2026-03-132026-03-13https://hdl.handle.net/11420/62103Biodiversity is considered an important indicator of the resilience of ecosystems. The decline of entomofauna in many ecosystems is currently the focus of public and scientific discourse. Biodiversity monitoring to identify insects of different functional groups in flowering areas and agroecosystems can therefore be seen as an integral part of sustainable land use and as a basis for agricultural strategies such as integrated pest management (IPM). Current solutions for the taxonomic recording of biodiversity are time-consuming, cost-intensive and require expert knowledge for many species groups. Depending on the method used, only certain groups are selectively captured, which means that only individual biodiversity factors can be assessed. Digital methods to support the determination of population densities and the diversity of species communities have enormous application potential, but have hardly been used to date. At the same time, advances in computer science and data science have led to the development of methods that allow the autonomous and self-learning taxonomic identification of species using artificial intelligence (AI) methods. However, a corresponding application in the field of entomofauna is not yet known, despite the enormous potential for identification. In this project, a digital, AI-supported solution for the monitoring of insects is being developed that works on the basis of acoustic signatures. In addition to the development and practical testing of a sensor system for collecting and processing acoustic data, this will be deployed, calibrated and evaluated in situ in practical studies. The combination with citizen science, i.e. a smartphone app for collecting acoustic measurements, will also complete the overall picture in a regional and national context and inherently raise public awareness of the topic of biodiversity.