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

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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

2 Downloads (Pure)

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.

Original languageEnglish
Article number1555
JournalScientific Reports
Volume11
Issue number1
Number of pages8
ISSN2045-2322
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

Fingerprint Dive into the research topics of 'Advances in automatic identification of flying insects using optical sensors and machine learning'. Together they form a unique fingerprint.

Cite this