N - Datasets
Hassall, K. L., Dye, A. and Bell, J. R. 2020. Extracted features from opto-acoustic audio recordings of aphids and beetles. Rothamsted Research. https://doi.org/10.23637/rothamsted.981w8
|Authors||Hassall, K. L., Dye, A. and Bell, J. R.|
Opto-acoustic recorders have been utilised to record the flight of weak-flying insects including aphids (Hemiptera: Aphididae) and beetles (Coleoptera). These data include 52 extracted features from 4928 audio recordings collected in 2019 and 2020 of 8 distinct species.
|Year of Publication||2020|
|Digital Object Identifier (DOI)||https://doi.org/10.23637/rothamsted.981w8|
|Funder||Biotechnology and Biological Sciences Research Council|
|Is derived from||Opto-acoustic audio recordings of aphids and beetles|
|Is supplement to||Resolving the identification of weak-flying insects during flight: a coupling between rigorous data processing and biology|
|Funder project or code||BBSRC Strategic Programme in Smart Crop Protection|
|The Rothamsted Insect Survey - National Capability [2017-2022]|
File Access Level
|Data collection method|
Opto-acoustic recorders capture the variation of light when an insect passes through a light beam. Both the main body and the wings cast a shadow in the emitter’s light beam, known as the extinction of light principle, and this shadow is subsequently detected by a receiver photodiode array (14). The Wingbeat Recorder® (Insectronics, Chania, Crete, Greece) was placed underneath a 15,000 ml heavy-walled glass beaker (Duran™).
Insects were collected and placed inside the beaker. Over a period of two days, insects were free to disperse in and around the sensor. Flights were automatically triggered as an insect enters the field of view of the LED array, generating a recording lasting 0.6 seconds. All flights were saved as an audio file on an SD card within the sensor along with average temperature and humidity covariates. These audio recordings were subject to internal signal processing (Potamitis I, Rigakis I. Large aperture optoelectronic devices to record and time-stamp insects’ wingbeats. IEEE Sensors Journal. 2016;16(15):6053-61.) after which an extensive data processing pipeline as described in Hassall et al (In Preparation) was applied to extract the 52 features contained in this dataset.
|Data preparation and processing activities|
The associated CSV file contains the extracted features from individual audio recordings stored in separate rows. Covariate information (species, time of recording, average temperature, average humidity, the length of the audio file) are detailed in the first 6 columns. Column 7 onwards contains the extracted feature information. Details of these features can be found in Hassall et al (In Preparation).
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