Literature DB >> 32059444

An Opto-electronic Sensor-ring to Detect Arthropods of Significantly Different Body Sizes.

Esztella Balla1, Norbert Flórián2, Veronika Gergócs2, Laura Gránicz2, Franciska Tóth2, Tímea Németh2, Miklós Dombos2.   

Abstract

Arthropods, including pollinators and pests, have high positive and negative impacts on human well-being and the economy, and there is an increasing need to monitor their activity and population growth. The monitoring of arthropod species is a time-consuming and financially demanding process. Automatic detection can be a solution to this problem. Here, we describe the setup and operation mechanism of an infrared opto-electronic sensor-ring, which can be used for both small and large arthropods. The sensor-ring consists of 16 infrared (IR) photodiodes along a semicircle in front of an infrared LED. Using 3D printing, we constructed two types of sensor-ring: one with a wider sensing field for detection of large arthropods (flying, crawling, surface-living) in the size range of 2-35 mm; and another one with a narrower sensing field for soil microarthropods in the size range of 0.1-2 mm. We examined the detection accuracy and reliability of the two types of sensor-ring in the laboratory by using particles, and dead and living arthropods at two different sensitivity levels. For the wider sensor-ring, the 95% detectability level was reached with grain particles of 0.9 mm size. This result allowed us to detect all of the macroarthropods that were applied in the tests and that might be encountered in pest management. In the case of living microarthropods with different colors and shapes, when we used the narrower sensor-ring, we achieved the 95% detectability level at 1.1 mm, 0.9 mm, and 0.5 mm in the cases of F. candida, H. nitidus, and H. aculeifer, respectively. The unique potential of arthropod-detecting sensors lies in their real-time measurement system; the data are automatically forwarded to the server, and the end-user receives pest abundance data daily or even immediately. This technological innovation will allow us to make pest management more effective.

Entities:  

Keywords:  Acari; Collembola; Oribatida; environmental monitoring; infrared sensor; insect detection; integrated pest management; microarthropods

Year:  2020        PMID: 32059444     DOI: 10.3390/s20040982

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  3 in total

1.  Deep learning and computer vision will transform entomology.

Authors:  Toke T Høye; Johanna Ärje; Kim Bjerge; Oskar L P Hansen; Alexandros Iosifidis; Florian Leese; Hjalte M R Mann; Kristian Meissner; Claus Melvad; Jenni Raitoharju
Journal:  Proc Natl Acad Sci U S A       Date:  2021-01-12       Impact factor: 11.205

2.  Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities.

Authors:  Ioannis Saradopoulos; Ilyas Potamitis; Stavros Ntalampiras; Antonios I Konstantaras; Emmanuel N Antonidakis
Journal:  Sensors (Basel)       Date:  2022-03-04       Impact factor: 3.576

3.  Detecting Soil Microarthropods with a Camera-Supported Trap.

Authors:  Norbert Flórián; Laura Gránicz; Veronika Gergócs; Franciska Tóth; Miklós Dombos
Journal:  Insects       Date:  2020-04-14       Impact factor: 2.769

  3 in total

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