Insect remote sensing using a polarization sensitive cw lidar system in chinese rice fields

Zhu, Shi Yao; Malmqvist, Ebba; Li, Yiyun; Jansson, Samuel; Li, Wansha; Duan, Zheng; Fu, Wei; Svanberg, Katarina; Bood, Joakim; Feng, Huiyun; Åkesson, Susanne; Song, Ziwei; Zhang, Baoxin; Zhao, Guangyu; Li, Dunsong; Brydegaard, Mikkel; Svanberg, Sune

doi:10.1051/epjconf/201817607001

Cited by 6 publications

(4 citation statements)

References 11 publications

(12 reference statements)

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“…Extensive field work employing CW lidar techniques in insect monitoring was also performed in a rice paddy setting in Southern China . A particularity of this experiment was that the system allowed full characterization of the depolarization properties of the backscattered radiation.…”

Section: Remote Sensing Of Atmospheric and Aquatic Faunamentioning

confidence: 99%

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Photonic Monitoring of Atmospheric and Aquatic Fauna

Brydegaard

Svanberg

2018

Laser & Photonics Reviews

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Flying insects are of utmost importance in ecology and for human living conditions. Certain species serve as indispensable pollinators to ensure the availability of food stuffs, while others are dangerous vectors for spreading deadly diseases, such as malaria. Agricultural pests reduce the yield of crops, and their abatement through pesticides cause many additional problems. Birds and bats are frequently carriers of diseases, which, especially for long‐distance migrants, cause serious consequences. Clearly, there is high motivation to be able to effectively identify and quantify flying fauna. Here, the emerging field of optics and laser‐based monitoring of flying fauna is reviewed with an emphasis on remote sensing based on pulsed and contineous wave (CW) lidar systems, and how they complement existing radar techniques. Furthermore, ground‐truth laboratory studies are covered. Wing‐beat and overtone spectra as well as reflectance, depolarization and fluorescence properties are studied. The aquatic environment is for many reasons less accessible for optical studies, but is clearly also of great importance. Phytoplankton constitute the start of the aquatic food chain, followed by zooplankton and a long chain of higher animals, including fish, an important part of the human food supply. Finally, how optical monitoring can complement sonar and sampling techniques is discussed.

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Section: Remote Sensing Of Atmospheric and Aquatic Faunamentioning

confidence: 99%

“…It is noted that the insect discrimination works similarly using both methods. Reproduced under the terms of the CC BY 4.0 license . Copyright 2017, The Authors, Published by Springer Berlin Heidelberg; and Copyright 2018, The Authors, Published by EDP Sciences.…”

Section: Remote Sensing Of Atmospheric and Aquatic Faunamentioning

confidence: 99%

Photonic Monitoring of Atmospheric and Aquatic Fauna

Brydegaard

Svanberg

2018

Laser & Photonics Reviews

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“…To remotely identify freely flying moths, their ventral infrared properties could be retrieved by entomological lidar [ 54 ] in vertical mode. Such lidars can be implemented with polarization sensitivity [ 55 ] and/or with dual band in the short-wave infrared [ 56 ]. Wings make the largest contribution to the backscattering, and most of the wing signal is coherent and specular.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, the coherent specular phenomena described in this study have a large impact on the resulting lidar signals. The infrared glossiness can be retrieved differentially with a dual-band lidar [ 56 ], through co-polarization- and de-polarization-sensitive lidar [ 55 ], or simply by calculating the waveform skewness. We thus can expect that numerous moth species can be differentiated remotely by retrieving reflected optical signals that are directly related to the microstructures of their wing scales.…”

Section: Discussionmentioning

confidence: 99%

Potential for identification of wild night-flying moths by remote infrared microscopy

Seinsche

Jansson

et al. 2022

J. R. Soc. Interface.

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There are hundreds of thousands of moth species with crucial ecological roles that are often obscured by their nocturnal lifestyles. The pigmentation and appearance of moths are dominated by cryptic diffuse shades of brown. In this study, 82 specimens representing 26 moth species were analysed using infrared polarimetric hyperspectral imaging in the range of 0.95–2.5 µm. Contrary to previous studies, we demonstrate that since infrared light does not resolve the surface roughness, wings appear glossy and specular at longer wavelengths. Such properties provide unique reflectance spectra between species. The reflectance of the majority of our species could be explained by comprehensive models, and a complete parametrization of the spectral, polarimetric and angular optical properties was reduced to just 11 parameters with physical units. These parameters are complementary and, compared with the within-species variation, were significantly distinct between species. Counterintuitively to the aperture-limited resolution criterion, we could deduce microscopic features along the surface from their infrared properties. These features were confirmed by electron microscopy. Finally, we show how our findings could greatly enhance opportunities for remote identification of free-flying moth species, and we hypothesize that such flat specular wing targets could be expected to be sensed over considerable distances.

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Advances in automatic identification of flying insects using optical sensors and machine learning

Kirkeby

Rydhmer²,

Cook

et al. 2021

Sci Rep

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Worldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in flight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority.

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Insect remote sensing using a polarization sensitive cw lidar system in chinese rice fields

Cited by 6 publications

References 11 publications

Photonic Monitoring of Atmospheric and Aquatic Fauna

Photonic Monitoring of Atmospheric and Aquatic Fauna

Potential for identification of wild night-flying moths by remote infrared microscopy

Advances in automatic identification of flying insects using optical sensors and machine learning

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