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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 and Brydegaard, Mikkel LU , et al. (2021) In Scientific Reports 11(1).
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... (More)

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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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
11
issue
1
article number
1555
publisher
Nature Publishing Group
external identifiers
  • scopus:85100094758
  • pmid:33452353
ISSN
2045-2322
DOI
10.1038/s41598-021-81005-0
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2021, The Author(s).
id
ea467e38-573d-4868-aed2-dece05fda700
date added to LUP
2023-09-29 15:02:01
date last changed
2024-06-28 08:15:09
@article{ea467e38-573d-4868-aed2-dece05fda700,
  abstract     = {{<p>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.</p>}},
  author       = {{Kirkeby, Carsten and Rydhmer, Klas and Cook, Samantha M. and Strand, Alfred and Torrance, Martin T. and Swain, Jennifer L. and Prangsma, Jord and Johnen, Andreas and Jensen, Mikkel and Brydegaard, Mikkel and Græsbøll, Kaare}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Advances in automatic identification of flying insects using optical sensors and machine learning}},
  url          = {{http://dx.doi.org/10.1038/s41598-021-81005-0}},
  doi          = {{10.1038/s41598-021-81005-0}},
  volume       = {{11}},
  year         = {{2021}},
}