Automating insect monitoring using unsupervised near-infrared sensors

Klas Rydhmer,Laurence Still,Jesper Lemmich,Mikkel Brydegaard,Thomas Nikolajsen,Ludvig Malmros,Rubens Luciano,Alfred Strand,Frederik Elbæk,Christoffer Grønne,Brittany D Beck,Emily Bick,Knud Poulsen,Salena Helmreich

doi:10.1038/s41598-022-06439-6

Klas Rydhmer, Laurence Still + Show 12 more

Open Access

https://doi.org/10.1038/s41598-022-06439-6

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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 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.

Highlights

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
Over the course of the season, an average of 1122 ± 242 (SE) insect observations per day were collected per sensor, compared to an average of 63 ± 6 (SE) insects caught per water trap per day over the same period
The sensor illuminates an air volume and records the backscattered light from insects that fly through the measurement volume

Summary

Introduction

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. 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. Insects are monitored via established sampling methods including trapping, sweep netting, and portable aspiration[12–14]. These methods are imperfect resulting in biases towards s ize[15–17] and s tage[18]. In order to reduce the cost of insect monitoring and identification, automation of insect trapping[23–27] and identification[27–31] has been developed While these methods could greatly improve monitoring via traps, they are unsuitable for monitoring a general insect population since trap designs and baits are generally biased in regard to species[32,33]. Automation of insect monitoring without traps could reduce species bias of conventional methods and human error, greatly improving the state of the art. While radar technologies have much larger monitoring range[16,40,48–50], Scientific Reports | (2022) 12:2603

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