Automating insect monitoring using unsupervised near-infrared sensors
(2022) In Scientific Reports 12(1).- Abstract
Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the... (More)
Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor’s capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman’s rank correlation coefficient of 0.61 and a p-value = 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional yellow water trap monitoring.
(Less)
- author
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Scientific Reports
- volume
- 12
- issue
- 1
- article number
- 2603
- publisher
- Nature Publishing Group
- external identifiers
-
- pmid:35173221
- scopus:85124777710
- ISSN
- 2045-2322
- DOI
- 10.1038/s41598-022-06439-6
- language
- English
- LU publication?
- yes
- id
- 77ca9931-6ee4-448f-925a-830154b2ebfd
- date added to LUP
- 2022-04-12 15:27:33
- date last changed
- 2024-07-15 02:20:03
@article{77ca9931-6ee4-448f-925a-830154b2ebfd,
abstract = {{<p>Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor’s capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman’s rank correlation coefficient of 0.61 and a p-value = 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional yellow water trap monitoring.</p>}},
author = {{Rydhmer, Klas and Bick, Emily and Still, Laurence and Strand, Alfred and Luciano, Rubens and Helmreich, Salena and Beck, Brittany D. and Grønne, Christoffer and Malmros, Ludvig and Poulsen, Knud and Elbæk, Frederik and Brydegaard, Mikkel and Lemmich, Jesper and Nikolajsen, Thomas}},
issn = {{2045-2322}},
language = {{eng}},
number = {{1}},
publisher = {{Nature Publishing Group}},
series = {{Scientific Reports}},
title = {{Automating insect monitoring using unsupervised near-infrared sensors}},
url = {{http://dx.doi.org/10.1038/s41598-022-06439-6}},
doi = {{10.1038/s41598-022-06439-6}},
volume = {{12}},
year = {{2022}},
}