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Publikationstyp: Beitrag in wissenschaftlicher Zeitschrift
Art der Begutachtung: Peer review (Publikation)
Titel: Deep learning for automated detection of Drosophila suzukii : potential for UAV‐based monitoring
Autor/-in: Roosjen, Peter PJ
Kellenberger, Benjamin
Kooistra, Lammert
Green, David R
Fahrentrapp, Johannes
et. al: No
DOI: 10.1002/ps.5845
10.21256/zhaw-21542
Erschienen in: Pest Management Science
Band(Heft): 76
Heft: 9
Seite(n): 2994
Seiten bis: 3002
Erscheinungsdatum: 4-Apr-2020
Verlag / Hrsg. Institution: Wiley
ISSN: 1526-498X
1526-4998
Sprache: Englisch
Schlagwörter: UAV; Pest monitoring; Integrated pest management; IPM
Fachgebiet (DDC): 006: Spezielle Computerverfahren
632: Pflanzenkrankheiten, Schädlinge
Zusammenfassung: BACKGROUND: 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/21542
Volltext Version: Publizierte Version
Lizenz (gemäss Verlagsvertrag): CC BY 4.0: Namensnennung 4.0 International
Departement: Life Sciences und Facility Management
Organisationseinheit: Institut für Umwelt und Natürliche Ressourcen (IUNR)
Publiziert im Rahmen des ZHAW-Projekts: Automated Airborne Pest Monitoring AAPM of Drosophila suzukii in Crops and Natural Habitats
Enthalten in den Sammlungen:Publikationen Life Sciences und Facility Management

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