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 b...

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Autores principales:	Carsten Kirkeby, Klas Rydhmer, Samantha M. Cook, Alfred Strand, Martin T. Torrance, Jennifer L. Swain, Jord Prangsma, Andreas Johnen, Mikkel Jensen, Mikkel Brydegaard, Kaare Græsbøll
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2e1c22a8f9124d7e81390cef1dddfb73
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spelling	oai:doaj.org-article:2e1c22a8f9124d7e81390cef1dddfb732021-12-02T14:01:35ZAdvances in automatic identification of flying insects using optical sensors and machine learning10.1038/s41598-021-81005-02045-2322https://doaj.org/article/2e1c22a8f9124d7e81390cef1dddfb732021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81005-0https://doaj.org/toc/2045-2322Abstract 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.Carsten KirkebyKlas RydhmerSamantha M. CookAlfred StrandMartin T. TorranceJennifer L. SwainJord PrangsmaAndreas JohnenMikkel JensenMikkel BrydegaardKaare GræsbøllNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Carsten Kirkeby Klas Rydhmer Samantha M. Cook Alfred Strand Martin T. Torrance Jennifer L. Swain Jord Prangsma Andreas Johnen Mikkel Jensen Mikkel Brydegaard Kaare Græsbøll Advances in automatic identification of flying insects using optical sensors and machine learning
description	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.
format	article
author	Carsten Kirkeby Klas Rydhmer Samantha M. Cook Alfred Strand Martin T. Torrance Jennifer L. Swain Jord Prangsma Andreas Johnen Mikkel Jensen Mikkel Brydegaard Kaare Græsbøll
author_facet	Carsten Kirkeby Klas Rydhmer Samantha M. Cook Alfred Strand Martin T. Torrance Jennifer L. Swain Jord Prangsma Andreas Johnen Mikkel Jensen Mikkel Brydegaard Kaare Græsbøll
author_sort	Carsten Kirkeby
title	Advances in automatic identification of flying insects using optical sensors and machine learning
title_short	Advances in automatic identification of flying insects using optical sensors and machine learning
title_full	Advances in automatic identification of flying insects using optical sensors and machine learning
title_fullStr	Advances in automatic identification of flying insects using optical sensors and machine learning
title_full_unstemmed	Advances in automatic identification of flying insects using optical sensors and machine learning
title_sort	advances in automatic identification of flying insects using optical sensors and machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2e1c22a8f9124d7e81390cef1dddfb73
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Advances in automatic identification of flying insects using optical sensors and machine learning

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