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Updated: Aug 25, 2025
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A Precise and Autonomous System for the Detection of Insect Emergence Patterns
Published on: January 9, 2019
5.8K
Automated computed tomography based parasitoid detection in mason bee rearings
Bart R Thomson1,2, Steffen Hagenbucher3, Robert Zboray4
1Department of Neurosurgery, Clinical Neuroscience Center, Universitätsspital and University of Zurich, Zurich, Switzerland.
Plos One
|October 13, 2022
Summary
Automated computed tomography rapidly counts bee cocoons, significantly reducing evaluation time. This technology aids in monitoring insect populations and managing rearing health.
Area of Science:
- Entomology
- Agricultural Science
- Biotechnology
Background:
- Insect husbandry is growing for food, materials, and pest control.
- Large insect rearings face risks from pathogens and pests.
- Efficient monitoring of insect populations and health is crucial.
Purpose of the Study:
- To develop an automated computed tomography (CT)-based method for counting insect rearings.
- To compare the efficiency of automated CT counting with manual methods.
- To provide a tool for rapid population assessment in insect husbandry.
Main Methods:
- Utilized computed tomography (CT) for non-invasive, 3D scanning of insect samples.
- Developed an automated algorithm for counting Osmia cornuta cocoons from CT data.
- Compared automated counting time with manual counting time for 12 samples.
Main Results:
- The automated CT method achieved comparable accuracy to manual counting.
- Automated counting averaged 10 seconds per sample, versus 90 minutes manually.
- The study successfully demonstrated an efficient bee population evaluation tool.
Conclusions:
- Automated CT scanning offers a fast and efficient alternative for insect population assessment.
- This technology can help manage environmental influences on insect rearings.
- The method has potential applications for other insect husbandry systems.
