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Drosophila suzukii has become a serious pest in Europe attacking many soft-skinned crops such as several berry species and grapevines since its spread in 2008 to Spain and Italy. An efficient and accurate monitoring system to identify the presence of Drosophila suzukii in crops and their surroundings is essential for the prevention of damage to economically valuable fruit crops. Existing methods for monitoring Drosophila suzukii are costly, time and labor intensive, prone to errors, and typically conducted at a low spatial resolution. To overcome current monitoring limitations, we are developing a novel system consisting of sticky traps which are monitored by means of Unmanned Aerial Vehicles (UAVs) and an image processing pipeline that automatically identifies and counts the number of Drosophila suzukii per trap location. To this end, we are currently collecting high resolution RGB imagery of Drosophila suzukii flies in sticky traps taken from both a static position (tripod) and from a UAV, which are then used as input to train deep learning models. Preliminary results show that a large part of the of Drosophila suzukii flies that are caught in the sticky traps can be correctly identified by the trained deep learning models. In the future, an autonomously flying UAV platform will be programmed to capture imagery of the sticky traps under field conditions. The collected imagery will be transferred directly to cloud-based storage for subsequent processing and analysis to identify the presence and count of Drosophila suzukii in near real time. This data will be used as input to a decision support system (DSS) to provide valuable information for farmers.

www.aapmproject.eu/

Spotted wing drosophila (SWD) is a fruit fly attacking soft berry fruits. To measure the success of control measures against SWD, accurate and frequent monitoring of its presence is required. The AAPM project develops a system based on planar, photographable such as sticky traps that are monitored with high-resolution airborne imagery. The images are analyzed with deep learning methods and target insects are counted. Currently, we selected the best performing trap to catch SWD. Some hundred traps with SWD were used to train detection and counting algorithms. The next part of the project will focus on airborne image acquisition and integration of SWD counts into decision support systems providing information for IPM strategies.