| dc.contributor.author | Roosjen, Peter P.J. | |
| dc.contributor.author | Kellenberger, Benjamin | |
| dc.contributor.author | Kooistra, Lammert | |
| dc.contributor.author | Green, David R. | |
| dc.contributor.author | Fahrentrapp, Johannes | |
| dc.date.accessioned | 2022-08-08T13:48:00Z | |
| dc.date.available | 2022-08-08T13:48:00Z | |
| dc.date.issued | 2020-09-01 | |
| dc.identifier.citation | Roosjen , P P J , Kellenberger , B , Kooistra , L , Green , D R & Fahrentrapp , J 2020 , ' Deep learning for automated detection of Drosophila suzukii : potential for UAV-based monitoring ' , Pest Management Science , vol. 76 , no. 9 , pp. 2994-3002 . https://doi.org/10.1002/ps.5845 | en |
| dc.identifier.issn | 1526-498X | |
| dc.identifier.other | PURE: 218242096 | |
| dc.identifier.other | PURE UUID: 31f75c5e-9d35-446f-9394-8f5be2bb4ce5 | |
| dc.identifier.other | Scopus: 85083725946 | |
| dc.identifier.other | PubMed: 32246738 | |
| dc.identifier.other | ORCID: /0000-0002-0518-9979/work/118591990 | |
| dc.identifier.uri | https://hdl.handle.net/2164/19031 | |
| dc.description | Funding Information: This work is part of the research programme ERA‐net C‐IPM 2016 with project number ALW.FACCE.7, which is (partly) financed by the Dutch Research Council (NWO). In Switzerland the project was funded by the Swiss Federal Office of Agriculture (grant 627000782). In the UK the project was supported by DEFRA. Funding Information: This work is part of the research programme ERA-net C-IPM 2016 with project number ALW.FACCE.7, which is (partly) financed by the Dutch Research Council (NWO). In Switzerland the project was funded by the Swiss Federal Office of Agriculture (grant 627000782). In the UK the project was supported by DEFRA. Publisher Copyright: © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. | en |
| dc.format.extent | 9 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Pest Management Science | en |
| dc.rights | © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | deep learning | en |
| dc.subject | Drosophila suzukii | en |
| dc.subject | integrated pest management (IPM) | en |
| dc.subject | object detection | en |
| dc.subject | unmanned aerial vehicle (UAV) | en |
| dc.subject | G Geography (General) | en |
| dc.subject | Agronomy and Crop Science | en |
| dc.subject | Insect Science | en |
| dc.subject.lcc | G1 | en |
| dc.title | Deep learning for automated detection of Drosophila suzukii : potential for UAV-based monitoring | en |
| dc.type | Journal article | en |
| dc.contributor.institution | University of Aberdeen.Geography & Environment | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Publisher PDF | en |
| dc.identifier.doi | https://doi.org/10.1002/ps.5845 | |
| dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85083725946&partnerID=8YFLogxK | en |
| dc.identifier.vol | 76 | en |
| dc.identifier.iss | 9 | en |
