Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21542
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dc.contributor.authorRoosjen, Peter PJ-
dc.contributor.authorKellenberger, Benjamin-
dc.contributor.authorKooistra, Lammert-
dc.contributor.authorGreen, David R-
dc.contributor.authorFahrentrapp, Johannes-
dc.date.accessioned2021-02-04T10:55:55Z-
dc.date.available2021-02-04T10:55:55Z-
dc.date.issued2020-04-04-
dc.identifier.issn1526-498Xde_CH
dc.identifier.issn1526-4998de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21542-
dc.description.abstractBACKGROUND: The fruit fly Drosophila suzukii, or spotted wing drosophila (SWD), is a serious pest worldwide, attacking many soft-skinned fruits. An efficient monitoring system that identifies and counts SWD in crops and their surroundings is therefore essential for integrated pest management (IPM) strategies. Existing methods, such as catching flies in liquid bait traps and counting them manually, are costly, time-consuming and labour-intensive. To overcome these limitations, we studied insect trap monitoring using image-based object detection with deep learning. RESULTS: Based on an image database with 4753 annotated SWDflies, we trained a ResNet-18-based deep convolutional neural network to detect and count SWD, including sex prediction and discrimination. The results show that SWD can be detected with an area under the precision recall curve (AUC) of 0.506 (female) and 0.603 (male) in digital images taken from a static position. For images collected using an unmanned aerial vehicle (UAV), the algorithm detected SWD individuals with an AUC of 0.086 (female) and 0.284 (male). The lower AUC for the aerial imagery was due to lower image quality caused by stabilisation manoeuvres of the UAV during image collection. CONCLUSION: Our results indicate that it is possible to monitor SWD using deep learning and object detection. Moreover, the results demonstrate the potential of UAVs to monitor insect traps, which could be valuable in the development of autonomous insect monitoring systems and IPM.de_CH
dc.language.isoende_CH
dc.publisherWileyde_CH
dc.relation.ispartofPest Management Sciencede_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectUAVde_CH
dc.subjectPest monitoringde_CH
dc.subjectIntegrated pest managementde_CH
dc.subjectIPMde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc632: Pflanzenkrankheiten, Schädlingede_CH
dc.titleDeep learning for automated detection of Drosophila suzukii : potential for UAV‐based monitoringde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Umwelt und Natürliche Ressourcen (IUNR)de_CH
dc.identifier.doi10.1002/ps.5845de_CH
dc.identifier.doi10.21256/zhaw-21542-
zhaw.funding.euNot specifiedde_CH
zhaw.issue9de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end3002de_CH
zhaw.pages.start2994de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume76de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedHortikulturde_CH
zhaw.funding.zhawAutomated Airborne Pest Monitoring AAPM of Drosophila suzukii in Crops and Natural Habitatsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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