Advances in automatic identification of flying insects using optical sensors and machine learning
(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.
(Less)
- author
- organization
- publishing date
- 2021-12
- 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}}, }