FaunaPhotonics ApS

We present autofluorescence of six zooplankton species, including salmon lice (Lepeophtheirus salmonis), for the purpose of classification in marine environments. Using a 410 nm excitation wavelength, we find that all measured zooplankton species exhibit broad cyan fluorescence centered around 510-520 nm. Furthermore, salmon lice show an absence of red fluorescence from undigested chlorophyll, which is measured from the gut of the herbivorous zooplankton species. We show the capability to distinguish noneating species, including salmon lice, from algae-eating species using a dual-band analysis of the fluorescence spectra. This shows the potential of autofluorescence as an important signature in real-time monitoring and classification of salmon lice.

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.However, conventional monitoring methods of trapping and identification are time consuming and thus expensive.Here, we present a network of distributed wireless sensors, recording backscattered near-IR modulation signatures from insects.The instrument is a compact sensor based on dual-wavelength IR 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 volumeInsect 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-wk 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 approx. 19 times more insect observations and demonstrating a larger temporal dynamic than conventional trapping.