Evidence map›Paper›PMID 32059444›Full record

ArticleSensors (Basel, Switzerland)2020

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

Esztella Balla, Norbert Flórián, Veronika Gergócs, Laura Gránicz, Franciska Tóth, Tímea Németh, Miklós Dombos

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Deep learning and computer vision will transform entomology.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Esztella BallaDepartment of Fluid Mechanics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, H-1111 Bertalan Lajos utca 4-6, Budapest, Hungary.
Norbert FlóriánCentre for Agricultural Research, Institute for Soil Sciences and Agricultural Chemistry, H-1022 Herman Ottó út 15, Budapest, Hungary.
Veronika GergócsCentre for Agricultural Research, Institute for Soil Sciences and Agricultural Chemistry, H-1022 Herman Ottó út 15, Budapest, Hungary.
Laura GrániczCentre for Agricultural Research, Institute for Soil Sciences and Agricultural Chemistry, H-1022 Herman Ottó út 15, Budapest, Hungary.
Franciska TóthCentre for Agricultural Research, Institute for Soil Sciences and Agricultural Chemistry, H-1022 Herman Ottó út 15, Budapest, Hungary.
Tímea NémethCentre for Agricultural Research, Institute for Soil Sciences and Agricultural Chemistry, H-1022 Herman Ottó út 15, Budapest, Hungary.
Miklós DombosCentre for Agricultural Research, Institute for Soil Sciences and Agricultural Chemistry, H-1022 Herman Ottó út 15, Budapest, Hungary.

Funding

European Commission LIFE13 ENV/HU/001092Hungarian Science Foundation GINOP-2.3.2-15-2016-00056Hungarian Scientific Research Fund PPD2018-001/2018
6 · The paper itself

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.

Indexed as

Body SizeAlgorithmsAmplifiers, ElectronicAnimalsArthropodsBiosensing TechniquesOptics and PhotonicsAcariCollembolaenvironmental monitoringinfrared sensorinsect detectionintegrated pest managementmicroarthropodsOribatida

Identifiers

PMID32059444
PMCPMC7070424

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.