Automated computed tomography based parasitoid detection in mason bee rearings.
In recent years, insect husbandry has seen an increased interest in order to supply in the production of raw materials, food, or as biological/environmental control. Unfortunately, large insect rearings are susceptible to pathogens, pests and parasitoids which can spread rapidly due to the confined...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0275891 |
| _version_ | 1797985864754659328 |
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| author | Bart R Thomson Steffen Hagenbucher Robert Zboray Michelle Aimée Oesch Robert Aellen Henning Richter |
| author_facet | Bart R Thomson Steffen Hagenbucher Robert Zboray Michelle Aimée Oesch Robert Aellen Henning Richter |
| author_sort | Bart R Thomson |
| collection | DOAJ |
| description | In recent years, insect husbandry has seen an increased interest in order to supply in the production of raw materials, food, or as biological/environmental control. Unfortunately, large insect rearings are susceptible to pathogens, pests and parasitoids which can spread rapidly due to the confined nature of a rearing system. Thus, it is of interest to monitor the spread of such manifestations and the overall population size quickly and efficiently. Medical imaging techniques could be used for this purpose, as large volumes can be scanned non-invasively. Due to its 3D acquisition nature, computed tomography seems to be the most suitable for this task. This study presents an automated, computed tomography-based, counting method for bee rearings that performs comparable to identifying all Osmia cornuta cocoons manually. The proposed methodology achieves this in an average of 10 seconds per sample, compared to 90 minutes per sample for the manual count over a total of 12 samples collected around lake Zurich in 2020. Such an automated bee population evaluation tool is efficient and valuable in combating environmental influences on bee, and potentially other insect, rearings. |
| first_indexed | 2024-04-11T07:23:47Z |
| format | Article |
| id | doaj.art-e8de59ef364e457bac1de7806e87b531 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T07:23:47Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-e8de59ef364e457bac1de7806e87b5312022-12-22T04:37:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710e027589110.1371/journal.pone.0275891Automated computed tomography based parasitoid detection in mason bee rearings.Bart R ThomsonSteffen HagenbucherRobert ZborayMichelle Aimée OeschRobert AellenHenning RichterIn recent years, insect husbandry has seen an increased interest in order to supply in the production of raw materials, food, or as biological/environmental control. Unfortunately, large insect rearings are susceptible to pathogens, pests and parasitoids which can spread rapidly due to the confined nature of a rearing system. Thus, it is of interest to monitor the spread of such manifestations and the overall population size quickly and efficiently. Medical imaging techniques could be used for this purpose, as large volumes can be scanned non-invasively. Due to its 3D acquisition nature, computed tomography seems to be the most suitable for this task. This study presents an automated, computed tomography-based, counting method for bee rearings that performs comparable to identifying all Osmia cornuta cocoons manually. The proposed methodology achieves this in an average of 10 seconds per sample, compared to 90 minutes per sample for the manual count over a total of 12 samples collected around lake Zurich in 2020. Such an automated bee population evaluation tool is efficient and valuable in combating environmental influences on bee, and potentially other insect, rearings.https://doi.org/10.1371/journal.pone.0275891 |
| spellingShingle | Bart R Thomson Steffen Hagenbucher Robert Zboray Michelle Aimée Oesch Robert Aellen Henning Richter Automated computed tomography based parasitoid detection in mason bee rearings. PLoS ONE |
| title | Automated computed tomography based parasitoid detection in mason bee rearings. |
| title_full | Automated computed tomography based parasitoid detection in mason bee rearings. |
| title_fullStr | Automated computed tomography based parasitoid detection in mason bee rearings. |
| title_full_unstemmed | Automated computed tomography based parasitoid detection in mason bee rearings. |
| title_short | Automated computed tomography based parasitoid detection in mason bee rearings. |
| title_sort | automated computed tomography based parasitoid detection in mason bee rearings |
| url | https://doi.org/10.1371/journal.pone.0275891 |
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