Advances in automatic identification of flying insects using optical sensors and machine learning
| Autor: | Andreas Johnen, Mikkel Jensen, Martin T. Torrance, Klas Rydhmer, Jord C. Prangsma, Carsten Kirkeby, Kaare Græsbøll, Samantha M. Cook, Alfred Strand, Mikkel Brydegaard, Jennifer L. Swain |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Crops
Agricultural Insecticides Insecta Monitoring Computer science Science media_common.quotation_subject 02 engineering and technology Insect Machine learning computer.software_genre 01 natural sciences Article 010309 optics Crop Machine Learning 020210 optoelectronics & photonics Pollinator 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Animals Beneficial insects Pesticides Insect pest management Pollination media_common Multidisciplinary business.industry Crop yield Brassica napus Sustainable agriculture Optical Devices Agriculture Pesticide Identification (information) AI Optical sensors Medicine Precision agriculture Artificial intelligence business computer Entomology Oilseed rape Agroecology |
| Zdroj: | Kirkeby, C, Rydhmer, K, Cook, S M, Strand, A, Torrance, M T, Swain, J L, Prangsma, J, Johnen, A, Jensen, M, Brydegaard, M & Græsbøll, K 2021, ' Advances in automatic identification of flying insects using optical sensors and machine learning ', Scientific Reports, vol. 11, no. 1, 1555 . https://doi.org/10.1038/s41598-021-81005-0 Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) |
| DOI: | 10.1038/s41598-021-81005-0 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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