A - Papers appearing in refereed journals
Kirkeby, C, Rydhmer, K, Cook, S. M., Strand, A, Torrance, M. T., Swain, J. L., Prangsma, J, Johnen, A, Jensen, M, Brydegaard, M and Graesboll, K 2021. Advances in automatic identifcation of flying insects using optical sensors and machine learning. Scientific Reports. 11 (1), pp. 1-8. https://doi.org/10.1038/s41598-021-81005-0
Authors | Kirkeby, C, Rydhmer, K, Cook, S. M., Strand, A, Torrance, M. T., Swain, J. L., Prangsma, J, Johnen, A, Jensen, M, Brydegaard, M and Graesboll, K |
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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 |
Keywords | Machine learning; AI; Optical sensors; Oilseed rape; Monitoring ; Sustainable agriculture; Insect pest management |
Year of Publication | 2021 |
Journal | Scientific Reports |
Journal citation | 11 (1), pp. 1-8 |
Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-021-81005-0 |
Web address (URL) | https://doi.org/10.1038/s41598-021-81005-0 |
Open access | Published as ‘gold’ (paid) open access |
Funder | BASF |
Publisher's version | |
Output status | Published |
Publication dates | |
Online | 15 Jan 2021 |
Publication process dates | |
Accepted | 29 Dec 2020 |
Publisher | Nature Publishing Group |
ISSN | 2045-2322 |
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