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https://doi.org/10.21256/zhaw-21542| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Deep learning for automated detection of Drosophila suzukii : potential for UAV‐based monitoring |
| Authors: | Roosjen, Peter PJ Kellenberger, Benjamin Kooistra, Lammert Green, David R Fahrentrapp, Johannes |
| et. al: | No |
| DOI: | 10.1002/ps.5845 10.21256/zhaw-21542 |
| Published in: | Pest Management Science |
| Volume(Issue): | 76 |
| Issue: | 9 |
| Page(s): | 2994 |
| Pages to: | 3002 |
| Issue Date: | 4-Apr-2020 |
| Publisher / Ed. Institution: | Wiley |
| ISSN: | 1526-498X 1526-4998 |
| Language: | English |
| Subjects: | UAV; Pest monitoring; Integrated pest management; IPM |
| Subject (DDC): | 006: Special computer methods 632: Plant diseases, pests |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/21542 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Natural Resource Sciences (IUNR) |
| Published as part of the ZHAW project: | Automated Airborne Pest Monitoring AAPM of Drosophila suzukii in Crops and Natural Habitats |
| 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 |
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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.
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