Automated Airborne Pest Monitoring AAPM of Drosophila suzukii in Crops and Natural Habitats

Beschreibung

Drosophila suzukii has become a serious pest in Europe since its spread in 2008 to Spain and Italy, attacking many soft-skinned crops such as several berry species, cherry and grapevines. Pest monitoring is the basis of its control. Therefore, an efficient and accurate monitoring system is essential in order to identify the presence of D. suzukii in the crops and the surrounding area, and to prevent damage to economically valuable fruit crops. Existing methods for monitoring D. suzukii are costly, time and labor intensive and consequently conducted at low spatial resolution and prone to errors. We therefore propose to develop a novel system to overcome current monitoring limitations consisting of traps which are monitored by means of an Unmanned Aerial Vehicle (UAV) and an automatic image processing pipeline for the identification and count of number of D. suzukii per trap location. The automated monitoring has an advantage over current methods in terms of (1) labor intensity, (2) sampling interval, (3) automatic integration into DSS, (4) monitoring of diverse and even hardly accessible habitats, and (5) population monitoring in vast areas in relation to climatic and other geo-processed parameters. A multi-variable sticky trap evaluation will allow selecting the most suitable one to attract the target insect. A small multi-rotor UAV platform will be flown at multiple intervals to capture high resolution color aerial photographs of the insect traps. The photographs will be subjected to image processing algorithms to identify the presence or absence of D. suzukii and their counts. The data collected will be transferred to a decision support system (DSS) to provide valuable information for growers in a format that is both meaningful and accessible, thereby demonstrating the added value and social importance of applied science and technology to the wider community and food security.

Eckdaten

Projektleitung

Dr. Johannes Fahrentrapp

Projektteam

Prof. Dr. DR Green, Dr. L Kooistra

Projektpartner

University of Aberdeen / Centre for Environmental Monitoring and Mapping; Wageningen University and Research / Unmanned Aerial Remote Sensing Facility

Projektstatus

abgeschlossen, 04/2017 - 03/2020

Institut/Zentrum

Institut für Umwelt und Natürliche Ressourcen (IUNR)

Drittmittelgeber

EU und andere Internationale Programme

Projektvolumen

282'996 EUR