Deep learning for automated detection of Drosophila suzukii: potential for UAV-based monitoring

Peter P.J. Roosjen* (Corresponding Author), Benjamin Kellenberger, Lammert Kooistra, David R. Green, Johannes Fahrentrapp

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)
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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 SWD flies, 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.

Original languageEnglish
Pages (from-to)2994-3002
Number of pages9
JournalPest Management Science
Issue number9
Early online date20 Apr 2020
Publication statusPublished - 1 Sept 2020

Bibliographical note

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.


  • deep learning
  • Drosophila suzukii
  • integrated pest management (IPM)
  • object detection
  • unmanned aerial vehicle (UAV)


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