Mid-Atlantic Section Meeting 2021
Volume 66, Number 18
Friday–Sunday, December 3–5, 2021;
Rutgers University, New Brunswick, New Jersey
Session H03: Optics, Atomic Physics, Devices
11:15 AM–1:15 PM,
Sunday, December 5, 2021
Room: 202A
Chair: Benjamin Thomas, New Jersey Institute of Technology
Abstract: H03.00001 : Near-infrared Optical Sensors to Monitor Flying Insects*
11:15 AM–11:51 AM
Preview Abstract
Abstract
Author:
Benjamin Thomas
(New Jersey Institute of Technology)
Insects, through their large diversity, numbers, and biomass, play a crucial
role in a variety of processes. Whether it is to study beneficial species,
such as pollinators, or to implement mitigation methods for detrimental
species, such as mosquitoes, fine scale measurements of insect behavior is
critical. However, monitoring change in insect distribution, diversity, and
abundance poses a significant challenge to entomologists. Most studies rely
on physical traps using light, pheromones, food, or CO$_{2}$ as bait. While
traps provide a high accuracy for the identification of the captured
insects, they have strong limitations. Notably, they require long and
expensive laboratory analysis, making data on insect population dynamics
scarce and often geographically or temporally limited.
Photonics surveillance of the insect fauna and entomological lidars offer a
potential solution and have shown promising development over the last
decade. The methodology generally relies on identifying and counting insects
flying through a near-infrared laser beam, by retrieving their optical
properties from either backscattered or transmitted optical signals. In this
contribution, we present results from both laboratory and field experiments,
showing that the family, species, sex group, and even gravidity of insects
can be retrieved from spectral and polarimetric backscattered measurements.
Fluctuations of the optical cross-section caused by the rapid movement of
the wings allow for the retrieval of the wing beat frequency and associated
Mel-frequency cepstral coefficients. These constitute a series of predictor
variables used in a supervised machine learning classifier to identify each
insect transiting through the laser beam. Finally, results obtained from
season-long field campaigns in New Jersey are presented, where multiple
infrared sensors have been deployed to continuously monitor insect
activities as well as aerial density and circadian rhythm.
*Research reported in this contribution was supported by the National Institute of Allergy and Infectious Diseases under award number R21AI153732.