Automating insect monitoring using unsupervised near-infrared sensors
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Automating insect monitoring using unsupervised near-infrared sensors. / Rydhmer, Klas; Bick, Emily; Still, Laurence; Strand, Alfred; Luciano, Rubens; Helmreich, Salena; Beck, Brittany D.; Grønne, Christoffer; Malmros, Ludvig; Poulsen, Knud; Elbæk, Frederik; Brydegaard, Mikkel; Lemmich, Jesper; Nikolajsen, Thomas.
In: Scientific Reports, Vol. 12, 2603, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Automating insect monitoring using unsupervised near-infrared sensors
AU - Rydhmer, Klas
AU - Bick, Emily
AU - Still, Laurence
AU - Strand, Alfred
AU - Luciano, Rubens
AU - Helmreich, Salena
AU - Beck, Brittany D.
AU - Grønne, Christoffer
AU - Malmros, Ludvig
AU - Poulsen, Knud
AU - Elbæk, Frederik
AU - Brydegaard, Mikkel
AU - Lemmich, Jesper
AU - Nikolajsen, Thomas
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - WING-BEAT
KW - RADAR
KW - CLASSIFICATION
KW - IDENTIFICATION
KW - HEMIPTERA
KW - FREQUENCY
KW - RESPONSES
KW - SYSTEM
KW - REMOTE
KW - TRAPS
U2 - 10.1038/s41598-022-06439-6
DO - 10.1038/s41598-022-06439-6
M3 - Journal article
C2 - 35173221
VL - 12
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
M1 - 2603
ER -
ID: 300776066