Deep learning for automated detection of Drosophila suzukii-Potential for UAV-based monitoring.

The fruit fly Drosophila suzukii, or spotted wing Drosophila (SWD), has become a serious pest world-wide 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 them in liquid bait traps and manually counting them, are costly, time-consuming, and labour-intensive. To overcome these limitations, we studied insect trap monitoring through image-based object detection with deep learning.Based on an image database with 4753 annotated SWD flies, we trained a ResNet-18-based deep convolutional neural network to detect and count them, 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 by a flying 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 caused by lower image quality as a result of the stabilization manoeuvres of the UAV during image collection.Our results indicate that it is possible to monitor SWD with deep learning and object detection. Moreover, they demonstrate the potential for UAVs to monitor insect traps which could be valuable for the development of autonomous insect monitoring systems and IPM. This article is protected by copyright. All rights reserved.