Multisensor Data Fusion for Automatized Insect Monitoring (KInsecta)
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914774811410432 |
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| author | Tschaikner, Martin Brandt, Danja Schmidt, Henning Bießmann, Felix Chiaburu, Teodor Schrimpf, Ilona Schrimpf, Thomas Stadel, Alexandra Haußer, Frank Beckers, Ingeborg |
| author_facet | Tschaikner, Martin Brandt, Danja Schmidt, Henning Bießmann, Felix Chiaburu, Teodor Schrimpf, Ilona Schrimpf, Thomas Stadel, Alexandra Haußer, Frank Beckers, Ingeborg |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multisensor Data Fusion for Automatized Insect Monitoring (KInsecta) Tschaikner, Martin Brandt, Danja Schmidt, Henning Bießmann, Felix Chiaburu, Teodor Schrimpf, Ilona Schrimpf, Thomas Stadel, Alexandra Haußer, Frank Beckers, Ingeborg Machine Learning Computer Vision and Pattern Recognition Signal Processing 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. |
| title | Multisensor Data Fusion for Automatized Insect Monitoring (KInsecta) |
| topic | Machine Learning Computer Vision and Pattern Recognition Signal Processing |
| url | https://arxiv.org/abs/2404.18504 |