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 -