Formulae for Insect Wingbeat Frequency

Deakin, Michael A. B.

doi:10.1673/031.010.9601

Cited by 34 publications

(31 citation statements)

References 6 publications

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“…Nevertheless, it is clear that in our general framework, it is possible that users may wish to use features that clearly violate this assumption. For example, if the sensor was augmented to obtain insect mass (a generally useful feature), it is clear from basic principles of allometric scaling that the frequency spectrum feature would not be independent (Deakin 2010). The good news is that as shown in Figure 12, the Bayesian network can be generalized to encode the dependencies among the features.…”

Section: Revisiting the Independent Assumptionmentioning

confidence: 99%

Flying Insect Classification with Inexpensive Sensors

et al. 2014

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The ability to use inexpensive, noninvasive sensors to accurately classify flying insects would have significant implications for entomological research, and allow for the development of many useful applications in vector control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts on this task. To date, however, none of this research has had a lasting impact. In this work, we explain this lack of progress. We attribute the stagnation on this problem to several factors, including the use of acoustic sensing devices, the overreliance on the single feature of wingbeat frequency, and the attempts to learn complex models with relatively little data. In contrast, we show that pseudo-acoustic optical sensors can produce vastly superior data, that we can exploit additional features, both intrinsic and extrinsic to the insect's flight behavior, and that a Bayesian classification approach allows us to efficiently learn classification models that are very robust to overfitting. We demonstrate our findings with large scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.

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Section: Revisiting the Independent Assumptionmentioning

confidence: 99%

Flying Insect Classification with Inexpensive Sensors

et al. 2014

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“…For example, Koehler et al [15] summarized the results obtained to estimate wingbeat frequency for locust, hoverfly, fruitfly, dronefly, and dragonfly; Casey et al [3] did the same for several species of euglossine bees, including Eulaema, Eufriesea, Euglossa, and Exaerete. With these measurements, some researchers, such as Deakin [5] and Sudo et al [28], have developed empirical formulas connecting wingbeat frequency of insects with their masses and wing areas.…”

Section: Related Workmentioning

confidence: 99%

Frequency analysis of a bumblebee (Bombus impatiens) wingbeat

Santoyo

Azarcoya

Valencia

et al. 2015

Pattern Anal Applic

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The wingbeat of an insect relates directly to energy consumption, is a strong indicator of its rate of metabolism and physical structure, and inversely relates to the length of its wing and to the mass of its body. It is also a principal component in understanding the aerodynamic properties of its flight. In this paper, we introduce a method based on the use of high-speed cameras and computer vision techniques to analyze a bumblebee (Bombus impatiens) wingbeat. We start capturing images with a virtual stereo system when a bumblebee crosses two intersecting laser beams. Then, we detect moving objects using background subtraction. Next, via Fourier analysis of the observed optical flow contraction/expansion, and marginalization of prior knowledge, we estimate the wingbeat frequency. Finally, the information from the two virtual cameras is fused using a robust state estimation. Our system is well prepared to handle occlusions; it works with untethered insects; and it does not require the synchronization of a multi-camera system.

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“…In addition, an increase of 5.2% can also be observed in the average wing beat frequency, from 347 Hz for non‐gravid females to 365 Hz for gravid females due to the insect compensating for the increase of its weight . Using the formulae provided by M. Deakin relating the wing beat frequency and the mass m of the insect, the wing beat frequency increases with m 0.3 leading to an increase of mass between gravid and non‐gravid of approximately 0.91 mg. A similar estimation of the change in mass, using the depolarization ratio to infer the increase in optical path‐length in the scattering medium and therefore the increase in volume and mass, leads to an increase in mass of 0.47 mg. The Culex female lays between 100 and 300 eggs, each fully grown egg weighting between 10 and 15 μg .…”

Section: Resultsmentioning

confidence: 99%

Identification of gravid mosquitoes from changes in spectral and polarimetric backscatter cross sections

Genoud

Gao

Williams

et al. 2019

Journal of Biophotonics

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Improving the survey of mosquito populations is of the utmost importance to further enhance mitigation techniques that protect human populations from mosquito‐borne diseases. While mosquito populations are generally studied using physical traps, stand‐off optical sensors allow to study insect ecosystems with potentially better spatial and temporal resolution. This can be greatly beneficial to eco‐epidemiological models and various mosquito control programs. In this contribution, we demonstrate that the gravidity of female mosquitoes can be identified from changes in their spectral and polarimetric backscatter cross sections. Among other predictive variables, the wing beat frequency and the depolarization ratio of the mosquito body allows for the identification of gravid females with a precision and recall of 86% and 87%, respectively. Since female mosquitoes need a blood meal to become gravid, statistics on gravidity is of prime importance as only females that have been gravid might carry infectious diseases. In addition, it allows to detect possible breeding habitat, predict a potential increase in the mosquito population and provide a better overall understanding of the ecosystem dynamics. As a result, targeted and localized mitigation techniques can be used, reducing the cost and improving the efficiency of mosquito population control.

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Formulae for Insect Wingbeat Frequency

Cited by 34 publications

References 6 publications

Flying Insect Classification with Inexpensive Sensors

Flying Insect Classification with Inexpensive Sensors

Frequency analysis of a bumblebee (Bombus impatiens) wingbeat

Identification of gravid mosquitoes from changes in spectral and polarimetric backscatter cross sections

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