, Felix Bießmann, Teodor Chiaburu, Ilona Schrimpf, Thomas Schrimpf, Alexandra Stadel, Frank Haußer, Ingeborg Beckers

Author Affiliations +

Proceedings Volume 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV; 1272702 (2023) https://doi.org/10.1117/12.2679927
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

Insect populations are declining globally, making systematic monitoring essential for conservation. Most classical methods involve death traps and counter insect conservation. This paper presents a multisensor approach that uses AI-based data fusion for insect classification. The system is designed as low-cost setup and consists of a camera module and an optical wing beat sensor as well as environmental sensors to measure temperature, irradiance or daytime as prior information. The system has been tested in the laboratory and in the field. First tests on a small very unbalanced data set with 7 species show promising results for species classification. The multisensor system will support biodiversity and agriculture studies.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Martin Tschaikner, Danja Brandt, Henning Schmidt, Felix Bießmann, Teodor Chiaburu, Ilona Schrimpf, Thomas Schrimpf, Alexandra Stadel, Frank Haußer, and Ingeborg Beckers "Multisensor data fusion for automatized insect monitoring (KInsecta)", Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 1272702 (17 October 2023); https://doi.org/10.1117/12.2679927

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