Publication type: Book part
Type of review: Editorial review
Title: Autonomous UAV-based insect monitoring
Authors: Fahrentrapp, Johannes
Roosjen, Peter
Kooistra, Lammert
Green, David R.
Gregory, Billy J.
et. al: No
DOI: 10.1201/9780429172410-9
Published in: Unmanned Aerial Remote Sensing : UAS for Environmental Applications
Editors of the parent work: Green, David R.
Gregory, Billy J.
Karachok, Alex R.
Page(s): 137
Pages to: 159
Issue Date: 2020
Publisher / Ed. Institution: CRC Press
Publisher / Ed. Institution: Boca Raton
ISBN: 9780429172410
Language: English
Subjects: Monitoring; UAV; Remote sensing; Insect; integrated pest management; IPM
Subject (DDC): 632: Plant diseases, pests
Abstract: Drosophila suzukii Matsumura, the spotted wing drosophila (SWD), 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 D. suzukii in crops and their surroundings is essential for the prevention of damage to economically valuable fruit crops. Existing methods for monitoring D. suzukii are costly, time and labour intensive, prone to errors, and typically conducted at a low spatial resolution. To overcome current monitoring limitations, we are investigating and developing a novel system consisting of traps that are monitored by means of cameras from Unmanned Aerial Vehicles (UAVs) and an image processing pipeline that automatically identifies and counts the number of D. suzukii per trap location. To this end, we are currently collecting high-resolution RGB imagery of D. suzukii flies in sticky traps taken from both a static position (tripod) and a UAV, which are then used as input to train deep learning object detection models. Preliminary results show that a large part of the D. 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 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 D. suzukii in near real time. This data will subsequently be used as input to a decision support system (DSS) to provide valuable information for farmers.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
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

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