KINoPro
Project
Optimization of the monitoring of potential forest pests via Artifical Intelligence using the example of the nun moth (Lymantria monacha L.) - KINoPro
Subproject 1
Natural scientific accompanying research
Project period
05/2021 - 04/2024
Project partner
con terra GmbH
Project description
The main objective of this project is to examine the quality of Artifical Intelligence (AI) models when used to predict the population development during the mass outbreak of potential pest insects with temporary fluctuations on Scots pine (Pinus sylvestris). The nun moth (Lymantria monacha) will be used as an example within this project, but it is assumed that trained artificial neural networks can also be adapted to and used for different potential pest insects and their antagonists. The project aims at significantly reducing the resources required to forecast the mass outbreak development of L. monacha by means of AI methods and at delivering resilient predictions in times of dynamic forest changes e. g. by forest conversion and climate change. Comprehensive meteorological data and the trapping numbers obtained by the nun moth standard monitoring in Brandenburg and Saxony will be integrated in the geo.ai platform of the con terra GbmH to develop the AI. Based on this different models will be built and subsequently further trained and optimized during the trapping periods within the project. The results will be verified using the standard monitoring data and data from additional sites.
Download of the final project report at https://www.fnr.de/fileadmin/projektdatenbank/2220NR204A.pdf
Project related publications, presentations and theses
Fabry I., Jordan-Fragstein C., Fröhlking J., Krautz N., Stöcker M., Müller M. (2023): KINoPro - Chancen durch KI-gestützte Monitoringverfahren. AFZ DerWald 78 (13), S. 44–45.
Krautz N., Müller M., Britsch C., Fröhlking J., Stöcker M., Fabry I. (2024): Artificial intelligence as a possibility for pest prediction in forests - case study nun moth (Lymantria monacha) - Current results of the KINoPro project. Poster zum 18th Symposium on Insect-Plant Relationships vom 04.-08. August 2024 in Bielefeld, Deutschland.
Krautz N., Müller M., Britsch C., Fröhlking J., Stöcker M., van Kevelaer R. (2025):
Anwendung von Künstlicher Intelligenz in der Prognose am Beispiel der Nonne (Lymantria mochacha): Aktuelle Ergebnisse des Projketes KINoPro. Poster zur DGaaE-Entomologentagung 2025 vom 17.-20. März 2025 in Geisenheim, Deutschland.
Paul, Vanessa (2025): Vergleichende Untersuchungen zur Lockwirkung von Dispensern verschiedener Hersteller auf die Nonne (Lymantria monacha) im Tharandter Wald. Bachelorarbeit. Professur für Waldschutz, Technische Universität Dresden.
Schmiechen, Teo (2025): Untersuchung des Einflusses unterschiedlicher Dispenser auf die Fangzahlen von Lymantria monacha unter Berücksichtigung von Umweltfaktoren. Bachelorarbeit. Professur für Waldschutz, Technische Universität Dresden.
Project staff
M. Sc. Nancy Krautz, Scientist
© BMEL
© BMU
based on a decision of the German Bundestag