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

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Standard

Advances in automatic identification of flying insects using optical sensors and machine learning. / Kirkeby, Carsten; Rydhmer, Klas; Cook, Samantha M.; Strand, Alfred; Torrance, Martin T.; Swain, Jennifer L.; Prangsma, Jord; Johnen, Andreas; Jensen, Mikkel; Brydegaard, Mikkel; Græsbøll, Kaare.

I: Scientific Reports, Bind 11, Nr. 1, 1555, 2021.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Kirkeby, C, Rydhmer, K, Cook, SM, Strand, A, Torrance, MT, Swain, JL, Prangsma, J, Johnen, A, Jensen, M, Brydegaard, M & Græsbøll, K 2021, 'Advances in automatic identification of flying insects using optical sensors and machine learning', Scientific Reports, bind 11, nr. 1, 1555. https://doi.org/10.1038/s41598-021-81005-0

APA

Kirkeby, C., Rydhmer, K., Cook, S. M., Strand, A., Torrance, M. T., Swain, J. L., Prangsma, J., Johnen, A., Jensen, M., Brydegaard, M., & Græsbøll, K. (2021). Advances in automatic identification of flying insects using optical sensors and machine learning. Scientific Reports, 11(1), [1555]. https://doi.org/10.1038/s41598-021-81005-0

Vancouver

Kirkeby C, Rydhmer K, Cook SM, Strand A, Torrance MT, Swain JL o.a. Advances in automatic identification of flying insects using optical sensors and machine learning. Scientific Reports. 2021;11(1). 1555. https://doi.org/10.1038/s41598-021-81005-0

Author

Kirkeby, Carsten ; Rydhmer, Klas ; Cook, Samantha M. ; Strand, Alfred ; Torrance, Martin T. ; Swain, Jennifer L. ; Prangsma, Jord ; Johnen, Andreas ; Jensen, Mikkel ; Brydegaard, Mikkel ; Græsbøll, Kaare. / Advances in automatic identification of flying insects using optical sensors and machine learning. I: Scientific Reports. 2021 ; Bind 11, Nr. 1.

Bibtex

@article{4f5acbd25eea4a5596427db72c424033,
title = "Advances in automatic identification of flying insects using optical sensors and machine learning",
abstract = "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.",
author = "Carsten Kirkeby and Klas Rydhmer and Cook, {Samantha M.} and Alfred Strand and Torrance, {Martin T.} and Swain, {Jennifer L.} and Jord Prangsma and Andreas Johnen and Mikkel Jensen and Mikkel Brydegaard and Kaare Gr{\ae}sb{\o}ll",
year = "2021",
doi = "10.1038/s41598-021-81005-0",
language = "English",
volume = "11",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "nature publishing group",
number = "1",

}

RIS

TY - JOUR

T1 - Advances in automatic identification of flying insects using optical sensors and machine learning

AU - Kirkeby, Carsten

AU - Rydhmer, Klas

AU - Cook, Samantha M.

AU - Strand, Alfred

AU - Torrance, Martin T.

AU - Swain, Jennifer L.

AU - Prangsma, Jord

AU - Johnen, Andreas

AU - Jensen, Mikkel

AU - Brydegaard, Mikkel

AU - Græsbøll, Kaare

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

U2 - 10.1038/s41598-021-81005-0

DO - 10.1038/s41598-021-81005-0

M3 - Journal article

C2 - 33452353

AN - SCOPUS:85100094758

VL - 11

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 1555

ER -

ID: 256513131