Objective

Biodiversity is considered to be an important indicator of the resilience of ecosystems. The declining 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 species can be 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 an enormous application potential, but have hardly been used to date. Nonetheless, advances in computing and data science have led to the development of methods that enable 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 species identification.

In this project, a digital AI-supported solution for the monitoring of insects is being developed which operates on the basis of acoustic signatures. In addition to the development and practical testing of a sensor platform for collecting and processing acoustic data, the system will be deployed, calibrated, and evaluated in practical studies. Its combination with citizen science features, i.e., a smartphone app for collecting acoustic measurements, will enable holitic assessments of biodiversity in a regional and national context, and inherently raise public awareness of the issue of biodiversity.