Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-21542Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Roosjen, Peter PJ | - |
| dc.contributor.author | Kellenberger, Benjamin | - |
| dc.contributor.author | Kooistra, Lammert | - |
| dc.contributor.author | Green, David R | - |
| dc.contributor.author | Fahrentrapp, Johannes | - |
| dc.date.accessioned | 2021-02-04T10:55:55Z | - |
| dc.date.available | 2021-02-04T10:55:55Z | - |
| dc.date.issued | 2020-04-04 | - |
| dc.identifier.issn | 1526-498X | de_CH |
| dc.identifier.issn | 1526-4998 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/21542 | - |
| dc.description.abstract | 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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Wiley | de_CH |
| dc.relation.ispartof | Pest Management Science | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject | UAV | de_CH |
| dc.subject | Pest monitoring | de_CH |
| dc.subject | Integrated pest management | de_CH |
| dc.subject | IPM | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.subject.ddc | 632: Pflanzenkrankheiten, Schädlinge | de_CH |
| dc.title | Deep learning for automated detection of Drosophila suzukii : potential for UAV‐based monitoring | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | Life Sciences und Facility Management | de_CH |
| zhaw.organisationalunit | Institut für Umwelt und Natürliche Ressourcen (IUNR) | de_CH |
| dc.identifier.doi | 10.1002/ps.5845 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-21542 | - |
| zhaw.funding.eu | Not specified | de_CH |
| zhaw.issue | 9 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 3002 | de_CH |
| zhaw.pages.start | 2994 | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 76 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.webfeed | Hortikultur | de_CH |
| zhaw.funding.zhaw | Automated Airborne Pest Monitoring AAPM of Drosophila suzukii in Crops and Natural Habitats | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen Life Sciences und Facility Management | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2020_Roosjen_etal_Deep-learning-for-automated-detection-of-Drosophila-suzukii.pdf | 7.61 MB | Adobe PDF |  View/Open |
Show simple item record
Roosjen, P. P., 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, 76(9), 2994–3002. https://doi.org/10.1002/ps.5845
Roosjen, P.P. et al. (2020) ‘Deep learning for automated detection of Drosophila suzukii : potential for UAV‐based monitoring’, Pest Management Science, 76(9), pp. 2994–3002. Available at: https://doi.org/10.1002/ps.5845.
P. P. Roosjen, B. Kellenberger, L. Kooistra, D. R. Green, and J. Fahrentrapp, “Deep learning for automated detection of Drosophila suzukii : potential for UAV‐based monitoring,” Pest Management Science, vol. 76, no. 9, pp. 2994–3002, Apr. 2020, doi: 10.1002/ps.5845.
ROOSJEN, Peter PJ, Benjamin KELLENBERGER, Lammert KOOISTRA, David R GREEN und Johannes FAHRENTRAPP, 2020. Deep learning for automated detection of Drosophila suzukii : potential for UAV‐based monitoring. Pest Management Science. 4 April 2020. Bd. 76, Nr. 9, S. 2994–3002. DOI 10.1002/ps.5845
Roosjen, Peter PJ, Benjamin Kellenberger, Lammert Kooistra, David R Green, and Johannes Fahrentrapp. 2020. “Deep Learning for Automated Detection of Drosophila Suzukii : Potential for UAV‐Based Monitoring.” Pest Management Science 76 (9): 2994–3002. https://doi.org/10.1002/ps.5845.
Roosjen, Peter PJ, et al. “Deep Learning for Automated Detection of Drosophila Suzukii : Potential for UAV‐Based Monitoring.” Pest Management Science, vol. 76, no. 9, Apr. 2020, pp. 2994–3002, https://doi.org/10.1002/ps.5845.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.