A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris)
Entomologists have widely applied re-identification techniques to better understand insects and their interaction with the environment. While humans can re-identify other humans and some mammals quite well, entomologists rely on gluing markers on insects to perform this task. This paper presents an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375522000995 |
| _version_ | 1811232828688433152 |
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| author | Parzival Borlinghaus Frederic Tausch Luca Rettenberger |
| author_facet | Parzival Borlinghaus Frederic Tausch Luca Rettenberger |
| author_sort | Parzival Borlinghaus |
| collection | DOAJ |
| description | Entomologists have widely applied re-identification techniques to better understand insects and their interaction with the environment. While humans can re-identify other humans and some mammals quite well, entomologists rely on gluing markers on insects to perform this task. This paper presents an approach for purely visual re-identification of bumblebees (Bombus terrestris) without the need to use markers. Non-invasive identification methods offer the possibility to observe the interaction of bumblebees with their environment without disturbance. Both a CNN model and a simple body shape model were used to investigate how they can be re-identified within a colony. The best-performing model, BumbleNet, correctly identifies more than two-thirds (CMC-1 score) of the individuals. Bumblebees are known for their substantial variations in body shape. To understand whether other features can also play a role in re-identification, different augmentations are applied during the training of BumbleNet. It was found that non-body-shape features increased the performance of BumbleNet by 25 percentage points (CMC-1 score). This also explains the observed superiority of the CNN-based BumbleNet compared to the BumbleShape model, that is solely based on body size parameters. |
| first_indexed | 2024-04-12T11:09:49Z |
| format | Article |
| id | doaj.art-0ef33909a20a49778ae16c325901e061 |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-04-12T11:09:49Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-0ef33909a20a49778ae16c325901e0612022-12-22T03:35:38ZengElsevierSmart Agricultural Technology2772-37552023-02-013100135A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris)Parzival Borlinghaus0Frederic Tausch1Luca Rettenberger2Institute for Operations Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Corresponding author.apic.ai GmbH, Karlsruhe, GermanyInstitute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyEntomologists have widely applied re-identification techniques to better understand insects and their interaction with the environment. While humans can re-identify other humans and some mammals quite well, entomologists rely on gluing markers on insects to perform this task. This paper presents an approach for purely visual re-identification of bumblebees (Bombus terrestris) without the need to use markers. Non-invasive identification methods offer the possibility to observe the interaction of bumblebees with their environment without disturbance. Both a CNN model and a simple body shape model were used to investigate how they can be re-identified within a colony. The best-performing model, BumbleNet, correctly identifies more than two-thirds (CMC-1 score) of the individuals. Bumblebees are known for their substantial variations in body shape. To understand whether other features can also play a role in re-identification, different augmentations are applied during the training of BumbleNet. It was found that non-body-shape features increased the performance of BumbleNet by 25 percentage points (CMC-1 score). This also explains the observed superiority of the CNN-based BumbleNet compared to the BumbleShape model, that is solely based on body size parameters.http://www.sciencedirect.com/science/article/pii/S2772375522000995Animal re-identificationRe-idBumblebeePrecision beekeeping |
| spellingShingle | Parzival Borlinghaus Frederic Tausch Luca Rettenberger A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris) Smart Agricultural Technology Animal re-identification Re-id Bumblebee Precision beekeeping |
| title | A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris) |
| title_full | A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris) |
| title_fullStr | A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris) |
| title_full_unstemmed | A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris) |
| title_short | A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris) |
| title_sort | purely visual re id approach for bumblebees bombus terrestris |
| topic | Animal re-identification Re-id Bumblebee Precision beekeeping |
| url | http://www.sciencedirect.com/science/article/pii/S2772375522000995 |
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